cons77way Conservation Vertebrate Multiz Alignment & Conservation (77 Species) Comparative Genomics Description This track shows multiple alignments of 77 vertebrate species: 55 birds, 10 reptiles (alligator, snake, frog) and 12 other species (fish, human, mouse, lamprey) and measurements of evolutionary conservation using two methods (phastCons and phyloP) from the PHAST package, for all 77 species. The multiple alignments were generated using multiz and other tools in the UCSC/Penn State Bioinformatics comparative genomics alignment pipeline. Conserved elements identified by phastCons are also displayed in this track. PhastCons (which has been used in previous Conservation tracks) is a hidden Markov model-based method that estimates the probability that each nucleotide belongs to a conserved element, based on the multiple alignment. It considers not just each individual alignment column, but also its flanking columns. By contrast, phyloP separately measures conservation at individual columns, ignoring the effects of their neighbors. As a consequence, the phyloP plots have a less smooth appearance than the phastCons plots, with more "texture" at individual sites. The two methods have different strengths and weaknesses. PhastCons is sensitive to "runs" of conserved sites, and is therefore effective for picking out conserved elements. PhyloP, on the other hand, is more appropriate for evaluating signatures of selection at particular nucleotides or classes of nucleotides (e.g., third codon positions, or first positions of miRNA target sites). Another important difference is that phyloP can measure acceleration (faster evolution than expected under neutral drift) as well as conservation (slower than expected evolution). In the phyloP plots, sites predicted to be conserved are assigned positive scores (and shown in blue), while sites predicted to be fast-evolving are assigned negative scores (and shown in red). The absolute values of the scores represent -log p-values under a null hypothesis of neutral evolution. The phastCons scores, by contrast, represent probabilities of negative selection and range between 0 and 1. Both phastCons and phyloP treat alignment gaps and unaligned nucleotides as missing data. See also: lastz parameters and other details, and chain minimum score and gap parameters used in these alignments. Missing sequence in the assemblies is highlighted in the track display by regions of yellow when zoomed out and Ns displayed at base level (see Gap Annotation, below). OrganismSpeciesAssembly namebrowser orNCBI sourcealignment type ChickenGallus gallus Mar. 2018 (GRCg6a/galGal6) Mar. 2018 (GRCg6a/galGal6) reference Adelie penguinPygoscelis adeliae Jun 2014 (ASM69910v1/pygAde1) Jun 2014 (ASM69910v1/pygAde1) syntenic African clawed frogXenopus laevis Aug. 2016 (Xenopus_laevis_v2/xenLae2) Aug. 2016 (Xenopus_laevis_v2/xenLae2) net American alligatorAlligator mississippiensis Aug. 2012 (allMis0.2/allMis1) Aug. 2012 (allMis0.2/allMis1) reciprocal best American crowCorvus brachyrhynchos Jun 2014 (ASM69197v1/corBra1) Jun 2014 (ASM69197v1/corBra1) syntenic American flamingoPhoenicopterus ruber ruber May 2014 (ASM68726v1/phoRub1) May 2014 (ASM68726v1/phoRub1) reciprocal best Anna's hummingbirdCalypte anna Jun 2014 (ASM69908v1/calAnn1) Jun 2014 (ASM69908v1/calAnn1) syntenic Arctic lampreyLethenteron camtschaticum Sep 2013 (LetJap1.0/letCam1) Sep 2013 (LetJap1.0/letCam1) net Bald eagleHaliaeetus leucocephalus Aug 2014 (Haliaeetus_leucocephalus-4.0/halLeu1) Aug 2014 (Haliaeetus_leucocephalus-4.0/halLeu1) syntenic Bar tailed trogonApaloderma vittatum 16 Jun 2014 (ASM70340v1/apaVit1) 16 Jun 2014 (ASM70340v1/apaVit1) reciprocal best Barn owlTyto alba May 2014 (ASM68720v1/tytAlb1) May 2014 (ASM68720v1/tytAlb1) reciprocal best Brown roateloMesitornis unicolor Jun 2014 (ASM69576v1/mesUni1) Jun 2014 (ASM69576v1/mesUni1) reciprocal best BudgerigarMelopsittacus undulatus Sep. 2011 (WUSTL v6.3/melUnd1) Sep. 2011 (WUSTL v6.3/melUnd1) syntenic Burmese pythonPython bivittatus Sep. 2013 (Python_molurus_bivittatus-5.0.2/pytBiv1) Sep. 2013 (Python_molurus_bivittatus-5.0.2/pytBiv1) net CanarySerinus canaria 15 Jan-2014 (SCA1/serCan1) 15 Jan-2014 (SCA1/serCan1) syntenic Chinese alligatorAlligator sinensis 29 Aug 2013 (ASM45574v1/allSin1) 29 Aug 2013 (ASM45574v1/allSin1) reciprocal best Chinese softshell turtlePelodiscus sinensis Oct 2011 (PelSin_1.0/pelSin1) Oct 2011 (PelSin_1.0/pelSin1) reciprocal best Chuck-will's-widowCaprimulgus carolinensis Jun 2014 (ASM70074v1/capCar1) Jun 2014 (ASM70074v1/capCar1) reciprocal best Collared flycatcherFicedula albicollis Jun 2013 (FicAlb1.5/ficAlb2) Jun 2013 (FicAlb1.5/ficAlb2) syntenic Common cuckooCuculus canorus Jun 2014 (ASM70932v1/cucCan1) Jun 2014 (ASM70932v1/cucCan1) reciprocal best Crested ibisNipponia nippon Jun 2014 (ASM70822v1/nipNip1) Jun 2014 (ASM70822v1/nipNip1) syntenic Crowned crainBalearica pavonina gibbericeps Jun 2014 (ASM70989v1/balPav1) Jun 2014 (ASM70989v1/balPav1) reciprocal best Cuckoo rollerLeptosomus discolor May 2014 (ASM69178v1/lepDis1) May 2014 (ASM69178v1/lepDis1) reciprocal best Dalmatian pelicanPelecanus crispus May 2014 (ASM68737v1/pelCri1) May 2014 (ASM68737v1/pelCri1) reciprocal best Downy woodpeckerPicoides pubescens Jun 2014 (ASM69900v1/picPub1) Jun 2014 (ASM69900v1/picPub1) reciprocal best Emperor penguinAptenodytes forsteri 06 Jun 2014 (ASM69914v1/aptFor1) 06 Jun 2014 (ASM69914v1/aptFor1) syntenic FuguTakifugu rubripes Oct. 2011 (FUGU5/fr3) Oct. 2011 (FUGU5/fr3) net Garter snakeThamnophis sirtalis Jun. 2015 (Thamnophis_sirtalis-6.0/thaSir1) Jun. 2015 (Thamnophis_sirtalis-6.0/thaSir1) net Golden eagleAquila chrysaetos canadensis Oct. 2014 (aquChr-1.0.2/aquChr2) Oct. 2014 (aquChr-1.0.2/aquChr2) syntenic Great cormorantPhalacrocorax carbo Jun 2014 (ASM70892v1/phaCar1) Jun 2014 (ASM70892v1/phaCar1) reciprocal best Green seaturtleChelonia mydas Mar 2013 (CheMyd_1.0/cheMyd1) Mar 2013 (CheMyd_1.0/cheMyd1) reciprocal best HoatzinOpisthocomus hoazin May 2014 (ASM69207v1/opiHoa1) May 2014 (ASM69207v1/opiHoa1) reciprocal best Hooded crowCorvus cornix cornix Aug 2014 (Hooded_Crow_genome/corCor1) Aug 2014 (Hooded_Crow_genome/corCor1) syntenic Houbara bustardChlamydotis undulata macqueenii Jun 2014 (ASM69519v1/chlUnd1) Jun 2014 (ASM69519v1/chlUnd1) reciprocal best HumanHomo sapiens Dec. 2013 (GRCh38/hg38) Dec. 2013 (GRCh38/hg38) net Japanese eelAnguilla japonica 18 Mar-2014 (japanese_eel_genome_v1 25_oct_2011_japonica_c401b400k25m200 sspacepremiumk3a02n24 extra.final.scaffolds/angJap1) 18 Mar-2014 (japanese_eel_genome_v1 25_oct_2011_japonica_c401b400k25m200 sspacepremiumk3a02n24 extra.final.scaffolds/angJap1) net Japanese quailCoturnix japonica Mar. 2016 (Coturnix japonica 2.0/cotJap2) Mar. 2016 (Coturnix japonica 2.0/cotJap2) reciprocal best KilldeerCharadrius vociferus Aug 2014 (ASM70802v2/chaVoc2) Aug 2014 (ASM70802v2/chaVoc2) syntenic LampreyPetromyzon marinus Dec. 2017 (Pmar_germline 1.0/petMar3) Dec. 2017 (Pmar_germline 1.0/petMar3) net Little egretEgretta garzetta May 2014 (ASM68718v1/egrGar1) May 2014 (ASM68718v1/egrGar1) reciprocal best LizardAnolis carolinensis May 2010 (Broad AnoCar2.0/anoCar2) May 2010 (Broad AnoCar2.0/anoCar2) net Mallard duckAnas platyrhynchos Apr 2013 (BGI_duck_1.0/anaPla1) Apr 2013 (BGI_duck_1.0/anaPla1) reciprocal best MedakaOryzias latipes Oct. 2005 (NIG/UT MEDAKA1/oryLat2) Oct. 2005 (NIG/UT MEDAKA1/oryLat2) net Medium ground finchGeospiza fortis Apr. 2012 (GeoFor_1.0/geoFor1) Apr. 2012 (GeoFor_1.0/geoFor1) syntenic MouseMus musculus Dec. 2011 (GRCm38/mm10) Dec. 2011 (GRCm38/mm10) net Nile tilapiaOreochromis niloticus Nov. 2016 (ASM185804v2/oreNil3) Nov. 2016 (ASM185804v2/oreNil3) net Northern carmine bee-eaterMerops nubicus May 2014 (ASM69184v1/merNub1) May 2014 (ASM69184v1/merNub1) reciprocal best Northern fulmarFulmarus glacialis May 2014 (ASM69083v1/fulGla1) May 2014 (ASM69083v1/fulGla1) reciprocal best OstrichStruthio camelus australis 06 Jun-2014 (ASM69896v1/strCam1) 06 Jun-2014 (ASM69896v1/strCam1) reciprocal best Painted turtleChrysemys picta bellii Mar. 2014 (v3.0.3/chrPic2) Mar. 2014 (v3.0.3/chrPic2) syntenic ParrotAmazona vittata Jan. 2013 (AV1/amaVit1) Jan. 2013 (AV1/amaVit1) reciprocal best Peregrine falconFalco peregrinus Feb 2013 (F_peregrinus_v1.0/falPer1) Feb 2013 (F_peregrinus_v1.0/falPer1) syntenic Red crested turacoTauraco erythrolophus Jun 2014 (ASM70936v1/tauEry1) Jun 2014 (ASM70936v1/tauEry1) reciprocal best Red-legged seriemaCariama cristata May 2014 (ASM69053v1/carCri1) May 2014 (ASM69053v1/carCri1) reciprocal best Red-throated loonGavia stellata May 2014 (ASM69087v1/gavSte1) May 2014 (ASM69087v1/gavSte1) reciprocal best Rhinoceros hornbillBuceros rhinoceros silvestris Jun 2014 (ASM71030v1/bucRhi1) Jun 2014 (ASM71030v1/bucRhi1) reciprocal best RiflemanAcanthisitta chloris 27 May 2014 (ASM69581v1/acaChl1) 27 May 2014 (ASM69581v1/acaChl1) reciprocal best Rock pigeonColumba livia Feb 2013 (Cliv_1.0/colLiv1) Feb 2013 (Cliv_1.0/colLiv1) syntenic Saker falconFalco cherrug Feb 2013 (F_cherrug_v1.0/falChe1) Feb 2013 (F_cherrug_v1.0/falChe1) syntenic Scarlet macawAra macao Jun 2013 (SMACv1.1/araMac1) Jun 2013 (SMACv1.1/araMac1) reciprocal best Speckled mousebirdColius striatus May 2014 (ASM69071v1/colStr1) May 2014 (ASM69071v1/colStr1) reciprocal best Spiny softshell turtleApalone spinifera May 2013 (ASM38561v1/apaSpi1) May 2013 (ASM38561v1/apaSpi1) reciprocal best SticklebackGasterosteus aculeatus Feb. 2006 (Broad/gasAcu1) Feb. 2006 (Broad/gasAcu1) net SunbitternEurypyga helias May 2014 (ASM69077v1/eurHel1) May 2014 (ASM69077v1/eurHel1) reciprocal best TetraodonTetraodon nigroviridis Mar. 2007 (Genoscope 8.0/tetNig2) Mar. 2007 (Genoscope 8.0/tetNig2) net Tibetan frogNanorana parkeri Mar. 2015 (BGI_ZX_2015/nanPar1) Mar. 2015 (BGI_ZX_2015/nanPar1) net Tibetan ground jayPseudopodoces humilis Jan 2013 (PseHum1.0/pseHum1) Jan 2013 (PseHum1.0/pseHum1) syntenic TurkeyMeleagris gallopavo Nov. 2014 (Turkey_5.0/melGal5) Nov. 2014 (Turkey_5.0/melGal5) reciprocal best White throated tinamouTinamus guttatus Aug 2014 (ASM70537v2/tinGut2) Aug 2014 (ASM70537v2/tinGut2) reciprocal best White-tailed eagleHaliaeetus albicilla May 2014 (ASM69140v1/halAlb1) May 2014 (ASM69140v1/halAlb1) reciprocal best White-tailed tropicbirdPhaethon lepturus May 2014 (ASM68728v1/phaLep1) May 2014 (ASM68728v1/phaLep1) reciprocal best White-throated sparrowZonotrichia albicollis Apr 2013 (ASM38545v1/zonAlb1) Apr 2013 (ASM38545v1/zonAlb1) syntenic X. tropicalisXenopus tropicalis Jul. 2016 (Xenopus_tropicalis_v9.1/xenTro9) Jul. 2016 (Xenopus_tropicalis_v9.1/xenTro9) net Yellow-throated sandgrousePterocles gutturalis Jun 2014 (ASM69924v1/pteGut1) Jun 2014 (ASM69924v1/pteGut1) reciprocal best Zebra finchTaeniopygia guttata Feb. 2013 (WashU taeGut324/taeGut2) Feb. 2013 (WashU taeGut324/taeGut2) syntenic Zebra mbunaMaylandia zebra Mar 2012 (MetZeb1.1/mayZeb1) Mar 2012 (MetZeb1.1/mayZeb1) net ZebrafishDanio rerio May 2017 (GRCz11/danRer11) May 2017 (GRCz11/danRer11) net Table 1. Genome assemblies included in the 77-way Conservation track. Downloads for data in this track are available: Multiz alignments (MAF format), and phylogenetic trees PhyloP conservation (WIG format) PhastCons conservation (WIG format) Display Conventions and Configuration The track configuration options allow the user to display the three different sets of alignments: all, birds, reptiles or vertebrate, individually or all simultaneously. In full and pack display modes, conservation scores are displayed as a wiggle track (histogram) in which the height reflects the value of the score. The conservation wiggles can be configured in a variety of ways to highlight different aspects of the displayed information. Click the Graph configuration help link for an explanation of the configuration options. Pairwise alignments of each species to the chicken genome are displayed below the conservation histogram as a grayscale density plot (in pack mode) or as a wiggle (in full mode) that indicates alignment quality. In dense display mode, conservation is shown in grayscale using darker values to indicate higher levels of overall conservation as scored by phastCons. Checkboxes on the track configuration page allow selection of the species to include in the pairwise display. Configuration buttons are available to select all of the species (Set all), deselect all of the species (Clear all), or use the default settings (Set defaults). Note that excluding species from the pairwise display does not alter the conservation score display. To view detailed information about the alignments at a specific position, zoom the display in to 30,000 or fewer bases, then click on the alignment. Gap Annotation The Display chains between alignments configuration option enables display of gaps between alignment blocks in the pairwise alignments in a manner similar to the Chain track display. The following conventions are used: Single line: No bases in the aligned species. Possibly due to a lineage-specific insertion between the aligned blocks in the chicken genome or a lineage-specific deletion between the aligned blocks in the aligning species. Double line: Aligning species has one or more unalignable bases in the gap region. Possibly due to excessive evolutionary distance between species or independent indels in the region between the aligned blocks in both species. Pale yellow coloring: Aligning species has Ns in the gap region. Reflects uncertainty in the relationship between the DNA of both species, due to lack of sequence in relevant portions of the aligning species. Genomic Breaks Discontinuities in the genomic context (chromosome, scaffold or region) of the aligned DNA in the aligning species are shown as follows: Vertical blue bar: Represents a discontinuity that persists indefinitely on either side, e.g. a large region of DNA on either side of the bar comes from a different chromosome in the aligned species due to a large scale rearrangement. Green square brackets: Enclose shorter alignments consisting of DNA from one genomic context in the aligned species nested inside a larger chain of alignments from a different genomic context. The alignment within the brackets may represent a short misalignment, a lineage-specific insertion of a transposon in the chicken genome that aligns to a paralogous copy somewhere else in the aligned species, or other similar occurrence. Base Level When zoomed-in to the base-level display, the track shows the base composition of each alignment. The numbers and symbols on the Gaps line indicate the lengths of gaps in the chicken sequence at those alignment positions relative to the longest non-chicken sequence. If there is sufficient space in the display, the size of the gap is shown. If the space is insufficient and the gap size is a multiple of 3, a "*" is displayed; other gap sizes are indicated by "+". Codon translation is available in base-level display mode if the displayed region is identified as a coding segment. To display this annotation, select the species for translation from the pull-down menu in the Codon Translation configuration section at the top of the page. Then, select one of the following modes: No codon translation: The gene annotation is not used; the bases are displayed without translation. Use default species reading frames for translation: The annotations from the genome displayed in the Default species to establish reading frame pull-down menu are used to translate all the aligned species present in the alignment. Use reading frames for species if available, otherwise no translation: Codon translation is performed only for those species where the region is annotated as protein coding. Use reading frames for species if available, otherwise use default species: Codon translation is done on those species that are annotated as being protein coding over the aligned region using species-specific annotation; the remaining species are translated using the default species annotation. Codon translation uses the following gene tracks as the basis for translation, depending on the species chosen (Table 2). Gene TrackSpecies NCBI RefSeq GenesChicken, Japanese quail, Turkey, Medium ground finch, Zebra finch, Budgerigar, American alligator Ensembl Genes v92Mallard duck, Chinese softshell turtle Xeno RefGeneGolden eagle, Painted turtle no annotationsall others Table 2. Gene tracks used for codon translation. Methods Pairwise alignments with the chicken genome were generated for each species using lastz from repeat-masked genomic sequence. Pairwise alignments were then linked into chains using a dynamic programming algorithm that finds maximally scoring chains of gapless subsections of the alignments organized in a kd-tree. Please note the specific parametersfor the alignments. High-scoring chains were then placed along the genome, with gaps filled by lower-scoring chains, to produce an alignment net. For more information about the chaining and netting process for each species, see the description pages for the Chain and Net tracks. An additional filtering step was introduced in the generation of the 77-way conservation track to reduce the number of paralogs and pseudogenes from the high-quality assemblies and the suspect alignments from the low-quality assemblies: some of the pairwise alignments were filtered based on synteny; and others were filtered to retain only alignments of best quality in both the target and query ("reciprocal best"). Note the indication in the table above for this filtering option. The resulting best-in-genome pairwise alignments were progressively aligned using multiz/autoMZ, following the tree topology diagrammed above, to produce multiple alignments. The multiple alignments were post-processed to add annotations indicating alignment gaps, genomic breaks, and base quality of the component sequences. The annotated multiple alignments, in MAF format, are available for bulk download. An alignment summary table containing an entry for each alignment block in each species was generated to improve track display performance at large scales. Framing tables were constructed to enable visualization of codons in the multiple alignment display. Phylogenetic Tree Model Both phastCons and phyloP are phylogenetic methods that rely on a tree model containing the tree topology, branch lengths representing evolutionary distance at neutrally evolving sites, the background distribution of nucleotides, and a substitution rate matrix. The all species tree model for this track was generated using the phyloFit program from the PHAST package (REV model, EM algorithm, medium precision) using multiple alignments of 4-fold degenerate sites extracted from the 77-way alignment (msa_view). The 4d sites were derived from the NCBI RefSeq gene set, filtered to select single-coverage long transcripts. This same tree model was used in the phyloP calculations, however their background frequencies were modified to maintain reversibility. The resulting tree model for all species. PhastCons Conservation The phastCons program computes conservation scores based on a phylo-HMM, a type of probabilistic model that describes both the process of DNA substitution at each site in a genome and the way this process changes from one site to the next (Felsenstein and Churchill 1996, Yang 1995, Siepel and Haussler 2005). PhastCons uses a two-state phylo-HMM, with a state for conserved regions and a state for non-conserved regions. The value plotted at each site is the posterior probability that the corresponding alignment column was "generated" by the conserved state of the phylo-HMM. These scores reflect the phylogeny (including branch lengths) of the species in question, a continuous-time Markov model of the nucleotide substitution process, and a tendency for conservation levels to be autocorrelated along the genome (i.e., to be similar at adjacent sites). The general reversible (REV) substitution model was used. Unlike many conservation-scoring programs, phastCons does not rely on a sliding window of fixed size; therefore, short highly-conserved regions and long moderately conserved regions can both obtain high scores. More information about phastCons can be found in Siepel et al. 2005. The phastCons parameters used were: expected-length=45, target-coverage=0.3, rho=0.3. PhyloP Conservation The phyloP program supports several different methods for computing p-values of conservation or acceleration, for individual nucleotides or larger elements ( http://compgen.cshl.edu/phast/). Here it was used to produce separate scores at each base (--wig-scores option), considering all branches of the phylogeny rather than a particular subtree or lineage (i.e., the --subtree option was not used). The scores were computed by performing a likelihood ratio test at each alignment column (--method LRT), and scores for both conservation and acceleration were produced (--mode CONACC). Conserved Elements The conserved elements were predicted by running phastCons with the --viterbi option. The predicted elements are segments of the alignment that are likely to have been "generated" by the conserved state of the phylo-HMM. Each element is assigned a log-odds score equal to its log probability under the conserved model minus its log probability under the non-conserved model. The "score" field associated with this track contains transformed log-odds scores, taking values between 0 and 1000. (The scores are transformed using a monotonic function of the form a * log(x) + b.) The raw log odds scores are retained in the "name" field and can be seen on the details page or in the browser when the track's display mode is set to "pack" or "full". Credits This track was created using the following programs: Alignment tools: lastz (formerly blastz) and multiz by Minmei Hou, Scott Schwartz and Webb Miller of the Penn State Bioinformatics Group Chaining and Netting: axtChain, chainNet by Jim Kent at UCSC Conservation scoring: phastCons, phyloP, phyloFit, tree_doctor, msa_view and other programs in PHAST by Adam Siepel at Cold Spring Harbor Laboratory (original development done at the Haussler lab at UCSC). MAF Annotation tools: mafAddIRows by Brian Raney, UCSC; mafAddQRows by Richard Burhans, Penn State; genePredToMafFrames by Mark Diekhans, UCSC Tree image generator: phyloPng by Galt Barber, UCSC Conservation track display: Kate Rosenbloom, Hiram Clawson (wiggle display), and Brian Raney (gap annotation and codon framing) at UCSC The phylogenetic tree is based on Murphy et al. (2001) and general consensus in the vertebrate phylogeny community as of March 2007. References Phylo-HMMs, phastCons, and phyloP: Felsenstein J, Churchill GA. A Hidden Markov Model approach to variation among sites in rate of evolution. Mol Biol Evol. 1996 Jan;13(1):93-104. PMID: 8583911 Pollard KS, Hubisz MJ, Rosenbloom KR, Siepel A. Detection of nonneutral substitution rates on mammalian phylogenies. Genome Res. 2010 Jan;20(1):110-21. PMID: 19858363; PMC: PMC2798823 Siepel A, Bejerano G, Pedersen JS, Hinrichs AS, Hou M, Rosenbloom K, Clawson H, Spieth J, Hillier LW, Richards S, et al. Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes. Genome Res. 2005 Aug;15(8):1034-50. PMID: 16024819; PMC: PMC1182216 Siepel A, Haussler D. Phylogenetic Hidden Markov Models. In: Nielsen R, editor. Statistical Methods in Molecular Evolution. New York: Springer; 2005. pp. 325-351. Yang Z. A space-time process model for the evolution of DNA sequences. Genetics. 1995 Feb;139(2):993-1005. PMID: 7713447; PMC: PMC1206396 Chain/Net: Kent WJ, Baertsch R, Hinrichs A, Miller W, Haussler D. Evolution's cauldron: duplication, deletion, and rearrangement in the mouse and human genomes. Proc Natl Acad Sci U S A. 2003 Sep 30;100(20):11484-9. PMID: 14500911; PMC: PMC208784 Multiz: Blanchette M, Kent WJ, Riemer C, Elnitski L, Smit AF, Roskin KM, Baertsch R, Rosenbloom K, Clawson H, Green ED, et al. Aligning multiple genomic sequences with the threaded blockset aligner. Genome Res. 2004 Apr;14(4):708-15. PMID: 15060014; PMC: PMC383317 Lastz (formerly Blastz): Chiaromonte F, Yap VB, Miller W. Scoring pairwise genomic sequence alignments. Pac Symp Biocomput. 2002:115-26. PMID: 11928468 Harris RS. Improved pairwise alignment of genomic DNA. Ph.D. Thesis. Pennsylvania State University, USA. 2007. Schwartz S, Kent WJ, Smit A, Zhang Z, Baertsch R, Hardison RC, Haussler D, Miller W. Human-mouse alignments with BLASTZ. Genome Res. 2003 Jan;13(1):103-7. PMID: 12529312; PMC: PMC430961 Phylogenetic Tree: Murphy WJ, Eizirik E, O'Brien SJ, Madsen O, Scally M, Douady CJ, Teeling E, Ryder OA, Stanhope MJ, de Jong WW, Springer MS. Resolution of the early placental mammal radiation using Bayesian phylogenetics. Science. 2001 Dec 14;294(5550):2348-51. PMID: 11743200 
cons77wayViewalign Multiz Alignments Vertebrate Multiz Alignment & Conservation (77 Species) Comparative Genomics 
multiz77way Multiz Align Multiz Alignments of 77 Vertebrates Comparative Genomics 
cons77wayViewphastcons Element Conservation (phastCons) Vertebrate Multiz Alignment & Conservation (77 Species) Comparative Genomics 
phastCons77way Cons 77 Verts 77 vertebrates conservation by PhastCons Comparative Genomics 
cons77wayViewelements Conserved Elements Vertebrate Multiz Alignment & Conservation (77 Species) Comparative Genomics 
phastConsElements77way 77 Vert. El 77 vertebrates Conserved Elements Comparative Genomics 
cons77wayViewphyloP Basewise Conservation (phyloP) Vertebrate Multiz Alignment & Conservation (77 Species) Comparative Genomics 
phyloP77way Cons 77 Verts 77 vertebrates Basewise Conservation by PhyloP Comparative Genomics 
cpgIslandExt CpG Islands CpG Islands (Islands < 300 Bases are Light Green) Expression and Regulation Description CpG islands are associated with genes, particularly housekeeping genes, in vertebrates. CpG islands are typically common near transcription start sites and may be associated with promoter regions. Normally a C (cytosine) base followed immediately by a G (guanine) base (a CpG) is rare in vertebrate DNA because the Cs in such an arrangement tend to be methylated. This methylation helps distinguish the newly synthesized DNA strand from the parent strand, which aids in the final stages of DNA proofreading after duplication. However, over evolutionary time, methylated Cs tend to turn into Ts because of spontaneous deamination. The result is that CpGs are relatively rare unless there is selective pressure to keep them or a region is not methylated for some other reason, perhaps having to do with the regulation of gene expression. CpG islands are regions where CpGs are present at significantly higher levels than is typical for the genome as a whole. The unmasked version of the track displays potential CpG islands that exist in repeat regions and would otherwise not be visible in the repeat masked version. By default, only the masked version of the track is displayed. To view the unmasked version, change the visibility settings in the track controls at the top of this page. Methods CpG islands were predicted by searching the sequence one base at a time, scoring each dinucleotide (+17 for CG and -1 for others) and identifying maximally scoring segments. Each segment was then evaluated for the following criteria: GC content of 50% or greater length greater than 200 bp ratio greater than 0.6 of observed number of CG dinucleotides to the expected number on the basis of the number of Gs and Cs in the segment The entire genome sequence, masking areas included, was used for the construction of the track Unmasked CpG. The track CpG Islands is constructed on the sequence after all masked sequence is removed. The CpG count is the number of CG dinucleotides in the island. The Percentage CpG is the ratio of CpG nucleotide bases (twice the CpG count) to the length. The ratio of observed to expected CpG is calculated according to the formula (cited in Gardiner-Garden et al. (1987)): Obs/Exp CpG = Number of CpG * N / (Number of C * Number of G) where N = length of sequence. The calculation of the track data is performed by the following command sequence: twoBitToFa assembly.2bit stdout | maskOutFa stdin hard stdout \ | cpg_lh /dev/stdin 2&gt; cpg_lh.err \ | awk '{&dollar;2 = &dollar;2 - 1; width = &dollar;3 - &dollar;2; printf("%s\t%d\t%s\t%s %s\t%s\t%s\t%0.0f\t%0.1f\t%s\t%s\n", &dollar;1, &dollar;2, &dollar;3, &dollar;5, &dollar;6, width, &dollar;6, width*&dollar;7*0.01, 100.0*2*&dollar;6/width, &dollar;7, &dollar;9);}' \ | sort -k1,1 -k2,2n &gt; cpgIsland.bed The unmasked track data is constructed from twoBitToFa -noMask output for the twoBitToFa command. Data access CpG islands and its associated tables can be explored interactively using the REST API, the Table Browser or the Data Integrator. All the tables can also be queried directly from our public MySQL servers, with more information available on our help page as well as on our blog. The source for the cpg_lh program can be obtained from src/utils/cpgIslandExt/. The cpg_lh program binary can be obtained from: http://hgdownload.soe.ucsc.edu/admin/exe/linux.x86_64/cpg_lh (choose "save file") Credits This track was generated using a modification of a program developed by G. Miklem and L. Hillier (unpublished). References Gardiner-Garden M, Frommer M. CpG islands in vertebrate genomes. J Mol Biol. 1987 Jul 20;196(2):261-82. PMID: 3656447 
cpgIslandSuper CpG Islands CpG Islands (Islands < 300 Bases are Light Green) Expression and Regulation Description CpG islands are associated with genes, particularly housekeeping genes, in vertebrates. CpG islands are typically common near transcription start sites and may be associated with promoter regions. Normally a C (cytosine) base followed immediately by a G (guanine) base (a CpG) is rare in vertebrate DNA because the Cs in such an arrangement tend to be methylated. This methylation helps distinguish the newly synthesized DNA strand from the parent strand, which aids in the final stages of DNA proofreading after duplication. However, over evolutionary time, methylated Cs tend to turn into Ts because of spontaneous deamination. The result is that CpGs are relatively rare unless there is selective pressure to keep them or a region is not methylated for some other reason, perhaps having to do with the regulation of gene expression. CpG islands are regions where CpGs are present at significantly higher levels than is typical for the genome as a whole. The unmasked version of the track displays potential CpG islands that exist in repeat regions and would otherwise not be visible in the repeat masked version. By default, only the masked version of the track is displayed. To view the unmasked version, change the visibility settings in the track controls at the top of this page. Methods CpG islands were predicted by searching the sequence one base at a time, scoring each dinucleotide (+17 for CG and -1 for others) and identifying maximally scoring segments. Each segment was then evaluated for the following criteria: GC content of 50% or greater length greater than 200 bp ratio greater than 0.6 of observed number of CG dinucleotides to the expected number on the basis of the number of Gs and Cs in the segment The entire genome sequence, masking areas included, was used for the construction of the track Unmasked CpG. The track CpG Islands is constructed on the sequence after all masked sequence is removed. The CpG count is the number of CG dinucleotides in the island. The Percentage CpG is the ratio of CpG nucleotide bases (twice the CpG count) to the length. The ratio of observed to expected CpG is calculated according to the formula (cited in Gardiner-Garden et al. (1987)): Obs/Exp CpG = Number of CpG * N / (Number of C * Number of G) where N = length of sequence. The calculation of the track data is performed by the following command sequence: twoBitToFa assembly.2bit stdout | maskOutFa stdin hard stdout \ | cpg_lh /dev/stdin 2&gt; cpg_lh.err \ | awk '{&dollar;2 = &dollar;2 - 1; width = &dollar;3 - &dollar;2; printf("%s\t%d\t%s\t%s %s\t%s\t%s\t%0.0f\t%0.1f\t%s\t%s\n", &dollar;1, &dollar;2, &dollar;3, &dollar;5, &dollar;6, width, &dollar;6, width*&dollar;7*0.01, 100.0*2*&dollar;6/width, &dollar;7, &dollar;9);}' \ | sort -k1,1 -k2,2n &gt; cpgIsland.bed The unmasked track data is constructed from twoBitToFa -noMask output for the twoBitToFa command. Data access CpG islands and its associated tables can be explored interactively using the REST API, the Table Browser or the Data Integrator. All the tables can also be queried directly from our public MySQL servers, with more information available on our help page as well as on our blog. The source for the cpg_lh program can be obtained from src/utils/cpgIslandExt/. The cpg_lh program binary can be obtained from: http://hgdownload.soe.ucsc.edu/admin/exe/linux.x86_64/cpg_lh (choose "save file") Credits This track was generated using a modification of a program developed by G. Miklem and L. Hillier (unpublished). References Gardiner-Garden M, Frommer M. CpG islands in vertebrate genomes. J Mol Biol. 1987 Jul 20;196(2):261-82. PMID: 3656447 
rmsk RepeatMasker Repeating Elements by RepeatMasker Variation and Repeats Description This track was created by using Arian Smit's RepeatMasker program, which screens DNA sequences for interspersed repeats and low complexity DNA sequences. The program outputs a detailed annotation of the repeats that are present in the query sequence (represented by this track), as well as a modified version of the query sequence in which all the annotated repeats have been masked (generally available on the Downloads page). RepeatMasker uses the Repbase Update library of repeats from the Genetic Information Research Institute (GIRI). Repbase Update is described in Jurka (2000) in the References section below. Some newer assemblies have been made with Dfam, not Repbase. You can find the details for how we make our database data here in our &quot;makeDb/doc/&quot; directory. When analyzing the data tables of this track, keep in mind that Repbase is not the same as the Repeatmasker sequence database and that the repeat names in the Repeatmasker output are not the same as the sequence names in the Repeatmasker database. Concretely, you can find a name such as "L1PA4" in the Repeatmasker output and this track, but there is not necessarily a single sequence "L1PA4" in the Repeatmasker database. This is because Repeatmasker creates annotations by joining matches to partial pieces of the database together so there is no 1:1 relationship between its sequence database and the annotations. To learn more, you can read the Repeatmasker paper, its source code or reach out to the Repeatmasker authors, your local expert on transposable elements or us. Display Conventions and Configuration In full display mode, this track displays up to ten different classes of repeats: Short interspersed nuclear elements (SINE), which include ALUs Long interspersed nuclear elements (LINE) Long terminal repeat elements (LTR), which include retroposons DNA repeat elements (DNA) Simple repeats (micro-satellites) Low complexity repeats Satellite repeats RNA repeats (including RNA, tRNA, rRNA, snRNA, scRNA, srpRNA) Other repeats, which includes class RC (Rolling Circle) Unknown The level of color shading in the graphical display reflects the amount of base mismatch, base deletion, and base insertion associated with a repeat element. The higher the combined number of these, the lighter the shading. A &quot;?&quot; at the end of the &quot;Family&quot; or &quot;Class&quot; (for example, DNA?) signifies that the curator was unsure of the classification. At some point in the future, either the &quot;?&quot; will be removed or the classification will be changed. Methods Data are generated using the RepeatMasker -s flag. Additional flags may be used for certain organisms. Repeats are soft-masked. Alignments may extend through repeats, but are not permitted to initiate in them. See the FAQ for more information. Credits Thanks to Arian Smit, Robert Hubley and GIRI for providing the tools and repeat libraries used to generate this track. References Smit AFA, Hubley R, Green P. RepeatMasker Open-3.0. http://www.repeatmasker.org. 1996-2010. Repbase Update is described in: Jurka J. Repbase Update: a database and an electronic journal of repetitive elements. Trends Genet. 2000 Sep;16(9):418-420. PMID: 10973072 For a discussion of repeats in mammalian genomes, see: Smit AF. Interspersed repeats and other mementos of transposable elements in mammalian genomes. Curr Opin Genet Dev. 1999 Dec;9(6):657-63. PMID: 10607616 Smit AF. The origin of interspersed repeats in the human genome. Curr Opin Genet Dev. 1996 Dec;6(6):743-8. PMID: 8994846 
refSeqComposite NCBI RefSeq RefSeq gene predictions from NCBI Genes and Gene Predictions Description The NCBI RefSeq Genes composite track shows chicken protein-coding and non-protein-coding genes taken from the NCBI RNA reference sequences collection (RefSeq). All subtracks use coordinates provided by RefSeq, except for the UCSC RefSeq track, which UCSC produces by realigning the RefSeq RNAs to the genome. This realignment may result in occasional differences between the annotation coordinates provided by UCSC and NCBI. For RNA-seq analysis, we advise using NCBI aligned tables like RefSeq All or RefSeq Curated. See the Methods section for more details about how the different tracks were created. Please visit NCBI's Feedback for Gene and Reference Sequences (RefSeq) page to make suggestions, submit additions and corrections, or ask for help concerning RefSeq records. For more information on the different gene tracks, see our Genes FAQ. Display Conventions and Configuration This track is a composite track that contains differing data sets. To show only a selected set of subtracks, uncheck the boxes next to the tracks that you wish to hide. Note: Not all subtracts are available on all assemblies. The possible subtracks include: RefSeq aligned annotations and UCSC alignment of RefSeq annotations RefSeq All &ndash; all curated and predicted annotations provided by RefSeq. RefSeq Curated &ndash; subset of RefSeq All that includes only those annotations whose accessions begin with NM, NR, NP or YP. (NP and YP are used only for protein-coding genes on the mitochondrion; YP is used for human only.) They were manually curated, based on publications describing transcripts and manual reviews of evidence which includes EST and full-length cDNA alignments, protein sequences, splice sites and any other evidence available in databases or the scientific literature. The resulting sequences can differ from the genome, they exist independently from a particular human genome build, and so must be aligned to the genome to create a track. The "RefSeq Curated" track is NCBI's mapping of these transcripts to the genome. Another alignment track exists for these, the "UCSC RefSeq" track (see beloow). RefSeq Predicted &ndash; subset of RefSeq All that includes those annotations whose accessions begin with XM or XR. They were predicted based on protein, cDNA, EST and RNA-seq alignments to the genome assembly by the NCBI Gnomon prediction software. RefSeq Other &ndash; all other annotations produced by the RefSeq group that do not fit the requirements for inclusion in the RefSeq Curated or the RefSeq Predicted tracks. Examples are untranscribed pseudogenes or gene clusters, such as HOX or protocadherin alpha. They were manually curated from publications or databases but are not typical transcribed genes. RefSeq Alignments &ndash; alignments of RefSeq RNAs to the chicken genome provided by the RefSeq group, following the display conventions for PSL tracks. RefSeq Diffs &ndash; alignment differences between the chicken reference genome(s) and RefSeq curated transcripts. (Track not currently available for every assembly.) UCSC RefSeq &ndash; annotations generated from UCSC's realignment of RNAs with NM and NR accessions to the chicken genome. This track was previously known as the &quot;RefSeq Genes&quot; track. RefSeq Select (subset, only on hg38) &ndash; Subset of RefSeq Curated, transcripts marked as part of the RefSeq Select dataset. A single Select transcript is chosen as representative for each protein-coding gene. See NCBI RefSeq Select. RefSeq HGMD (subset) &ndash; Subset of RefSeq Curated, transcripts annotated by the Human Gene Mutation Database. This track is only available on the human genomes hg19 and hg38. It is the most restricted RefSeq subset, targeting clinical diagnostics. The RefSeq All, RefSeq Curated, RefSeq Predicted, and UCSC RefSeq tracks follow the display conventions for gene prediction tracks. The color shading indicates the level of review the RefSeq record has undergone: predicted (light), provisional (medium), or reviewed (dark), as defined by RefSeq. Color Level of review Reviewed: the RefSeq record has been reviewed by NCBI staff or by a collaborator. The NCBI review process includes assessing available sequence data and the literature. Some RefSeq records may incorporate expanded sequence and annotation information. Provisional: the RefSeq record has not yet been subject to individual review. The initial sequence-to-gene association has been established by outside collaborators or NCBI staff. Predicted: the RefSeq record has not yet been subject to individual review, and some aspect of the RefSeq record is predicted. The item labels and codon display properties for features within this track can be configured through the check-box controls at the top of the track description page. To adjust the settings for an individual subtrack, click the wrench icon next to the track name in the subtrack list . Label: By default, items are labeled by gene name. Click the appropriate Label option to display the accession name or OMIM identifier instead of the gene name, show all or a subset of these labels including the gene name, OMIM identifier and accession names, or turn off the label completely. Codon coloring: This track has an optional codon coloring feature that allows users to quickly validate and compare gene predictions. To display codon colors, select the genomic codons option from the Color track by codons pull-down menu. For more information about this feature, go to the Coloring Gene Predictions and Annotations by Codon page. The RefSeq Diffs track contains five different types of inconsistency between the reference genome sequence and the RefSeq transcript sequences. The five types of differences are as follows: mismatch &ndash; aligned but mismatching bases, plus HGVS g. to show the genomic change required to match the transcript and HGVS c./n. to show the transcript change required to match the genome. short gap &ndash; genomic gaps that are too small to be introns (arbitrary cutoff of &lt; 45 bp), most likely insertions/deletion variants or errors, with HGVS g. and c./n. showing differences. shift gap &ndash; shortGap items whose placement could be shifted left and/or right on the genome due to repetitive sequence, with HGVS c./n. position range of ambiguous region in transcript. Here, thin and thick lines are used -- the thin line shows the span of the repetitive sequence, and the thick line shows the rightmost shifted gap. double gap &ndash; genomic gaps that are long enough to be introns but that skip over transcript sequence (invisible in default setting), with HGVS c./n. deletion. skipped &ndash; sequence at the beginning or end of a transcript that is not aligned to the genome (invisible in default setting), with HGVS c./n. deletion HGVS Terminology (Human Genome Variation Society): g. = genomic sequence ; c. = coding DNA sequence ; n. = non-coding RNA reference sequence. When reporting HGVS with RefSeq sequences, to make sure that results from research articles can be mapped to the genome unambiguously, please specify the RefSeq annotation release displayed on the transcript's Genome Browser details page and also the RefSeq transcript ID with version (e.g. NM_012309.4 not NM_012309). Methods Tracks contained in the RefSeq annotation and RefSeq RNA alignment tracks were created at UCSC using data from the NCBI RefSeq project. Data files were downloaded from RefSeq in GFF file format and converted to the genePred and PSL table formats for display in the Genome Browser. Information about the NCBI annotation pipeline can be found here. The RefSeq Diffs track is generated by UCSC using NCBI's RefSeq RNA alignments. The UCSC RefSeq Genes track is constructed using the same methods as previous RefSeq Genes tracks. RefSeq RNAs were aligned against the chicken genome using BLAT. Those with an alignment of less than 15% were discarded. When a single RNA aligned in multiple places, the alignment having the highest base identity was identified. Only alignments having a base identity level within 0.1% of the best and at least 96% base identity with the genomic sequence were kept. Data Access The raw data for these tracks can be accessed in multiple ways. It can be explored interactively using the REST API, Table Browser or Data Integrator. The tables can also be accessed programmatically through our public MySQL server or downloaded from our downloads server for local processing. The previous track versions are available in the archives of our downloads server. You can also access any RefSeq table entries in JSON format through our JSON API. The data in the RefSeq Other and RefSeq Diffs tracks are organized in bigBed file format; more information about accessing the information in this bigBed file can be found below. The other subtracks are associated with database tables as follows: genePred format: RefSeq All - ncbiRefSeq RefSeq Curated - ncbiRefSeqCurated RefSeq Predicted - ncbiRefSeqPredicted UCSC RefSeq - refGene PSL format: RefSeq Alignments - ncbiRefSeqPsl The first column of each of these tables is &quot;bin&quot;. This column is designed to speed up access for display in the Genome Browser, but can be safely ignored in downstream analysis. You can read more about the bin indexing system here. The annotations in the RefSeqOther and RefSeqDiffs tracks are stored in bigBed files, which can be obtained from our downloads server here, ncbiRefSeqOther.bb and ncbiRefSeqDiffs.bb. Individual regions or the whole set of genome-wide annotations can be obtained using our tool bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system from the utilities directory linked below. For example, to extract only annotations in a given region, you could use the following command: bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/galGal6/ncbiRefSeq/ncbiRefSeqOther.bb -chrom=chr16 -start=34990190 -end=36727467 stdout You can download a GTF format version of the RefSeq All table from the GTF downloads directory. The genePred format tracks can also be converted to GTF format using the genePredToGtf utility, available from the utilities directory on the UCSC downloads server. The utility can be run from the command line like so: genePredToGtf galGal6 ncbiRefSeqPredicted ncbiRefSeqPredicted.gtf Note that using genePredToGtf in this manner accesses our public MySQL server, and you therefore must set up your hg.conf as described on the MySQL page linked near the beginning of the Data Access section. A file containing the RNA sequences in FASTA format for all items in the RefSeq All, RefSeq Curated, and RefSeq Predicted tracks can be found on our downloads server here. Please refer to our mailing list archives for questions. Previous versions of the ncbiRefSeq set of tracks can be found on our archive download server. Credits This track was produced at UCSC from data generated by scientists worldwide and curated by the NCBI RefSeq project. References Kent WJ. BLAT - the BLAST-like alignment tool. Genome Res. 2002 Apr;12(4):656-64. PMID: 11932250; PMC: PMC187518 Pruitt KD, Brown GR, Hiatt SM, Thibaud-Nissen F, Astashyn A, Ermolaeva O, Farrell CM, Hart J, Landrum MJ, McGarvey KM et al. RefSeq: an update on mammalian reference sequences. Nucleic Acids Res. 2014 Jan;42(Database issue):D756-63. PMID: 24259432; PMC: PMC3965018 Pruitt KD, Tatusova T, Maglott DR. NCBI Reference Sequence (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res. 2005 Jan 1;33(Database issue):D501-4. PMID: 15608248; PMC: PMC539979 
ucscRefSeqView UCSC RefSeq Genes RefSeq gene predictions from NCBI Genes and Gene Predictions 
refGene UCSC RefSeq UCSC annotations of RefSeq RNAs (NM_* and NR_*) Genes and Gene Predictions Description The RefSeq Genes track shows known chicken protein-coding and non-protein-coding genes taken from the NCBI RNA reference sequences collection (RefSeq). The data underlying this track are updated weekly. Please visit the Feedback for Gene and Reference Sequences (RefSeq) page to make suggestions, submit additions and corrections, or ask for help concerning RefSeq records. For more information on the different gene tracks, see our Genes FAQ. Display Conventions and Configuration This track follows the display conventions for gene prediction tracks. The color shading indicates the level of review the RefSeq record has undergone: predicted (light), provisional (medium), reviewed (dark). The item labels and display colors of features within this track can be configured through the controls at the top of the track description page. Label: By default, items are labeled by gene name. Click the appropriate Label option to display the accession name instead of the gene name, show both the gene and accession names, or turn off the label completely. Codon coloring: This track contains an optional codon coloring feature that allows users to quickly validate and compare gene predictions. To display codon colors, select the genomic codons option from the Color track by codons pull-down menu. For more information about this feature, go to the Coloring Gene Predictions and Annotations by Codon page. Hide non-coding genes: By default, both the protein-coding and non-protein-coding genes are displayed. If you wish to see only the coding genes, click this box. Methods RefSeq RNAs were aligned against the chicken genome using BLAT. Those with an alignment of less than 15% were discarded. When a single RNA aligned in multiple places, the alignment having the highest base identity was identified. Only alignments having a base identity level within 0.1% of the best and at least 96% base identity with the genomic sequence were kept. Credits This track was produced at UCSC from RNA sequence data generated by scientists worldwide and curated by the NCBI RefSeq project. References Kent WJ. BLAT - the BLAST-like alignment tool. Genome Res. 2002 Apr;12(4):656-64. PMID: 11932250; PMC: PMC187518 Pruitt KD, Brown GR, Hiatt SM, Thibaud-Nissen F, Astashyn A, Ermolaeva O, Farrell CM, Hart J, Landrum MJ, McGarvey KM et al. RefSeq: an update on mammalian reference sequences. Nucleic Acids Res. 2014 Jan;42(Database issue):D756-63. PMID: 24259432; PMC: PMC3965018 Pruitt KD, Tatusova T, Maglott DR. NCBI Reference Sequence (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res. 2005 Jan 1;33(Database issue):D501-4. PMID: 15608248; PMC: PMC539979 
ncbiRefSeqView NCBI RefSeq genes and alignments RefSeq gene predictions from NCBI Genes and Gene Predictions 
ncbiRefSeqGenomicDiff RefSeq Diffs Differences between NCBI RefSeq Transcripts and the Reference Genome Genes and Gene Predictions 
ncbiRefSeqPsl RefSeq Alignments RefSeq Alignments of RNAs Genes and Gene Predictions 
ncbiRefSeqOther RefSeq Other NCBI RefSeq other annotations (not NM_*, NR_*, XM_*, XR_*, or YP_*) Genes and Gene Predictions 
ncbiRefSeqPredicted RefSeq Predicted NCBI RefSeq genes, predicted subset (XM_* and XR_*) Genes and Gene Predictions 
ncbiRefSeqCurated RefSeq Curated NCBI RefSeq genes, curated subset (NM_*, NR_*, and YP_*) Genes and Gene Predictions 
ncbiRefSeq RefSeq All NCBI RefSeq genes, curated and predicted sets (NM_*, XM_*, NR_*, XR_*, and YP_*) Genes and Gene Predictions 
cpgIslandExtUnmasked Unmasked CpG CpG Islands on All Sequence (Islands < 300 Bases are Light Green) Expression and Regulation Description CpG islands are associated with genes, particularly housekeeping genes, in vertebrates. CpG islands are typically common near transcription start sites and may be associated with promoter regions. Normally a C (cytosine) base followed immediately by a G (guanine) base (a CpG) is rare in vertebrate DNA because the Cs in such an arrangement tend to be methylated. This methylation helps distinguish the newly synthesized DNA strand from the parent strand, which aids in the final stages of DNA proofreading after duplication. However, over evolutionary time, methylated Cs tend to turn into Ts because of spontaneous deamination. The result is that CpGs are relatively rare unless there is selective pressure to keep them or a region is not methylated for some other reason, perhaps having to do with the regulation of gene expression. CpG islands are regions where CpGs are present at significantly higher levels than is typical for the genome as a whole. The unmasked version of the track displays potential CpG islands that exist in repeat regions and would otherwise not be visible in the repeat masked version. By default, only the masked version of the track is displayed. To view the unmasked version, change the visibility settings in the track controls at the top of this page. Methods CpG islands were predicted by searching the sequence one base at a time, scoring each dinucleotide (+17 for CG and -1 for others) and identifying maximally scoring segments. Each segment was then evaluated for the following criteria: GC content of 50% or greater length greater than 200 bp ratio greater than 0.6 of observed number of CG dinucleotides to the expected number on the basis of the number of Gs and Cs in the segment The entire genome sequence, masking areas included, was used for the construction of the track Unmasked CpG. The track CpG Islands is constructed on the sequence after all masked sequence is removed. The CpG count is the number of CG dinucleotides in the island. The Percentage CpG is the ratio of CpG nucleotide bases (twice the CpG count) to the length. The ratio of observed to expected CpG is calculated according to the formula (cited in Gardiner-Garden et al. (1987)): Obs/Exp CpG = Number of CpG * N / (Number of C * Number of G) where N = length of sequence. The calculation of the track data is performed by the following command sequence: twoBitToFa assembly.2bit stdout | maskOutFa stdin hard stdout \ | cpg_lh /dev/stdin 2&gt; cpg_lh.err \ | awk '{&dollar;2 = &dollar;2 - 1; width = &dollar;3 - &dollar;2; printf("%s\t%d\t%s\t%s %s\t%s\t%s\t%0.0f\t%0.1f\t%s\t%s\n", &dollar;1, &dollar;2, &dollar;3, &dollar;5, &dollar;6, width, &dollar;6, width*&dollar;7*0.01, 100.0*2*&dollar;6/width, &dollar;7, &dollar;9);}' \ | sort -k1,1 -k2,2n &gt; cpgIsland.bed The unmasked track data is constructed from twoBitToFa -noMask output for the twoBitToFa command. Data access CpG islands and its associated tables can be explored interactively using the REST API, the Table Browser or the Data Integrator. All the tables can also be queried directly from our public MySQL servers, with more information available on our help page as well as on our blog. The source for the cpg_lh program can be obtained from src/utils/cpgIslandExt/. The cpg_lh program binary can be obtained from: http://hgdownload.soe.ucsc.edu/admin/exe/linux.x86_64/cpg_lh (choose "save file") Credits This track was generated using a modification of a program developed by G. Miklem and L. Hillier (unpublished). References Gardiner-Garden M, Frommer M. CpG islands in vertebrate genomes. J Mol Biol. 1987 Jul 20;196(2):261-82. PMID: 3656447 
transMapEnsemblV5 TransMap Ensembl TransMap Ensembl and GENCODE Mappings Version 5 Genes and Gene Predictions Description This track contains GENCODE or Ensembl alignments produced by the TransMap cross-species alignment algorithm from other vertebrate species in the UCSC Genome Browser. GENCODE is Ensembl for human and mouse, for other Ensembl sources, only ones with full gene builds are used. Projection Ensembl gene annotations will not be used as sources. For closer evolutionary distances, the alignments are created using syntenically filtered BLASTZ alignment chains, resulting in a prediction of the orthologous genes in chicken. Display Conventions and Configuration This track follows the display conventions for PSL alignment tracks. This track may also be configured to display codon coloring, a feature that allows the user to quickly compare cDNAs against the genomic sequence. For more information about this option, click here. Several types of alignment gap may also be colored; for more information, click here. Methods Source transcript alignments were obtained from vertebrate organisms in the UCSC Genome Browser Database. BLAT alignments of RefSeq Genes, GenBank mRNAs, and GenBank Spliced ESTs to the cognate genome, along with UCSC Genes, were used as available. For all vertebrate assemblies that had BLASTZ alignment chains and nets to the chicken (galGal6) genome, a subset of the alignment chains were selected as follows: For organisms whose branch distance was no more than 0.5 (as computed by phyloFit, see Conservation track description for details), syntenic filtering was used. Reciprocal best nets were used if available; otherwise, nets were selected with the netfilter -syn command. The chains corresponding to the selected nets were used for mapping. For more distant species, where the determination of synteny is difficult, the full set of chains was used for mapping. This allows for more genes to map at the expense of some mapping to paralogous regions. The post-alignment filtering step removes some of the duplications. The pslMap program was used to do a base-level projection of the source transcript alignments via the selected chains to the chicken genome, resulting in pairwise alignments of the source transcripts to the genome. The resulting alignments were filtered with pslCDnaFilter with a global near-best criteria of 0.5% in finished genomes (human and mouse) and 1.0% in other genomes. Alignments where less than 20% of the transcript mapped were discarded. To ensure unique identifiers for each alignment, cDNA and gene accessions were made unique by appending a suffix for each location in the source genome and again for each mapped location in the destination genome. The format is: accession.version-srcUniq.destUniq Where srcUniq is a number added to make each source alignment unique, and destUniq is added to give the subsequent TransMap alignments unique identifiers. For example, in the cow genome, there are two alignments of mRNA BC149621.1. These are assigned the identifiers BC149621.1-1 and BC149621.1-2. When these are mapped to the human genome, BC149621.1-1 maps to a single location and is given the identifier BC149621.1-1.1. However, BC149621.1-2 maps to two locations, resulting in BC149621.1-2.1 and BC149621.1-2.2. Note that multiple TransMap mappings are usually the result of tandem duplications, where both chains are identified as syntenic. Data Access The raw data for these tracks can be accessed interactively through the Table Browser or the Data Integrator. For automated analysis, the annotations are stored in bigPsl files (containing a number of extra columns) and can be downloaded from our download server, or queried using our API. For more information on accessing track data see our Track Data Access FAQ. The files are associated with these tracks in the following way: TransMap Ensembl - galGal6.ensembl.transMapV4.bigPsl TransMap RefGene - galGal6.refseq.transMapV4.bigPsl TransMap RNA - galGal6.rna.transMapV4.bigPsl TransMap ESTs - galGal6.est.transMapV4.bigPsl Individual regions or the whole genome annotation can be obtained using our tool bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tool can also be used to obtain only features within a given range, for example: bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/galGal6/transMap/V4/galGal6.refseq.transMapV4.bigPsl -chrom=chr6 -start=0 -end=1000000 stdout Credits This track was produced by Mark Diekhans at UCSC from cDNA and EST sequence data submitted to the international public sequence databases by scientists worldwide and annotations produced by the RefSeq, Ensembl, and GENCODE annotations projects. References Siepel A, Diekhans M, Brejov&#225; B, Langton L, Stevens M, Comstock CL, Davis C, Ewing B, Oommen S, Lau C et al. Targeted discovery of novel human exons by comparative genomics. Genome Res. 2007 Dec;17(12):1763-73. PMID: 17989246; PMC: PMC2099585 Stanke M, Diekhans M, Baertsch R, Haussler D. Using native and syntenically mapped cDNA alignments to improve de novo gene finding. Bioinformatics. 2008 Mar 1;24(5):637-44. PMID: 18218656 Zhu J, Sanborn JZ, Diekhans M, Lowe CB, Pringle TH, Haussler D. Comparative genomics search for losses of long-established genes on the human lineage. PLoS Comput Biol. 2007 Dec;3(12):e247. PMID: 18085818; PMC: PMC2134963 
transMapV5 TransMap V5 TransMap Alignments Version 5 Genes and Gene Predictions Description These tracks contain cDNA and gene alignments produced by the TransMap cross-species alignment algorithm from other vertebrate species in the UCSC Genome Browser. For closer evolutionary distances, the alignments are created using syntenically filtered LASTZ or BLASTZ alignment chains, resulting in a prediction of the orthologous genes in chicken. For more distant organisms, reciprocal best alignments are used. TransMap maps genes and related annotations in one species to another using synteny-filtered pairwise genome alignments (chains and nets) to determine the most likely orthologs. For example, for the mRNA TransMap track on the human assembly, more than 400,000 mRNAs from 25 vertebrate species were aligned at high stringency to the native assembly using BLAT. The alignments were then mapped to the human assembly using the chain and net alignments produced using BLASTZ, which has higher sensitivity than BLAT for diverged organisms. Compared to translated BLAT, TransMap finds fewer paralogs and aligns more UTR bases. Display Conventions and Configuration This track follows the display conventions for PSL alignment tracks. This track may also be configured to display codon coloring, a feature that allows the user to quickly compare cDNAs against the genomic sequence. For more information about this option, click here. Several types of alignment gap may also be colored; for more information, click here. Methods Source transcript alignments were obtained from vertebrate organisms in the UCSC Genome Browser Database. BLAT alignments of RefSeq Genes, GenBank mRNAs, and GenBank Spliced ESTs to the cognate genome, along with UCSC Genes, were used as available. For all vertebrate assemblies that had BLASTZ alignment chains and nets to the chicken (galGal6) genome, a subset of the alignment chains were selected as follows: For organisms whose branch distance was no more than 0.5 (as computed by phyloFit, see Conservation track description for details), syntenic filtering was used. Reciprocal best nets were used if available; otherwise, nets were selected with the netfilter -syn command. The chains corresponding to the selected nets were used for mapping. For more distant species, where the determination of synteny is difficult, the full set of chains was used for mapping. This allows for more genes to map at the expense of some mapping to paralogous regions. The post-alignment filtering step removes some of the duplications. The pslMap program was used to do a base-level projection of the source transcript alignments via the selected chains to the chicken genome, resulting in pairwise alignments of the source transcripts to the genome. The resulting alignments were filtered with pslCDnaFilter with a global near-best criteria of 0.5% in finished genomes (human and mouse) and 1.0% in other genomes. Alignments where less than 20% of the transcript mapped were discarded. To ensure unique identifiers for each alignment, cDNA and gene accessions were made unique by appending a suffix for each location in the source genome and again for each mapped location in the destination genome. The format is: accession.version-srcUniq.destUniq Where srcUniq is a number added to make each source alignment unique, and destUniq is added to give the subsequent TransMap alignments unique identifiers. For example, in the cow genome, there are two alignments of mRNA BC149621.1. These are assigned the identifiers BC149621.1-1 and BC149621.1-2. When these are mapped to the human genome, BC149621.1-1 maps to a single location and is given the identifier BC149621.1-1.1. However, BC149621.1-2 maps to two locations, resulting in BC149621.1-2.1 and BC149621.1-2.2. Note that multiple TransMap mappings are usually the result of tandem duplications, where both chains are identified as syntenic. Data Access The raw data for these tracks can be accessed interactively through the Table Browser or the Data Integrator. For automated analysis, the annotations are stored in bigPsl files (containing a number of extra columns) and can be downloaded from our download server, or queried using our API. For more information on accessing track data see our Track Data Access FAQ. The files are associated with these tracks in the following way: TransMap Ensembl - galGal6.ensembl.transMapV5.bigPsl TransMap RefGene - galGal6.refseq.transMapV5.bigPsl TransMap RNA - galGal6.rna.transMapV5.bigPsl TransMap ESTs - galGal6.est.transMapV5.bigPsl Individual regions or the whole genome annotation can be obtained using our tool bigBedToBed, which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tool can also be used to obtain only features within a given range, for example: bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/galGal6/transMap/V5/galGal6.refseq.transMapV5.bigPsl -chrom=chr6 -start=0 -end=1000000 stdout Credits This track was produced by Mark Diekhans at UCSC from cDNA and EST sequence data submitted to the international public sequence databases by scientists worldwide and annotations produced by the RefSeq, Ensembl, and GENCODE annotations projects. References Siepel A, Diekhans M, Brejov&#225; B, Langton L, Stevens M, Comstock CL, Davis C, Ewing B, Oommen S, Lau C et al. Targeted discovery of novel human exons by comparative genomics. Genome Res. 2007 Dec;17(12):1763-73. PMID: 17989246; PMC: PMC2099585 Stanke M, Diekhans M, Baertsch R, Haussler D. Using native and syntenically mapped cDNA alignments to improve de novo gene finding. Bioinformatics. 2008 Mar 1;24(5):637-44. PMID: 18218656 Zhu J, Sanborn JZ, Diekhans M, Lowe CB, Pringle TH, Haussler D. Comparative genomics search for losses of long-established genes on the human lineage. PLoS Comput Biol. 2007 Dec;3(12):e247. PMID: 18085818; PMC: PMC2134963 
transMapRefSeqV5 TransMap RefGene TransMap RefSeq Gene Mappings Version 5 Genes and Gene Predictions Description This track contains RefSeq Gene alignments produced by the TransMap cross-species alignment algorithm from other vertebrate species in the UCSC Genome Browser. For closer evolutionary distances, the alignments are created using syntenically filtered BLASTZ alignment chains, resulting in a prediction of the orthologous genes in chicken. Display Conventions and Configuration This track follows the display conventions for PSL alignment tracks. This track may also be configured to display codon coloring, a feature that allows the user to quickly compare cDNAs against the genomic sequence. For more information about this option, click here. Several types of alignment gap may also be colored; for more information, click here. Methods Source transcript alignments were obtained from vertebrate organisms in the UCSC Genome Browser Database. BLAT alignments of RefSeq Genes, GenBank mRNAs, and GenBank Spliced ESTs to the cognate genome, along with UCSC Genes, were used as available. For all vertebrate assemblies that had BLASTZ alignment chains and nets to the chicken (galGal6) genome, a subset of the alignment chains were selected as follows: For organisms whose branch distance was no more than 0.5 (as computed by phyloFit, see Conservation track description for details), syntenic filtering was used. Reciprocal best nets were used if available; otherwise, nets were selected with the netfilter -syn command. The chains corresponding to the selected nets were used for mapping. For more distant species, where the determination of synteny is difficult, the full set of chains was used for mapping. This allows for more genes to map at the expense of some mapping to paralogous regions. The post-alignment filtering step removes some of the duplications. The pslMap program was used to do a base-level projection of the source transcript alignments via the selected chains to the chicken genome, resulting in pairwise alignments of the source transcripts to the genome. The resulting alignments were filtered with pslCDnaFilter with a global near-best criteria of 0.5% in finished genomes (human and mouse) and 1.0% in other genomes. Alignments where less than 20% of the transcript mapped were discarded. To ensure unique identifiers for each alignment, cDNA and gene accessions were made unique by appending a suffix for each location in the source genome and again for each mapped location in the destination genome. The format is: accession.version-srcUniq.destUniq Where srcUniq is a number added to make each source alignment unique, and destUniq is added to give the subsequent TransMap alignments unique identifiers. For example, in the cow genome, there are two alignments of mRNA BC149621.1. These are assigned the identifiers BC149621.1-1 and BC149621.1-2. When these are mapped to the human genome, BC149621.1-1 maps to a single location and is given the identifier BC149621.1-1.1. However, BC149621.1-2 maps to two locations, resulting in BC149621.1-2.1 and BC149621.1-2.2. Note that multiple TransMap mappings are usually the result of tandem duplications, where both chains are identified as syntenic. Data Access The raw data for these tracks can be accessed interactively through the Table Browser or the Data Integrator. For automated analysis, the annotations are stored in bigPsl files (containing a number of extra columns) and can be downloaded from our download server, or queried using our API. For more information on accessing track data see our Track Data Access FAQ. The files are associated with these tracks in the following way: TransMap Ensembl - galGal6.ensembl.transMapV4.bigPsl TransMap RefGene - galGal6.refseq.transMapV4.bigPsl TransMap RNA - galGal6.rna.transMapV4.bigPsl TransMap ESTs - galGal6.est.transMapV4.bigPsl Individual regions or the whole genome annotation can be obtained using our tool bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tool can also be used to obtain only features within a given range, for example: bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/galGal6/transMap/V4/galGal6.refseq.transMapV4.bigPsl -chrom=chr6 -start=0 -end=1000000 stdout Credits This track was produced by Mark Diekhans at UCSC from cDNA and EST sequence data submitted to the international public sequence databases by scientists worldwide and annotations produced by the RefSeq, Ensembl, and GENCODE annotations projects. References Siepel A, Diekhans M, Brejov&#225; B, Langton L, Stevens M, Comstock CL, Davis C, Ewing B, Oommen S, Lau C et al. Targeted discovery of novel human exons by comparative genomics. Genome Res. 2007 Dec;17(12):1763-73. PMID: 17989246; PMC: PMC2099585 Stanke M, Diekhans M, Baertsch R, Haussler D. Using native and syntenically mapped cDNA alignments to improve de novo gene finding. Bioinformatics. 2008 Mar 1;24(5):637-44. PMID: 18218656 Zhu J, Sanborn JZ, Diekhans M, Lowe CB, Pringle TH, Haussler D. Comparative genomics search for losses of long-established genes on the human lineage. PLoS Comput Biol. 2007 Dec;3(12):e247. PMID: 18085818; PMC: PMC2134963 
transMapRnaV5 TransMap RNA TransMap GenBank RNA Mappings Version 5 Genes and Gene Predictions Description This track contains GenBank mRNA alignments produced by the TransMap cross-species alignment algorithm from other vertebrate species in the UCSC Genome Browser. For closer evolutionary distances, the alignments are created using syntenically filtered BLASTZ alignment chains, resulting in a prediction of the orthologous genes in chicken. Display Conventions and Configuration This track follows the display conventions for PSL alignment tracks. This track may also be configured to display codon coloring, a feature that allows the user to quickly compare cDNAs against the genomic sequence. For more information about this option, click here. Several types of alignment gap may also be colored; for more information, click here. Methods Source transcript alignments were obtained from vertebrate organisms in the UCSC Genome Browser Database. BLAT alignments of RefSeq Genes, GenBank mRNAs, and GenBank Spliced ESTs to the cognate genome, along with UCSC Genes, were used as available. For all vertebrate assemblies that had BLASTZ alignment chains and nets to the chicken (galGal6) genome, a subset of the alignment chains were selected as follows: For organisms whose branch distance was no more than 0.5 (as computed by phyloFit, see Conservation track description for details), syntenic filtering was used. Reciprocal best nets were used if available; otherwise, nets were selected with the netfilter -syn command. The chains corresponding to the selected nets were used for mapping. For more distant species, where the determination of synteny is difficult, the full set of chains was used for mapping. This allows for more genes to map at the expense of some mapping to paralogous regions. The post-alignment filtering step removes some of the duplications. The pslMap program was used to do a base-level projection of the source transcript alignments via the selected chains to the chicken genome, resulting in pairwise alignments of the source transcripts to the genome. The resulting alignments were filtered with pslCDnaFilter with a global near-best criteria of 0.5% in finished genomes (human and mouse) and 1.0% in other genomes. Alignments where less than 20% of the transcript mapped were discarded. To ensure unique identifiers for each alignment, cDNA and gene accessions were made unique by appending a suffix for each location in the source genome and again for each mapped location in the destination genome. The format is: accession.version-srcUniq.destUniq Where srcUniq is a number added to make each source alignment unique, and destUniq is added to give the subsequent TransMap alignments unique identifiers. For example, in the cow genome, there are two alignments of mRNA BC149621.1. These are assigned the identifiers BC149621.1-1 and BC149621.1-2. When these are mapped to the human genome, BC149621.1-1 maps to a single location and is given the identifier BC149621.1-1.1. However, BC149621.1-2 maps to two locations, resulting in BC149621.1-2.1 and BC149621.1-2.2. Note that multiple TransMap mappings are usually the result of tandem duplications, where both chains are identified as syntenic. Data Access The raw data for these tracks can be accessed interactively through the Table Browser or the Data Integrator. For automated analysis, the annotations are stored in bigPsl files (containing a number of extra columns) and can be downloaded from our download server, or queried using our API. For more information on accessing track data see our Track Data Access FAQ. The files are associated with these tracks in the following way: TransMap Ensembl - galGal6.ensembl.transMapV4.bigPsl TransMap RefGene - galGal6.refseq.transMapV4.bigPsl TransMap RNA - galGal6.rna.transMapV4.bigPsl TransMap ESTs - galGal6.est.transMapV4.bigPsl Individual regions or the whole genome annotation can be obtained using our tool bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tool can also be used to obtain only features within a given range, for example: bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/galGal6/transMap/V4/galGal6.refseq.transMapV4.bigPsl -chrom=chr6 -start=0 -end=1000000 stdout Credits This track was produced by Mark Diekhans at UCSC from cDNA and EST sequence data submitted to the international public sequence databases by scientists worldwide and annotations produced by the RefSeq, Ensembl, and GENCODE annotations projects. References Siepel A, Diekhans M, Brejov&#225; B, Langton L, Stevens M, Comstock CL, Davis C, Ewing B, Oommen S, Lau C et al. Targeted discovery of novel human exons by comparative genomics. Genome Res. 2007 Dec;17(12):1763-73. PMID: 17989246; PMC: PMC2099585 Stanke M, Diekhans M, Baertsch R, Haussler D. Using native and syntenically mapped cDNA alignments to improve de novo gene finding. Bioinformatics. 2008 Mar 1;24(5):637-44. PMID: 18218656 Zhu J, Sanborn JZ, Diekhans M, Lowe CB, Pringle TH, Haussler D. Comparative genomics search for losses of long-established genes on the human lineage. PLoS Comput Biol. 2007 Dec;3(12):e247. PMID: 18085818; PMC: PMC2134963 
transMapEstV5 TransMap ESTs TransMap EST Mappings Version 5 Genes and Gene Predictions Description This track contains GenBank spliced EST alignments produced by the TransMap cross-species alignment algorithm from other vertebrate species in the UCSC Genome Browser. For closer evolutionary distances, the alignments are created using syntenically filtered BLASTZ alignment chains, resulting in a prediction of the orthologous genes in chicken. Display Conventions and Configuration This track follows the display conventions for PSL alignment tracks. This track may also be configured to display codon coloring, a feature that allows the user to quickly compare cDNAs against the genomic sequence. For more information about this option, click here. Several types of alignment gap may also be colored; for more information, click here. Methods Source transcript alignments were obtained from vertebrate organisms in the UCSC Genome Browser Database. BLAT alignments of RefSeq Genes, GenBank mRNAs, and GenBank Spliced ESTs to the cognate genome, along with UCSC Genes, were used as available. For all vertebrate assemblies that had BLASTZ alignment chains and nets to the chicken (galGal6) genome, a subset of the alignment chains were selected as follows: For organisms whose branch distance was no more than 0.5 (as computed by phyloFit, see Conservation track description for details), syntenic filtering was used. Reciprocal best nets were used if available; otherwise, nets were selected with the netfilter -syn command. The chains corresponding to the selected nets were used for mapping. For more distant species, where the determination of synteny is difficult, the full set of chains was used for mapping. This allows for more genes to map at the expense of some mapping to paralogous regions. The post-alignment filtering step removes some of the duplications. The pslMap program was used to do a base-level projection of the source transcript alignments via the selected chains to the chicken genome, resulting in pairwise alignments of the source transcripts to the genome. The resulting alignments were filtered with pslCDnaFilter with a global near-best criteria of 0.5% in finished genomes (human and mouse) and 1.0% in other genomes. Alignments where less than 20% of the transcript mapped were discarded. To ensure unique identifiers for each alignment, cDNA and gene accessions were made unique by appending a suffix for each location in the source genome and again for each mapped location in the destination genome. The format is: accession.version-srcUniq.destUniq Where srcUniq is a number added to make each source alignment unique, and destUniq is added to give the subsequent TransMap alignments unique identifiers. For example, in the cow genome, there are two alignments of mRNA BC149621.1. These are assigned the identifiers BC149621.1-1 and BC149621.1-2. When these are mapped to the human genome, BC149621.1-1 maps to a single location and is given the identifier BC149621.1-1.1. However, BC149621.1-2 maps to two locations, resulting in BC149621.1-2.1 and BC149621.1-2.2. Note that multiple TransMap mappings are usually the result of tandem duplications, where both chains are identified as syntenic. Data Access The raw data for these tracks can be accessed interactively through the Table Browser or the Data Integrator. For automated analysis, the annotations are stored in bigPsl files (containing a number of extra columns) and can be downloaded from our download server, or queried using our API. For more information on accessing track data see our Track Data Access FAQ. The files are associated with these tracks in the following way: TransMap Ensembl - galGal6.ensembl.transMapV4.bigPsl TransMap RefGene - galGal6.refseq.transMapV4.bigPsl TransMap RNA - galGal6.rna.transMapV4.bigPsl TransMap ESTs - galGal6.est.transMapV4.bigPsl Individual regions or the whole genome annotation can be obtained using our tool bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tool can also be used to obtain only features within a given range, for example: bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/galGal6/transMap/V4/galGal6.refseq.transMapV4.bigPsl -chrom=chr6 -start=0 -end=1000000 stdout Credits This track was produced by Mark Diekhans at UCSC from cDNA and EST sequence data submitted to the international public sequence databases by scientists worldwide and annotations produced by the RefSeq, Ensembl, and GENCODE annotations projects. References Siepel A, Diekhans M, Brejov&#225; B, Langton L, Stevens M, Comstock CL, Davis C, Ewing B, Oommen S, Lau C et al. Targeted discovery of novel human exons by comparative genomics. Genome Res. 2007 Dec;17(12):1763-73. PMID: 17989246; PMC: PMC2099585 Stanke M, Diekhans M, Baertsch R, Haussler D. Using native and syntenically mapped cDNA alignments to improve de novo gene finding. Bioinformatics. 2008 Mar 1;24(5):637-44. PMID: 18218656 Zhu J, Sanborn JZ, Diekhans M, Lowe CB, Pringle TH, Haussler D. Comparative genomics search for losses of long-established genes on the human lineage. PLoS Comput Biol. 2007 Dec;3(12):e247. PMID: 18085818; PMC: PMC2134963 
gold Assembly Assembly from Fragments Mapping and Sequencing Description This track shows the sequences used in the Mar. 2018 chicken genome assembly. Genome assembly procedures are covered in the NCBI assembly documentation. NCBI also provides specific information about this assembly. The definition of this assembly is from the AGP file delivered with the sequence. The NCBI document AGP Specification describes the format of the AGP file. In dense mode, this track depicts the contigs that make up the currently viewed scaffold. Contig boundaries are distinguished by the use of alternating gold and brown coloration. Where gaps exist between contigs, spaces are shown between the gold and brown blocks. The relative order and orientation of the contigs within a scaffold is always known; therefore, a line is drawn in the graphical display to bridge the blocks. Component types found in this track (with counts of that type in parentheses): W - whole genome shotgun (1,519) F - finished sequence (854) A - active finishing (6) O - one other sequence (chrM/NC_001323.1) 
augustusGene AUGUSTUS AUGUSTUS ab initio gene predictions v3.1 Genes and Gene Predictions Description This track shows ab initio predictions from the program AUGUSTUS (version 3.1). The predictions are based on the genome sequence alone. For more information on the different gene tracks, see our Genes FAQ. Methods Statistical signal models were built for splice sites, branch-point patterns, translation start sites, and the poly-A signal. Furthermore, models were built for the sequence content of protein-coding and non-coding regions as well as for the length distributions of different exon and intron types. Detailed descriptions of most of these different models can be found in Mario Stanke's dissertation. This track shows the most likely gene structure according to a Semi-Markov Conditional Random Field model. Alternative splicing transcripts were obtained with a sampling algorithm (--alternatives-from-sampling=true --sample=100 --minexonintronprob=0.2 --minmeanexonintronprob=0.5 --maxtracks=3 --temperature=2). The different models used by Augustus were trained on a number of different species-specific gene sets, which included 1000-2000 training gene structures. The --species option allows one to choose the species used for training the models. Different training species were used for the --species option when generating these predictions for different groups of assemblies. Assembly Group Training Species Fish zebrafish Birds chicken Human and all other vertebrates human Nematodes caenorhabditis Drosophila fly A. mellifera honeybee1 A. gambiae culex S. cerevisiae saccharomyces This table describes which training species was used for a particular group of assemblies. When available, the closest related training species was used. Credits Thanks to the Stanke lab for providing the AUGUSTUS program. The training for the chicken version was done by Stefanie K&ouml;nig and the training for the human and zebrafish versions was done by Mario Stanke. References Stanke M, Diekhans M, Baertsch R, Haussler D. Using native and syntenically mapped cDNA alignments to improve de novo gene finding. Bioinformatics. 2008 Mar 1;24(5):637-44. PMID: 18218656 Stanke M, Waack S. Gene prediction with a hidden Markov model and a new intron submodel. Bioinformatics. 2003 Oct;19 Suppl 2:ii215-25. PMID: 14534192 
birdsChainNet Birds Chain/Net Birds Chain and Net Alignments Comparative Genomics Description Chain Track The chain track shows alignments of chicken (Mar. 2018 (GRCg6a/galGal6)/galGal6) to other genomes using a gap scoring system that allows longer gaps than traditional affine gap scoring systems. It can also tolerate gaps in both chicken and the other genome simultaneously. These &quot;double-sided&quot; gaps can be caused by local inversions and overlapping deletions in both species. The chain track displays boxes joined together by either single or double lines. The boxes represent aligning regions. Single lines indicate gaps that are largely due to a deletion in the chicken assembly or an insertion in the chicken assembly. Double lines represent more complex gaps that involve substantial sequence in both species. This may result from inversions, overlapping deletions, an abundance of local mutation, or an unsequenced gap in one species. In cases where multiple chains align over a particular region of the other genome, the chains with single-lined gaps are often due to processed pseudogenes, while chains with double-lined gaps are more often due to paralogs and unprocessed pseudogenes. In the "pack" and "full" display modes, the individual feature names indicate the chromosome, strand, and location (in thousands) of the match for each matching alignment. Net Track The net track shows the best chicken/other chain for every part of the other genome. It is useful for finding orthologous regions and for studying genome rearrangement. The chicken sequence used in this annotation is from the Mar. 2018 (GRCg6a/galGal6) (galGal6) assembly. Display Conventions and Configuration Multiple species are grouped together in a composite track. In the display and on the configuration page, an effort was made to group them loosely into "clades." These groupings are based on the taxonomic classification at NCBI, using the CommonTree tool. Some organisms may be pulled from a larger group into a subgroup, to emphasize a relationship. For example, members of an Order may be listed together, while other organisms in the same Superorder may be grouped separately under the Superorder name. Chain Track By default, the chains to chromosome-based assemblies are colored based on which chromosome they map to in the aligning organism. To turn off the coloring, check the &quot;off&quot; button next to: Color track based on chromosome. To display only the chains of one chromosome in the aligning organism, enter the name of that chromosome (e.g. chr4) in box next to: Filter by chromosome. Net Track In full display mode, the top-level (level 1) chains are the largest, highest-scoring chains that span this region. In many cases gaps exist in the top-level chain. When possible, these are filled in by other chains that are displayed at level 2. The gaps in level 2 chains may be filled by level 3 chains and so forth. In the graphical display, the boxes represent ungapped alignments; the lines represent gaps. Click on a box to view detailed information about the chain as a whole; click on a line to display information about the gap. The detailed information is useful in determining the cause of the gap or, for lower level chains, the genomic rearrangement. Individual items in the display are categorized as one of four types (other than gap): Top - the best, longest match. Displayed on level 1. Syn - line-ups on the same chromosome as the gap in the level above it. Inv - a line-up on the same chromosome as the gap above it, but in the opposite orientation. NonSyn - a match to a chromosome different from the gap in the level above. Methods Chain track Transposons that have been inserted since the chicken/other split were removed from the assemblies. The abbreviated genomes were aligned with lastz, and the transposons were added back in. The resulting alignments were converted into axt format using the lavToAxt program. The axt alignments were fed into axtChain, which organizes all alignments between a single chicken chromosome and a single chromosome from the other genome into a group and creates a kd-tree out of the gapless subsections (blocks) of the alignments. A dynamic program was then run over the kd-trees to find the maximally scoring chains of these blocks. See also: lastz parameters and other details (e.g., update time) and chain minimum score and gap parameters used in these alignments. Net track Chains were derived from lastz alignments, using the methods described on the chain tracks description pages, and sorted with the highest-scoring chains in the genome ranked first. The program chainNet was then used to place the chains one at a time, trimming them as necessary to fit into sections not already covered by a higher-scoring chain. During this process, a natural hierarchy emerged in which a chain that filled a gap in a higher-scoring chain was placed underneath that chain. The program netSyntenic was used to fill in information about the relationship between higher- and lower-level chains, such as whether a lower-level chain was syntenic or inverted relative to the higher-level chain. The program netClass was then used to fill in how much of the gaps and chains contained Ns (sequencing gaps) in one or both species and how much was filled with transposons inserted before and after the two organisms diverged. Credits Lastz (previously known as blastz) was developed at Pennsylvania State University by Minmei Hou, Scott Schwartz, Zheng Zhang, and Webb Miller with advice from Ross Hardison. Lineage-specific repeats were identified by Arian Smit and his RepeatMasker program. The axtChain program was developed at the University of California at Santa Cruz by Jim Kent with advice from Webb Miller and David Haussler. The browser display and database storage of the chains and nets were created by Robert Baertsch and Jim Kent. The chainNet, netSyntenic, and netClass programs were developed at the University of California Santa Cruz by Jim Kent. References Chiaromonte F, Yap VB, Miller W. Scoring pairwise genomic sequence alignments. Pac Symp Biocomput. 2002:115-26. PMID: 11928468 Kent WJ, Baertsch R, Hinrichs A, Miller W, Haussler D. Evolution's cauldron: duplication, deletion, and rearrangement in the mouse and human genomes. Proc Natl Acad Sci U S A. 2003 Sep 30;100(20):11484-9. PMID: 14500911; PMC: PMC208784 Schwartz S, Kent WJ, Smit A, Zhang Z, Baertsch R, Hardison RC, Haussler D, Miller W. Human-mouse alignments with BLASTZ. Genome Res. 2003 Jan;13(1):103-7. PMID: 12529312; PMC: PMC430961 
birdsChainNetViewnet Nets Birds Chain and Net Alignments Comparative Genomics 
netAraMac1 araMac1 Net Scarlet macaw (Jun 2013 (SMACv1.1/araMac1)) Alignment Net Comparative Genomics 
netAmaVit1 amaVit1 Net Parrot (Jan. 2013 (AV1/amaVit1)) Alignment Net Comparative Genomics 
netChlUnd1 chlUnd1 Net Houbara bustard (Jun 2014 (ASM69519v1/chlUnd1)) Alignment Net Comparative Genomics 
netBalPav1 balPav1 Net Crowned crain (Jun 2014 (ASM70989v1/balPav1)) Alignment Net Comparative Genomics 
netPhoRub1 phoRub1 Net American flamingo (May 2014 (ASM68726v1/phoRub1)) Alignment Net Comparative Genomics 
netHalLeu1 halLeu1 Net Bald eagle (Aug 2014 (Haliaeetus_leucocephalus-4.0/halLeu1)) Alignment Net Comparative Genomics 
netHalAlb1 halAlb1 Net White-tailed eagle (May 2014 (ASM69140v1/halAlb1)) Alignment Net Comparative Genomics 
netFalPer1 falPer1 Net Peregrine falcon (Feb 2013 (F_peregrinus_v1.0/falPer1)) Alignment Net Comparative Genomics 
netFalChe1 falChe1 Net Saker falcon (Feb 2013 (F_cherrug_v1.0/falChe1)) Alignment Net Comparative Genomics 
netAquChr2 aquChr2 Net Golden eagle (Oct. 2014 (aquChr-1.0.2/aquChr2)) Alignment Net Comparative Genomics 
netMesUni1 mesUni1 Net Brown roatelo (Jun 2014 (ASM69576v1/mesUni1)) Alignment Net Comparative Genomics 
netEurHel1 eurHel1 Net Sunbittern (May 2014 (ASM69077v1/eurHel1)) Alignment Net Comparative Genomics 
netCarCri1 carCri1 Net Red-legged seriema (May 2014 (ASM69053v1/carCri1)) Alignment Net Comparative Genomics 
netPicPub1 picPub1 Net Downy woodpecker (Jun 2014 (ASM69900v1/picPub1)) Alignment Net Comparative Genomics 
netCalAnn1 calAnn1 Net Anna's hummingbird (Jun 2014 (ASM69908v1/calAnn1)) Alignment Net Comparative Genomics 
netBucRhi1 bucRhi1 Net Rhinoceros hornbill (Jun 2014 (ASM71030v1/bucRhi1)) Alignment Net Comparative Genomics 
netColStr1 colStr1 Net Speckled mousebird (May 2014 (ASM69071v1/colStr1)) Alignment Net Comparative Genomics 
netApaVit1 apaVit1 Net Bar tailed trogon (16 Jun 2014 (ASM70340v1/apaVit1)) Alignment Net Comparative Genomics 
netMerNub1 merNub1 Net Northern carmine bee-eater (May 2014 (ASM69184v1/merNub1)) Alignment Net Comparative Genomics 
netMelUnd1 melUnd1 Net Budgerigar (Sep. 2011 (WUSTL v6.3/melUnd1)) Alignment Net Comparative Genomics 
netPteGut1 pteGut1 Net Yellow-throated sandgrouse (Jun 2014 (ASM69924v1/pteGut1)) Alignment Net Comparative Genomics 
netColLiv1 colLiv1 Net Rock pigeon (Feb 2013 (Cliv_1.0/colLiv1)) Alignment Net Comparative Genomics 
netCucCan1 cucCan1 Net Common cuckoo (Jun 2014 (ASM70932v1/cucCan1)) Alignment Net Comparative Genomics 
netCapCar1 capCar1 Net Chuck-will's-widow (Jun 2014 (ASM70074v1/capCar1)) Alignment Net Comparative Genomics 
netTytAlb1 tytAlb1 Net Barn owl (May 2014 (ASM68720v1/tytAlb1)) Alignment Net Comparative Genomics 
netTauEry1 tauEry1 Net Red crested turaco (Jun 2014 (ASM70936v1/tauEry1)) Alignment Net Comparative Genomics 
netOpiHoa1 opiHoa1 Net Hoatzin (May 2014 (ASM69207v1/opiHoa1)) Alignment Net Comparative Genomics 
netGavSte1 gavSte1 Net Red-throated loon (May 2014 (ASM69087v1/gavSte1)) Alignment Net Comparative Genomics 
netLepDis1 lepDis1 Net Cuckoo roller (May 2014 (ASM69178v1/lepDis1)) Alignment Net Comparative Genomics 
netFulGla1 fulGla1 Net Northern fulmar (May 2014 (ASM69083v1/fulGla1)) Alignment Net Comparative Genomics 
netPygAde1 pygAde1 Net Adelie penguin (Jun 2014 (ASM69910v1/pygAde1)) Alignment Net Comparative Genomics 
netChaVoc2 chaVoc2 Net Killdeer (Aug 2014 (ASM70802v2/chaVoc2)) Alignment Net Comparative Genomics 
netAptFor1 aptFor1 Net Emperor penguin (06 Jun 2014 (ASM69914v1/aptFor1)) Alignment Net Comparative Genomics 
netTinGut2 tinGut2 Net White throated tinamou (Aug 2014 (ASM70537v2/tinGut2)) Alignment Net Comparative Genomics 
netStrCam1 strCam1 Net Ostrich (06 Jun-2014 (ASM69896v1/strCam1)) Alignment Net Comparative Genomics 
netZonAlb1 zonAlb1 Net White-throated sparrow (Apr 2013 (ASM38545v1/zonAlb1)) Alignment Net Comparative Genomics 
netTaeGut2 Zebra finch Net Zebra finch (Feb. 2013 (WashU taeGut324/taeGut2)) Alignment Net Comparative Genomics 
netSerCan1 serCan1 Net Canary (15 Jan-2014 (SCA1/serCan1)) Alignment Net Comparative Genomics 
netPseHum1 pseHum1 Net Tibetan ground jay (Jan 2013 (PseHum1.0/pseHum1)) Alignment Net Comparative Genomics 
netGeoFor1 Medium ground finch Net Medium ground finch (Apr. 2012 (GeoFor_1.0/geoFor1)) Alignment Net Comparative Genomics 
netFicAlb2 ficAlb2 Net Collared flycatcher (Jun 2013 (FicAlb1.5/ficAlb2)) Alignment Net Comparative Genomics 
netCorCor1 corCor1 Net Hooded crow (Aug 2014 (Hooded_Crow_genome/corCor1)) Alignment Net Comparative Genomics 
netCorBra1 corBra1 Net American crow (Jun 2014 (ASM69197v1/corBra1)) Alignment Net Comparative Genomics 
netAcaChl1 acaChl1 Net Rifleman (27 May 2014 (ASM69581v1/acaChl1)) Alignment Net Comparative Genomics 
netPhaLep1 phaLep1 Net White-tailed tropicbird (May 2014 (ASM68728v1/phaLep1)) Alignment Net Comparative Genomics 
netPhaCar1 phaCar1 Net Great cormorant (Jun 2014 (ASM70892v1/phaCar1)) Alignment Net Comparative Genomics 
netPelCri1 pelCri1 Net Dalmatian pelican (May 2014 (ASM68737v1/pelCri1)) Alignment Net Comparative Genomics 
netNipNip1 nipNip1 Net Crested ibis (Jun 2014 (ASM70822v1/nipNip1)) Alignment Net Comparative Genomics 
netEgrGar1 egrGar1 Net Little egret (May 2014 (ASM68718v1/egrGar1)) Alignment Net Comparative Genomics 
netMelGal5 Turkey Net Turkey (Nov. 2014 (Turkey_5.0/melGal5)) Alignment Net Comparative Genomics 
netAnaPla1 anaPla1 Net Mallard duck (Apr 2013 (BGI_duck_1.0/anaPla1)) Alignment Net Comparative Genomics 
netCotJap2 cotJap2 Net Japanese quail (Mar. 2016 (Coturnix japonica 2.0/cotJap2)) Alignment Net Comparative Genomics 
birdsChainNetViewchain Chains Birds Chain and Net Alignments Comparative Genomics 
chainAraMac1 araMac1 Chain Scarlet macaw (Jun 2013 (SMACv1.1/araMac1)) Chained Alignments Comparative Genomics 
chainAmaVit1 amaVit1 Chain Parrot (Jan. 2013 (AV1/amaVit1)) Chained Alignments Comparative Genomics 
chainChlUnd1 chlUnd1 Chain Houbara bustard (Jun 2014 (ASM69519v1/chlUnd1)) Chained Alignments Comparative Genomics 
chainBalPav1 balPav1 Chain Crowned crain (Jun 2014 (ASM70989v1/balPav1)) Chained Alignments Comparative Genomics 
chainPhoRub1 phoRub1 Chain American flamingo (May 2014 (ASM68726v1/phoRub1)) Chained Alignments Comparative Genomics 
chainHalLeu1 halLeu1 Chain Bald eagle (Aug 2014 (Haliaeetus_leucocephalus-4.0/halLeu1)) Chained Alignments Comparative Genomics 
chainHalAlb1 halAlb1 Chain White-tailed eagle (May 2014 (ASM69140v1/halAlb1)) Chained Alignments Comparative Genomics 
chainFalPer1 falPer1 Chain Peregrine falcon (Feb 2013 (F_peregrinus_v1.0/falPer1)) Chained Alignments Comparative Genomics 
chainFalChe1 falChe1 Chain Saker falcon (Feb 2013 (F_cherrug_v1.0/falChe1)) Chained Alignments Comparative Genomics 
chainAquChr2 aquChr2 Chain Golden eagle (Oct. 2014 (aquChr-1.0.2/aquChr2)) Chained Alignments Comparative Genomics 
chainMesUni1 mesUni1 Chain Brown roatelo (Jun 2014 (ASM69576v1/mesUni1)) Chained Alignments Comparative Genomics 
chainEurHel1 eurHel1 Chain Sunbittern (May 2014 (ASM69077v1/eurHel1)) Chained Alignments Comparative Genomics 
chainCarCri1 carCri1 Chain Red-legged seriema (May 2014 (ASM69053v1/carCri1)) Chained Alignments Comparative Genomics 
chainPicPub1 picPub1 Chain Downy woodpecker (Jun 2014 (ASM69900v1/picPub1)) Chained Alignments Comparative Genomics 
chainCalAnn1 calAnn1 Chain Anna's hummingbird (Jun 2014 (ASM69908v1/calAnn1)) Chained Alignments Comparative Genomics 
chainBucRhi1 bucRhi1 Chain Rhinoceros hornbill (Jun 2014 (ASM71030v1/bucRhi1)) Chained Alignments Comparative Genomics 
chainColStr1 colStr1 Chain Speckled mousebird (May 2014 (ASM69071v1/colStr1)) Chained Alignments Comparative Genomics 
chainApaVit1 apaVit1 Chain Bar tailed trogon (16 Jun 2014 (ASM70340v1/apaVit1)) Chained Alignments Comparative Genomics 
chainMerNub1 merNub1 Chain Northern carmine bee-eater (May 2014 (ASM69184v1/merNub1)) Chained Alignments Comparative Genomics 
chainMelUnd1 melUnd1 Chain Budgerigar (Sep. 2011 (WUSTL v6.3/melUnd1)) Chained Alignments Comparative Genomics 
chainPteGut1 pteGut1 Chain Yellow-throated sandgrouse (Jun 2014 (ASM69924v1/pteGut1)) Chained Alignments Comparative Genomics 
chainColLiv1 colLiv1 Chain Rock pigeon (Feb 2013 (Cliv_1.0/colLiv1)) Chained Alignments Comparative Genomics 
chainCucCan1 cucCan1 Chain Common cuckoo (Jun 2014 (ASM70932v1/cucCan1)) Chained Alignments Comparative Genomics 
chainCapCar1 capCar1 Chain Chuck-will's-widow (Jun 2014 (ASM70074v1/capCar1)) Chained Alignments Comparative Genomics 
chainTytAlb1 tytAlb1 Chain Barn owl (May 2014 (ASM68720v1/tytAlb1)) Chained Alignments Comparative Genomics 
chainTauEry1 tauEry1 Chain Red crested turaco (Jun 2014 (ASM70936v1/tauEry1)) Chained Alignments Comparative Genomics 
chainOpiHoa1 opiHoa1 Chain Hoatzin (May 2014 (ASM69207v1/opiHoa1)) Chained Alignments Comparative Genomics 
chainGavSte1 gavSte1 Chain Red-throated loon (May 2014 (ASM69087v1/gavSte1)) Chained Alignments Comparative Genomics 
chainLepDis1 lepDis1 Chain Cuckoo roller (May 2014 (ASM69178v1/lepDis1)) Chained Alignments Comparative Genomics 
chainFulGla1 fulGla1 Chain Northern fulmar (May 2014 (ASM69083v1/fulGla1)) Chained Alignments Comparative Genomics 
chainPygAde1 pygAde1 Chain Adelie penguin (Jun 2014 (ASM69910v1/pygAde1)) Chained Alignments Comparative Genomics 
chainChaVoc2 chaVoc2 Chain Killdeer (Aug 2014 (ASM70802v2/chaVoc2)) Chained Alignments Comparative Genomics 
chainAptFor1 aptFor1 Chain Emperor penguin (06 Jun 2014 (ASM69914v1/aptFor1)) Chained Alignments Comparative Genomics 
chainTinGut2 tinGut2 Chain White throated tinamou (Aug 2014 (ASM70537v2/tinGut2)) Chained Alignments Comparative Genomics 
chainStrCam1 strCam1 Chain Ostrich (06 Jun-2014 (ASM69896v1/strCam1)) Chained Alignments Comparative Genomics 
chainZonAlb1 zonAlb1 Chain White-throated sparrow (Apr 2013 (ASM38545v1/zonAlb1)) Chained Alignments Comparative Genomics 
chainTaeGut2 Zebra finch Chain Zebra finch (Feb. 2013 (WashU taeGut324/taeGut2)) Chained Alignments Comparative Genomics 
chainSerCan1 serCan1 Chain Canary (15 Jan-2014 (SCA1/serCan1)) Chained Alignments Comparative Genomics 
chainPseHum1 pseHum1 Chain Tibetan ground jay (Jan 2013 (PseHum1.0/pseHum1)) Chained Alignments Comparative Genomics 
chainGeoFor1 Medium ground finch Chain Medium ground finch (Apr. 2012 (GeoFor_1.0/geoFor1)) Chained Alignments Comparative Genomics 
chainFicAlb2 ficAlb2 Chain Collared flycatcher (Jun 2013 (FicAlb1.5/ficAlb2)) Chained Alignments Comparative Genomics 
chainCorCor1 corCor1 Chain Hooded crow (Aug 2014 (Hooded_Crow_genome/corCor1)) Chained Alignments Comparative Genomics 
chainCorBra1 corBra1 Chain American crow (Jun 2014 (ASM69197v1/corBra1)) Chained Alignments Comparative Genomics 
chainAcaChl1 acaChl1 Chain Rifleman (27 May 2014 (ASM69581v1/acaChl1)) Chained Alignments Comparative Genomics 
chainPhaLep1 phaLep1 Chain White-tailed tropicbird (May 2014 (ASM68728v1/phaLep1)) Chained Alignments Comparative Genomics 
chainPhaCar1 phaCar1 Chain Great cormorant (Jun 2014 (ASM70892v1/phaCar1)) Chained Alignments Comparative Genomics 
chainPelCri1 pelCri1 Chain Dalmatian pelican (May 2014 (ASM68737v1/pelCri1)) Chained Alignments Comparative Genomics 
chainNipNip1 nipNip1 Chain Crested ibis (Jun 2014 (ASM70822v1/nipNip1)) Chained Alignments Comparative Genomics 
chainEgrGar1 egrGar1 Chain Little egret (May 2014 (ASM68718v1/egrGar1)) Chained Alignments Comparative Genomics 
chainMelGal5 Turkey Chain Turkey (Nov. 2014 (Turkey_5.0/melGal5)) Chained Alignments Comparative Genomics 
chainAnaPla1 anaPla1 Chain Mallard duck (Apr 2013 (BGI_duck_1.0/anaPla1)) Chained Alignments Comparative Genomics 
chainCotJap2 cotJap2 Chain Japanese quail (Mar. 2016 (Coturnix japonica 2.0/cotJap2)) Chained Alignments Comparative Genomics 
est Chicken ESTs Chicken ESTs Including Unspliced mRNA and EST Description This track shows alignments between chicken expressed sequence tags (ESTs) in GenBank and the genome. ESTs are single-read sequences, typically about 500 bases in length, that usually represent fragments of transcribed genes. Display Conventions and Configuration This track follows the display conventions for PSL alignment tracks. In dense display mode, the items that are more darkly shaded indicate matches of better quality. The strand information (+/-) indicates the direction of the match between the EST and the matching genomic sequence. It bears no relationship to the direction of transcription of the RNA with which it might be associated. The description page for this track has a filter that can be used to change the display mode, alter the color, and include/exclude a subset of items within the track. This may be helpful when many items are shown in the track display, especially when only some are relevant to the current task. To use the filter: Type a term in one or more of the text boxes to filter the EST display. For example, to apply the filter to all ESTs expressed in a specific organ, type the name of the organ in the tissue box. To view the list of valid terms for each text box, consult the table in the Table Browser that corresponds to the factor on which you wish to filter. For example, the &quot;tissue&quot; table contains all the types of tissues that can be entered into the tissue text box. Multiple terms may be entered at once, separated by a space. Wildcards may also be used in the filter. If filtering on more than one value, choose the desired combination logic. If &quot;and&quot; is selected, only ESTs that match all filter criteria will be highlighted. If &quot;or&quot; is selected, ESTs that match any one of the filter criteria will be highlighted. Choose the color or display characteristic that should be used to highlight or include/exclude the filtered items. If &quot;exclude&quot; is chosen, the browser will not display ESTs that match the filter criteria. If &quot;include&quot; is selected, the browser will display only those ESTs that match the filter criteria. This track may also be configured to display base labeling, a feature that allows the user to display all bases in the aligning sequence or only those that differ from the genomic sequence. For more information about this option, go to the Base Coloring for Alignment Tracks page. Several types of alignment gap may also be colored; for more information, go to the Alignment Insertion/Deletion Display Options page. Methods To make an EST, RNA is isolated from cells and reverse transcribed into cDNA. Typically, the cDNA is cloned into a plasmid vector and a read is taken from the 5' and/or 3' primer. For most &mdash; but not all &mdash; ESTs, the reverse transcription is primed by an oligo-dT, which hybridizes with the poly-A tail of mature mRNA. The reverse transcriptase may or may not make it to the 5' end of the mRNA, which may or may not be degraded. In general, the 3' ESTs mark the end of transcription reasonably well, but the 5' ESTs may end at any point within the transcript. Some of the newer cap-selected libraries cover transcription start reasonably well. Before the cap-selection techniques emerged, some projects used random rather than poly-A priming in an attempt to retrieve sequence distant from the 3' end. These projects were successful at this, but as a side effect also deposited sequences from unprocessed mRNA and perhaps even genomic sequences into the EST databases. Even outside of the random-primed projects, there is a degree of non-mRNA contamination. Because of this, a single unspliced EST should be viewed with considerable skepticism. To generate this track, chicken ESTs from GenBank were aligned against the genome using blat. Note that the maximum intron length allowed by blat is 750,000 bases, which may eliminate some ESTs with very long introns that might otherwise align. When a single EST aligned in multiple places, the alignment having the highest base identity was identified. Only alignments having a base identity level within 0.5% of the best and at least 96% base identity with the genomic sequence were kept. Credits This track was produced at UCSC from EST sequence data submitted to the international public sequence databases by scientists worldwide. References Benson DA, Cavanaugh M, Clark K, Karsch-Mizrachi I, Lipman DJ, Ostell J, Sayers EW. GenBank. Nucleic Acids Res. 2013 Jan;41(Database issue):D36-42. PMID: 23193287; PMC: PMC3531190 Benson DA, Karsch-Mizrachi I, Lipman DJ, Ostell J, Wheeler DL. GenBank: update. Nucleic Acids Res. 2004 Jan 1;32(Database issue):D23-6. PMID: 14681350; PMC: PMC308779 Kent WJ. BLAT - the BLAST-like alignment tool. Genome Res. 2002 Apr;12(4):656-64. PMID: 11932250; PMC: PMC187518 
mrna Chicken mRNAs Chicken mRNAs from GenBank mRNA and EST Description The mRNA track shows alignments between chicken mRNAs in GenBank and the genome. Display Conventions and Configuration This track follows the display conventions for PSL alignment tracks. In dense display mode, the items that are more darkly shaded indicate matches of better quality. The description page for this track has a filter that can be used to change the display mode, alter the color, and include/exclude a subset of items within the track. This may be helpful when many items are shown in the track display, especially when only some are relevant to the current task. To use the filter: Type a term in one or more of the text boxes to filter the mRNA display. For example, to apply the filter to all mRNAs expressed in a specific organ, type the name of the organ in the tissue box. To view the list of valid terms for each text box, consult the table in the Table Browser that corresponds to the factor on which you wish to filter. For example, the &quot;tissue&quot; table contains all the types of tissues that can be entered into the tissue text box. Multiple terms may be entered at once, separated by a space. Wildcards may also be used in the filter. If filtering on more than one value, choose the desired combination logic. If &quot;and&quot; is selected, only mRNAs that match all filter criteria will be highlighted. If &quot;or&quot; is selected, mRNAs that match any one of the filter criteria will be highlighted. Choose the color or display characteristic that should be used to highlight or include/exclude the filtered items. If &quot;exclude&quot; is chosen, the browser will not display mRNAs that match the filter criteria. If &quot;include&quot; is selected, the browser will display only those mRNAs that match the filter criteria. This track may also be configured to display codon coloring, a feature that allows the user to quickly compare mRNAs against the genomic sequence. For more information about this option, go to the Codon and Base Coloring for Alignment Tracks page. Several types of alignment gap may also be colored; for more information, go to the Alignment Insertion/Deletion Display Options page. Methods GenBank chicken mRNAs were aligned against the genome using the blat program. When a single mRNA aligned in multiple places, the alignment having the highest base identity was found. Only alignments having a base identity level within 0.5% of the best and at least 96% base identity with the genomic sequence were kept. Credits The mRNA track was produced at UCSC from mRNA sequence data submitted to the international public sequence databases by scientists worldwide. References Benson DA, Cavanaugh M, Clark K, Karsch-Mizrachi I, Lipman DJ, Ostell J, Sayers EW. GenBank. Nucleic Acids Res. 2013 Jan;41(Database issue):D36-42. PMID: 23193287; PMC: PMC3531190 Benson DA, Karsch-Mizrachi I, Lipman DJ, Ostell J, Wheeler DL. GenBank: update. Nucleic Acids Res. 2004 Jan 1;32(Database issue):D23-6. PMID: 14681350; PMC: PMC308779 Kent WJ. BLAT - the BLAST-like alignment tool. Genome Res. 2002 Apr;12(4):656-64. PMID: 11932250; PMC: PMC187518 
cytoBandIdeo Chromosome Band (Ideogram) Ideogram for Orientation Mapping and Sequencing 
crispr10KRanges CRISPR Regions 10K Genome regions processed to find CRISPR/Cas9 target sites (exons +/- 10,000 bp) Genes and Gene Predictions Description This track shows regions of the genome within 10,000 bp of transcribed regions and DNA sequences targetable by CRISPR RNA guides using the Cas9 enzyme from S. pyogenes (PAM: NGG). CRISPR target sites were annotated with predicted specificity (off-target effects) and predicted efficiency (on-target cleavage) by various algorithms through the tool CRISPOR. Display Conventions and Configuration The track &quot;CRISPR Regions&quot; shows the regions of the genome where target sites were analyzed, i.e. within 10,000 bp of transcribed regions as annotated by Ensembl transcript models. The track &quot;CRISPR Targets&quot; shows the target sites in these regions. The target sequence of the guide is shown with a thick (exon) bar. The PAM motif match (NGG) is shown with a thinner bar. Guides are colored to reflect both predicted specificity and efficiency. Specificity reflects the &quot;uniqueness&quot; of a 20mer sequence in the genome; the less unique a sequence is, the more likely it is to cleave other locations of the genome (off-target effects). Efficiency is the frequency of cleavage at the target site (on-target efficiency). Shades of gray stand for sites that are hard to target specifically, as the 20mer is not very unique in the genome: impossible to target: target site has at least one identical copy in the genome and was not scored hard to target: many similar sequences in the genome that alignment stopped, repeat? hard to target: target site was aligned but results in a low specificity score &lt;= 50 (see below) Colors highlight targets that are specific in the genome (MIT specificity &gt; 50) but have different predicted efficiencies: unable to calculate Doench/Fusi 2016 efficiency score low predicted cleavage: Doench/Fusi 2016 Efficiency percentile &lt;= 30 medium predicted cleavage: Doench/Fusi 2016 Efficiency percentile &gt; 30 and &lt; 55 high predicted cleavage: Doench/Fusi 2016 Efficiency &gt; 55 Mouse-over a target site to show predicted specificity and efficiency scores: The MIT Specificity score summarizes all off-targets into a single number from 0-100. The higher the number, the fewer off-target effects are expected. We recommend guides with an MIT specificity &gt; 50. The efficiency score tries to predict if a guide leads to rather strong or weak cleavage. According to (Haeussler et al. 2016), the Doench 2016 Efficiency score should be used to select the guide with the highest cleavage efficiency when expressing guides from RNA PolIII Promoters such as U6. Scores are given as percentiles, e.g. &quot;70%&quot; means that 70% of mammalian guides have a score equal or lower than this guide. The raw score number is also shown in parentheses after the percentile. The Moreno-Mateos 2015 Efficiency score should be used instead of the Doench 2016 score when transcribing the guide in vitro with a T7 promoter, e.g. for injections in mouse, zebrafish or Xenopus embryos. The Moreno-Mateos score is given in percentiles and the raw value in parentheses, see the note above. Click onto features to show all scores and predicted off-targets with up to four mismatches. The Out-of-Frame score by Bae et al. 2014 is correlated with the probability that mutations induced by the guide RNA will disrupt the open reading frame. The authors recommend out-of-frame scores &gt; 66 to create knock-outs with a single guide efficiently. Off-target sites are sorted by the CFD score (Doench et al. 2016). The higher the CFD score, the more likely there is off-target cleavage at that site. Off-targets with a CFD score &lt; 0.023 are not shown on this page, but are availble when following the link to the external CRISPOR tool. When compared against experimentally validated off-targets by Haeussler et al. 2016, the large majority of predicted off-targets with CFD scores &lt; 0.023 were false-positives. For storage and performance reasons, on the level of individual off-targets, only CFD scores are available. Methods Relationship between predictions and experimental data Like most algorithms, the MIT specificity score is not always a perfect predictor of off-target effects. Despite low scores, many tested guides caused few and/or weak off-target cleavage when tested with whole-genome assays (Figure 2 from Haeussler et al. 2016), as shown below, and the published data contains few data points with high specificity scores. Overall though, the assays showed that the higher the specificity score, the lower the off-target effects. Similarly, efficiency scoring is not very accurate: guides with low scores can be efficient and vice versa. As a general rule, however, the higher the score, the less likely that a guide is very inefficient. The following histograms illustrate, for each type of score, how the share of inefficient guides drops with increasing efficiency scores: When reading this plot, keep in mind that both scores were evaluated on their own training data. Especially for the Moreno-Mateos score, the results are too optimistic, due to overfitting. When evaluated on independent datasets, the correlation of the prediction with other assays was around 25% lower, see Haeussler et al. 2016. At the time of writing, there is no independent dataset available yet to determine the Moreno-Mateos accuracy for each score percentile range. Track methods Exons as predicted by Ensembl Gene models were used, extended by 10,000 basepairs on each side, searched for the -NGG motif. Flanking 20mer guide sequences were aligned to the genome with BWA and scored with MIT Specificity scores using the command-line version of crispor.org. Non-unique guide sequences were skipped. Flanking sequences were extracted from the genome and input for Crispor efficiency scoring, available from the Crispor downloads page, which includes the Doench 2016, Moreno-Mateos 2015 and Bae 2014 algorithms, among others. Note that the Doench 2016 scores were updated by the Broad institute in 2017 ("Azimuth" update). As a result, earlier versions of the track show the old Doench 2016 scores and the 10k version of the track shows new Doench 2016 scores. Old and new scores are almost identical, they are correlated to 0.99 and for more than 80% of the guides the difference is below 0.02. However, for very few guides, the difference can be bigger. In case of doubt, we recommend the new scores. Crispor.org can display both scores and many more with the "Show all scores" link. Data Access The raw data can be explored interactively with the Table Browser. For automated analysis, the genome annotation is stored in a bigBed file that can be downloaded from our download server. The files for this track are called crispr.bb and crisprDetails.tab. Individual regions or the whole genome annotation can be obtained using our tool bigBedToBed, which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tool can also be used to obtain only features within a given range, e.g. bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/galGal6/crispr10K/crispr.bb -chrom=chr21 -start=0 -end=1000000 stdout Credits Track created by Maximilian Haeussler, with helpful input from Jean-Paul Concordet (MNHN Paris) and Alberto Stolfi (NYU). This track was built with financial support from Regeneron, Inc. References Haeussler M, Sch&#246;nig K, Eckert H, Eschstruth A, Miann&#233; J, Renaud JB, Schneider-Maunoury S, Shkumatava A, Teboul L, Kent J et al. Evaluation of off-target and on-target scoring algorithms and integration into the guide RNA selection tool CRISPOR. Genome Biol. 2016 Jul 5;17(1):148. PMID: 27380939; PMC: PMC4934014 Bae S, Kweon J, Kim HS, Kim JS. Microhomology-based choice of Cas9 nuclease target sites. Nat Methods. 2014 Jul;11(7):705-6. PMID: 24972169 Doench JG, Fusi N, Sullender M, Hegde M, Vaimberg EW, Donovan KF, Smith I, Tothova Z, Wilen C, Orchard R et al. Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nat Biotechnol. 2016 Feb;34(2):184-91. PMID: 26780180; PMC: PMC4744125 Hsu PD, Scott DA, Weinstein JA, Ran FA, Konermann S, Agarwala V, Li Y, Fine EJ, Wu X, Shalem O et al. DNA targeting specificity of RNA-guided Cas9 nucleases. Nat Biotechnol. 2013 Sep;31(9):827-32. PMID: 23873081; PMC: PMC3969858 Moreno-Mateos MA, Vejnar CE, Beaudoin JD, Fernandez JP, Mis EK, Khokha MK, Giraldez AJ. CRISPRscan: designing highly efficient sgRNAs for CRISPR-Cas9 targeting in vivo. Nat Methods. 2015 Oct;12(10):982-8. PMID: 26322839; PMC: PMC4589495 
crispr10K CRISPR 10K CRISPR/Cas9 Sp. Pyog. target sites (exons +/- 10,000 bp) Genes and Gene Predictions Description This track shows regions of the genome within 10,000 bp of transcribed regions and DNA sequences targetable by CRISPR RNA guides using the Cas9 enzyme from S. pyogenes (PAM: NGG). CRISPR target sites were annotated with predicted specificity (off-target effects) and predicted efficiency (on-target cleavage) by various algorithms through the tool CRISPOR. Display Conventions and Configuration The track &quot;CRISPR Regions&quot; shows the regions of the genome where target sites were analyzed, i.e. within 10,000 bp of transcribed regions as annotated by Ensembl transcript models. The track &quot;CRISPR Targets&quot; shows the target sites in these regions. The target sequence of the guide is shown with a thick (exon) bar. The PAM motif match (NGG) is shown with a thinner bar. Guides are colored to reflect both predicted specificity and efficiency. Specificity reflects the &quot;uniqueness&quot; of a 20mer sequence in the genome; the less unique a sequence is, the more likely it is to cleave other locations of the genome (off-target effects). Efficiency is the frequency of cleavage at the target site (on-target efficiency). Shades of gray stand for sites that are hard to target specifically, as the 20mer is not very unique in the genome: impossible to target: target site has at least one identical copy in the genome and was not scored hard to target: many similar sequences in the genome that alignment stopped, repeat? hard to target: target site was aligned but results in a low specificity score &lt;= 50 (see below) Colors highlight targets that are specific in the genome (MIT specificity &gt; 50) but have different predicted efficiencies: unable to calculate Doench/Fusi 2016 efficiency score low predicted cleavage: Doench/Fusi 2016 Efficiency percentile &lt;= 30 medium predicted cleavage: Doench/Fusi 2016 Efficiency percentile &gt; 30 and &lt; 55 high predicted cleavage: Doench/Fusi 2016 Efficiency &gt; 55 Mouse-over a target site to show predicted specificity and efficiency scores: The MIT Specificity score summarizes all off-targets into a single number from 0-100. The higher the number, the fewer off-target effects are expected. We recommend guides with an MIT specificity &gt; 50. The efficiency score tries to predict if a guide leads to rather strong or weak cleavage. According to (Haeussler et al. 2016), the Doench 2016 Efficiency score should be used to select the guide with the highest cleavage efficiency when expressing guides from RNA PolIII Promoters such as U6. Scores are given as percentiles, e.g. &quot;70%&quot; means that 70% of mammalian guides have a score equal or lower than this guide. The raw score number is also shown in parentheses after the percentile. The Moreno-Mateos 2015 Efficiency score should be used instead of the Doench 2016 score when transcribing the guide in vitro with a T7 promoter, e.g. for injections in mouse, zebrafish or Xenopus embryos. The Moreno-Mateos score is given in percentiles and the raw value in parentheses, see the note above. Click onto features to show all scores and predicted off-targets with up to four mismatches. The Out-of-Frame score by Bae et al. 2014 is correlated with the probability that mutations induced by the guide RNA will disrupt the open reading frame. The authors recommend out-of-frame scores &gt; 66 to create knock-outs with a single guide efficiently. Off-target sites are sorted by the CFD score (Doench et al. 2016). The higher the CFD score, the more likely there is off-target cleavage at that site. Off-targets with a CFD score &lt; 0.023 are not shown on this page, but are availble when following the link to the external CRISPOR tool. When compared against experimentally validated off-targets by Haeussler et al. 2016, the large majority of predicted off-targets with CFD scores &lt; 0.023 were false-positives. For storage and performance reasons, on the level of individual off-targets, only CFD scores are available. Methods Relationship between predictions and experimental data Like most algorithms, the MIT specificity score is not always a perfect predictor of off-target effects. Despite low scores, many tested guides caused few and/or weak off-target cleavage when tested with whole-genome assays (Figure 2 from Haeussler et al. 2016), as shown below, and the published data contains few data points with high specificity scores. Overall though, the assays showed that the higher the specificity score, the lower the off-target effects. Similarly, efficiency scoring is not very accurate: guides with low scores can be efficient and vice versa. As a general rule, however, the higher the score, the less likely that a guide is very inefficient. The following histograms illustrate, for each type of score, how the share of inefficient guides drops with increasing efficiency scores: When reading this plot, keep in mind that both scores were evaluated on their own training data. Especially for the Moreno-Mateos score, the results are too optimistic, due to overfitting. When evaluated on independent datasets, the correlation of the prediction with other assays was around 25% lower, see Haeussler et al. 2016. At the time of writing, there is no independent dataset available yet to determine the Moreno-Mateos accuracy for each score percentile range. Track methods Exons as predicted by Ensembl Gene models were used, extended by 10,000 basepairs on each side, searched for the -NGG motif. Flanking 20mer guide sequences were aligned to the genome with BWA and scored with MIT Specificity scores using the command-line version of crispor.org. Non-unique guide sequences were skipped. Flanking sequences were extracted from the genome and input for Crispor efficiency scoring, available from the Crispor downloads page, which includes the Doench 2016, Moreno-Mateos 2015 and Bae 2014 algorithms, among others. Note that the Doench 2016 scores were updated by the Broad institute in 2017 ("Azimuth" update). As a result, earlier versions of the track show the old Doench 2016 scores and the 10k version of the track shows new Doench 2016 scores. Old and new scores are almost identical, they are correlated to 0.99 and for more than 80% of the guides the difference is below 0.02. However, for very few guides, the difference can be bigger. In case of doubt, we recommend the new scores. Crispor.org can display both scores and many more with the "Show all scores" link. Data Access The raw data can be explored interactively with the Table Browser. For automated analysis, the genome annotation is stored in a bigBed file that can be downloaded from our download server. The files for this track are called crispr.bb and crisprDetails.tab. Individual regions or the whole genome annotation can be obtained using our tool bigBedToBed, which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tool can also be used to obtain only features within a given range, e.g. bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/galGal6/crispr10K/crispr.bb -chrom=chr21 -start=0 -end=1000000 stdout Credits Track created by Maximilian Haeussler, with helpful input from Jean-Paul Concordet (MNHN Paris) and Alberto Stolfi (NYU). This track was built with financial support from Regeneron, Inc. References Haeussler M, Sch&#246;nig K, Eckert H, Eschstruth A, Miann&#233; J, Renaud JB, Schneider-Maunoury S, Shkumatava A, Teboul L, Kent J et al. Evaluation of off-target and on-target scoring algorithms and integration into the guide RNA selection tool CRISPOR. Genome Biol. 2016 Jul 5;17(1):148. PMID: 27380939; PMC: PMC4934014 Bae S, Kweon J, Kim HS, Kim JS. Microhomology-based choice of Cas9 nuclease target sites. Nat Methods. 2014 Jul;11(7):705-6. PMID: 24972169 Doench JG, Fusi N, Sullender M, Hegde M, Vaimberg EW, Donovan KF, Smith I, Tothova Z, Wilen C, Orchard R et al. Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nat Biotechnol. 2016 Feb;34(2):184-91. PMID: 26780180; PMC: PMC4744125 Hsu PD, Scott DA, Weinstein JA, Ran FA, Konermann S, Agarwala V, Li Y, Fine EJ, Wu X, Shalem O et al. DNA targeting specificity of RNA-guided Cas9 nucleases. Nat Biotechnol. 2013 Sep;31(9):827-32. PMID: 23873081; PMC: PMC3969858 Moreno-Mateos MA, Vejnar CE, Beaudoin JD, Fernandez JP, Mis EK, Khokha MK, Giraldez AJ. CRISPRscan: designing highly efficient sgRNAs for CRISPR-Cas9 targeting in vivo. Nat Methods. 2015 Oct;12(10):982-8. PMID: 26322839; PMC: PMC4589495 
crispr10KTargets CRISPR Targets 10K CRISPR/Cas9 -NGG Targets (exons +/- 10,000 bp) Genes and Gene Predictions Description This track shows regions of the genome within 10,000 bp of transcribed regions and DNA sequences targetable by CRISPR RNA guides using the Cas9 enzyme from S. pyogenes (PAM: NGG). CRISPR target sites were annotated with predicted specificity (off-target effects) and predicted efficiency (on-target cleavage) by various algorithms through the tool CRISPOR. Display Conventions and Configuration The track &quot;CRISPR Regions&quot; shows the regions of the genome where target sites were analyzed, i.e. within 10,000 bp of transcribed regions as annotated by Ensembl transcript models. The track &quot;CRISPR Targets&quot; shows the target sites in these regions. The target sequence of the guide is shown with a thick (exon) bar. The PAM motif match (NGG) is shown with a thinner bar. Guides are colored to reflect both predicted specificity and efficiency. Specificity reflects the &quot;uniqueness&quot; of a 20mer sequence in the genome; the less unique a sequence is, the more likely it is to cleave other locations of the genome (off-target effects). Efficiency is the frequency of cleavage at the target site (on-target efficiency). Shades of gray stand for sites that are hard to target specifically, as the 20mer is not very unique in the genome: impossible to target: target site has at least one identical copy in the genome and was not scored hard to target: many similar sequences in the genome that alignment stopped, repeat? hard to target: target site was aligned but results in a low specificity score &lt;= 50 (see below) Colors highlight targets that are specific in the genome (MIT specificity &gt; 50) but have different predicted efficiencies: unable to calculate Doench/Fusi 2016 efficiency score low predicted cleavage: Doench/Fusi 2016 Efficiency percentile &lt;= 30 medium predicted cleavage: Doench/Fusi 2016 Efficiency percentile &gt; 30 and &lt; 55 high predicted cleavage: Doench/Fusi 2016 Efficiency &gt; 55 Mouse-over a target site to show predicted specificity and efficiency scores: The MIT Specificity score summarizes all off-targets into a single number from 0-100. The higher the number, the fewer off-target effects are expected. We recommend guides with an MIT specificity &gt; 50. The efficiency score tries to predict if a guide leads to rather strong or weak cleavage. According to (Haeussler et al. 2016), the Doench 2016 Efficiency score should be used to select the guide with the highest cleavage efficiency when expressing guides from RNA PolIII Promoters such as U6. Scores are given as percentiles, e.g. &quot;70%&quot; means that 70% of mammalian guides have a score equal or lower than this guide. The raw score number is also shown in parentheses after the percentile. The Moreno-Mateos 2015 Efficiency score should be used instead of the Doench 2016 score when transcribing the guide in vitro with a T7 promoter, e.g. for injections in mouse, zebrafish or Xenopus embryos. The Moreno-Mateos score is given in percentiles and the raw value in parentheses, see the note above. Click onto features to show all scores and predicted off-targets with up to four mismatches. The Out-of-Frame score by Bae et al. 2014 is correlated with the probability that mutations induced by the guide RNA will disrupt the open reading frame. The authors recommend out-of-frame scores &gt; 66 to create knock-outs with a single guide efficiently. Off-target sites are sorted by the CFD score (Doench et al. 2016). The higher the CFD score, the more likely there is off-target cleavage at that site. Off-targets with a CFD score &lt; 0.023 are not shown on this page, but are availble when following the link to the external CRISPOR tool. When compared against experimentally validated off-targets by Haeussler et al. 2016, the large majority of predicted off-targets with CFD scores &lt; 0.023 were false-positives. For storage and performance reasons, on the level of individual off-targets, only CFD scores are available. Methods Relationship between predictions and experimental data Like most algorithms, the MIT specificity score is not always a perfect predictor of off-target effects. Despite low scores, many tested guides caused few and/or weak off-target cleavage when tested with whole-genome assays (Figure 2 from Haeussler et al. 2016), as shown below, and the published data contains few data points with high specificity scores. Overall though, the assays showed that the higher the specificity score, the lower the off-target effects. Similarly, efficiency scoring is not very accurate: guides with low scores can be efficient and vice versa. As a general rule, however, the higher the score, the less likely that a guide is very inefficient. The following histograms illustrate, for each type of score, how the share of inefficient guides drops with increasing efficiency scores: When reading this plot, keep in mind that both scores were evaluated on their own training data. Especially for the Moreno-Mateos score, the results are too optimistic, due to overfitting. When evaluated on independent datasets, the correlation of the prediction with other assays was around 25% lower, see Haeussler et al. 2016. At the time of writing, there is no independent dataset available yet to determine the Moreno-Mateos accuracy for each score percentile range. Track methods Exons as predicted by Ensembl Gene models were used, extended by 10,000 basepairs on each side, searched for the -NGG motif. Flanking 20mer guide sequences were aligned to the genome with BWA and scored with MIT Specificity scores using the command-line version of crispor.org. Non-unique guide sequences were skipped. Flanking sequences were extracted from the genome and input for Crispor efficiency scoring, available from the Crispor downloads page, which includes the Doench 2016, Moreno-Mateos 2015 and Bae 2014 algorithms, among others. Note that the Doench 2016 scores were updated by the Broad institute in 2017 ("Azimuth" update). As a result, earlier versions of the track show the old Doench 2016 scores and the 10k version of the track shows new Doench 2016 scores. Old and new scores are almost identical, they are correlated to 0.99 and for more than 80% of the guides the difference is below 0.02. However, for very few guides, the difference can be bigger. In case of doubt, we recommend the new scores. Crispor.org can display both scores and many more with the "Show all scores" link. Data Access The raw data can be explored interactively with the Table Browser. For automated analysis, the genome annotation is stored in a bigBed file that can be downloaded from our download server. The files for this track are called crispr.bb and crisprDetails.tab. Individual regions or the whole genome annotation can be obtained using our tool bigBedToBed, which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tool can also be used to obtain only features within a given range, e.g. bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/galGal6/crispr10K/crispr.bb -chrom=chr21 -start=0 -end=1000000 stdout Credits Track created by Maximilian Haeussler, with helpful input from Jean-Paul Concordet (MNHN Paris) and Alberto Stolfi (NYU). This track was built with financial support from Regeneron, Inc. References Haeussler M, Sch&#246;nig K, Eckert H, Eschstruth A, Miann&#233; J, Renaud JB, Schneider-Maunoury S, Shkumatava A, Teboul L, Kent J et al. Evaluation of off-target and on-target scoring algorithms and integration into the guide RNA selection tool CRISPOR. Genome Biol. 2016 Jul 5;17(1):148. PMID: 27380939; PMC: PMC4934014 Bae S, Kweon J, Kim HS, Kim JS. Microhomology-based choice of Cas9 nuclease target sites. Nat Methods. 2014 Jul;11(7):705-6. PMID: 24972169 Doench JG, Fusi N, Sullender M, Hegde M, Vaimberg EW, Donovan KF, Smith I, Tothova Z, Wilen C, Orchard R et al. Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nat Biotechnol. 2016 Feb;34(2):184-91. PMID: 26780180; PMC: PMC4744125 Hsu PD, Scott DA, Weinstein JA, Ran FA, Konermann S, Agarwala V, Li Y, Fine EJ, Wu X, Shalem O et al. DNA targeting specificity of RNA-guided Cas9 nucleases. Nat Biotechnol. 2013 Sep;31(9):827-32. PMID: 23873081; PMC: PMC3969858 Moreno-Mateos MA, Vejnar CE, Beaudoin JD, Fernandez JP, Mis EK, Khokha MK, Giraldez AJ. CRISPRscan: designing highly efficient sgRNAs for CRISPR-Cas9 targeting in vivo. Nat Methods. 2015 Oct;12(10):982-8. PMID: 26322839; PMC: PMC4589495 
ensGene Ensembl Genes Ensembl Genes Genes and Gene Predictions Description These gene predictions were generated by Ensembl. For more information on the different gene tracks, see our Genes FAQ. Methods For a description of the methods used in Ensembl gene predictions, please refer to Hubbard et al. (2002), also listed in the References section below. Data access Ensembl Gene data can be explored interactively using the Table Browser or the Data Integrator. For local downloads, the genePred format files for galGal6 are available in our downloads directory as ensGene.txt.gz or in our genes download directory in GTF format. For programmatic access, the data can be queried from the REST API or directly from our public MySQL servers. Instructions on this method are available on our MySQL help page and on our blog. Previous versions of this track can be found on our archive download server. Credits We would like to thank Ensembl for providing these gene annotations. For more information, please see Ensembl&#39s genome annotation page. References Hubbard T, Barker D, Birney E, Cameron G, Chen Y, Clark L, Cox T, Cuff J, Curwen V, Down T et al. The Ensembl genome database project. Nucleic Acids Res. 2002 Jan 1;30(1):38-41. PMID: 11752248; PMC: PMC99161 
evaSnpContainer EVA SNP Short Genetic Variants from European Variant Archive Variation and Repeats Description These tracks contain mappings of single nucleotide variants and small insertions and deletions (indels) from the European Variation Archive (EVA) for the chicken galGal6 genome. The dbSNP database at NCBI no longer hosts non-human variants. Interpreting and Configuring the Graphical Display Variants are shown as single tick marks at most zoom levels. When viewing the track at or near base-level resolution, the displayed width of the SNP variant corresponds to the width of the variant in the reference sequence. Insertions are indicated by a single tick mark displayed between two nucleotides, single nucleotide polymorphisms are displayed as the width of a single base, and multiple nucleotide variants are represented by a block that spans two or more bases. The display is set to automatically collapse to dense visibility when there are more than 100k variants in the window. When the window size is more than 250k bp, the display is switched to density graph mode. Searching, details, and filtering Navigation to an individual variant can be accomplished by typing or copying the variant identifier (rsID) or the genomic coordinates into the Position/Search box on the Browser. A click on an item in the graphical display displays a page with data about that variant. Data fields include the Reference and Alternate Alleles, the class of the variant as reported by EVA, the source of the data, the amino acid change, if any, and the functional class as determined by UCSC's Variant Annotation Integrator. Variants can be filtered using the track controls to show subsets of the data by either EVA Sequence Ontology (SO) term, UCSC-generated functional effect, or by color, which bins the UCSC functional effects into general classes. Mouse-over Mousing over an item shows the ucscClass, which is the consequence according to the Variant Annotation Integrator, and the aaChange when one is available, which is the change in amino acid in HGVS.p terms. Items may have multiple ucscClasses, which will all be shown in the mouse-over in a comma-separated list. Likewise, multiple HGVS.p terms may be shown for each rsID separated by spaces describing all possible AA changes. Multiple items may appear due to different variant predictions on multiple gene transcripts. For all organisms, the gene models used were the NCBI RefSeq curated when available, if not then ensembl genes, or finally UCSC mappings of RefSeq if neither of the previous models was possible. Track colors Variants are colored according to the most potentially deleterious functional effect prediction according to the Variant Annotation Integrator. Specific bins can be seen in the Methods section below. Color Variant Type Protein-altering variants and splice site variants Synonymous codon variants Non-coding transcript or Untranslated Region (UTR) variants Intergenic and intronic variants Sequence Ontology (SO) Variants are classified by EVA into one of the following sequence ontology terms: substitution &mdash; A single nucleotide in the reference is replaced by another, alternate allele deletion &mdash; One or more nucleotides are deleted. The representation in the database is to display one additional nucleotide in both the Reference field (Ref) and the Alternate Allele field (Alt). E.g. a variant that is a deletion of an A maybe be represented as Ref = GA and Alt = G. insertion &mdash; One or more nucleotides are inserted. The representation in the database is to display one additional nucleotide in both the Reference field (Ref) and the Alternate Allele field (Alt). E.g. a variant that is an insertion of a T may be represented as Ref = G and Alt = GT delins &mdash; Similar to a tandem repeat, in that the runs of Ref and Alt Alleles are of different length, except that there is more than one type of nucleotide, e.g., Ref = CCAAAAACAAAAACA, Alt = ACAAAAAC. multipleNucleotideVariant &mdash; More than one nucleotide is substituted by an equal number of different nucleotides, e.g., Ref = AA, Alt = GC. sequence alteration &mdash; A parent term meant to signify a deviation from another sequence. Can be assigned to variants that have not been characterized yet. Methods Data were downloaded from the European Variation Archive EVA current_ids.vcf.gz files corresponding to the proper assembly. Chromosome names were converted to UCSC-style and the variants passed through the Variant Annotation Integrator to predict consequence. For every organism the NCBI RefSeq curated models were used when available, followed by ensembl genes, and finally UCSC mapping of RefSeq when neither of the previous models were possible. Variants were then colored according to their predicted consequence in the following fashion: Protein-altering variants and splice site variants - exon_loss_variant, frameshift_variant, inframe_deletion, inframe_insertion, initiator_codon_variant, missense_variant, splice_acceptor_variant, splice_donor_variant, splice_region_variant, stop_gained, stop_lost, coding_sequence_variant, transcript_ablation Synonymous codon variants - synonymous_variant, stop_retained_variant Non-coding transcript or Untranslated Region (UTR) variants - 5_prime_UTR_variant, 3_prime_UTR_variant, complex_transcript_variant, non_coding_transcript_exon_variant Intergenic and intronic variants - upstream_gene_variant, downstream_gene_variant, intron_variant, intergenic_variant, NMD_transcript_variant, no_sequence_alteration Sequence Ontology (&quot;SO:&quot;) terms were converted to the variant classes, then the files were converted to BED, and then bigBed format. No functional annotations were provided by the EVA (e.g., missense, nonsense, etc). These were computed using UCSC's Variant Annotation Integrator (Hinrichs, et al., 2016). Amino-acid substitutions for missense variants are based on RefSeq alignments of mRNA transcripts, which do not always match the amino acids predicted from translating the genomic sequence. Therefore, in some instances, the variant and the genomic nucleotide and associated amino acid may be reversed. E.g., a Pro &gt; Arg change from the perspective of the mRNA would be Arg &gt; Pro from the persepective the genomic sequence. Also, in bosTau9, galGal5, rheMac8, danRer10 and danRer11 the mitochondrial sequence was removed or renamed to match UCSC. For complete documentation of the processing of these tracks, see the makedoc corresponding to the version of interest. For example, the EVA Release 6 MakeDoc. Data Access Note: It is not recommended to use LiftOver to convert SNPs between assemblies, and more information about how to convert SNPs between assemblies can be found on the following FAQ entry. The data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. For automated download and analysis, this annotation is stored in a bigBed file that can be downloaded from our download server. Use the corresponding version number for the track of interest, e.g. evaSnp6.bb. Individual regions or the whole genome annotation can be obtained using our tool bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tool can also be used to obtain only features within a given range, e.g. bigBedToBed https://hgdownload.soe.ucsc.edu/gbdb/galGal6/bbi/evaSnp6.bb -chrom=chr21 -start=0 -end=100000000 stdout Credits This track was produced from the European Variation Archive release data. Consequences were predicted using UCSC's Variant Annotation Integrator and NCBI's RefSeq as well as ensembl gene models. References Cezard T, Cunningham F, Hunt SE, Koylass B, Kumar N, Saunders G, Shen A, Silva AF, Tsukanov K, Venkataraman S et al. The European Variation Archive: a FAIR resource of genomic variation for all species. Nucleic Acids Res. 2021 Oct 28:gkab960. doi:10.1093/nar/gkab960. Epub ahead of print. PMID: 34718739. PMID: PMC8728205. Hinrichs AS, Raney BJ, Speir ML, Rhead B, Casper J, Karolchik D, Kuhn RM, Rosenbloom KR, Zweig AS, Haussler D, Kent WJ. UCSC Data Integrator and Variant Annotation Integrator. Bioinformatics. 2016 May 1;32(9):1430-2. PMID: 26740527; PMC: PMC4848401 
evaSnp6 EVA SNP Release 6 Short Genetic Variants from European Variant Archive Release 6 Variation and Repeats Description These tracks contain mappings of single nucleotide variants and small insertions and deletions (indels) from the European Variation Archive (EVA) for the chicken galGal6 genome. The dbSNP database at NCBI no longer hosts non-human variants. Interpreting and Configuring the Graphical Display Variants are shown as single tick marks at most zoom levels. When viewing the track at or near base-level resolution, the displayed width of the SNP variant corresponds to the width of the variant in the reference sequence. Insertions are indicated by a single tick mark displayed between two nucleotides, single nucleotide polymorphisms are displayed as the width of a single base, and multiple nucleotide variants are represented by a block that spans two or more bases. The display is set to automatically collapse to dense visibility when there are more than 100k variants in the window. When the window size is more than 250k bp, the display is switched to density graph mode. Searching, details, and filtering Navigation to an individual variant can be accomplished by typing or copying the variant identifier (rsID) or the genomic coordinates into the Position/Search box on the Browser. A click on an item in the graphical display displays a page with data about that variant. Data fields include the Reference and Alternate Alleles, the class of the variant as reported by EVA, the source of the data, the amino acid change, if any, and the functional class as determined by UCSC's Variant Annotation Integrator. Variants can be filtered using the track controls to show subsets of the data by either EVA Sequence Ontology (SO) term, UCSC-generated functional effect, or by color, which bins the UCSC functional effects into general classes. Mouse-over Mousing over an item shows the ucscClass, which is the consequence according to the Variant Annotation Integrator, and the aaChange when one is available, which is the change in amino acid in HGVS.p terms. Items may have multiple ucscClasses, which will all be shown in the mouse-over in a comma-separated list. Likewise, multiple HGVS.p terms may be shown for each rsID separated by spaces describing all possible AA changes. Multiple items may appear due to different variant predictions on multiple gene transcripts. For all organisms, the gene models used were the NCBI RefSeq curated when available, if not then ensembl genes, or finally UCSC mappings of RefSeq if neither of the previous models was possible. Track colors Variants are colored according to the most potentially deleterious functional effect prediction according to the Variant Annotation Integrator. Specific bins can be seen in the Methods section below. Color Variant Type Protein-altering variants and splice site variants Synonymous codon variants Non-coding transcript or Untranslated Region (UTR) variants Intergenic and intronic variants Sequence Ontology (SO) Variants are classified by EVA into one of the following sequence ontology terms: substitution &mdash; A single nucleotide in the reference is replaced by another, alternate allele deletion &mdash; One or more nucleotides are deleted. The representation in the database is to display one additional nucleotide in both the Reference field (Ref) and the Alternate Allele field (Alt). E.g. a variant that is a deletion of an A maybe be represented as Ref = GA and Alt = G. insertion &mdash; One or more nucleotides are inserted. The representation in the database is to display one additional nucleotide in both the Reference field (Ref) and the Alternate Allele field (Alt). E.g. a variant that is an insertion of a T may be represented as Ref = G and Alt = GT delins &mdash; Similar to a tandem repeat, in that the runs of Ref and Alt Alleles are of different length, except that there is more than one type of nucleotide, e.g., Ref = CCAAAAACAAAAACA, Alt = ACAAAAAC. multipleNucleotideVariant &mdash; More than one nucleotide is substituted by an equal number of different nucleotides, e.g., Ref = AA, Alt = GC. sequence alteration &mdash; A parent term meant to signify a deviation from another sequence. Can be assigned to variants that have not been characterized yet. Methods Data were downloaded from the European Variation Archive EVA current_ids.vcf.gz files corresponding to the proper assembly. Chromosome names were converted to UCSC-style and the variants passed through the Variant Annotation Integrator to predict consequence. For every organism the NCBI RefSeq curated models were used when available, followed by ensembl genes, and finally UCSC mapping of RefSeq when neither of the previous models were possible. Variants were then colored according to their predicted consequence in the following fashion: Protein-altering variants and splice site variants - exon_loss_variant, frameshift_variant, inframe_deletion, inframe_insertion, initiator_codon_variant, missense_variant, splice_acceptor_variant, splice_donor_variant, splice_region_variant, stop_gained, stop_lost, coding_sequence_variant, transcript_ablation Synonymous codon variants - synonymous_variant, stop_retained_variant Non-coding transcript or Untranslated Region (UTR) variants - 5_prime_UTR_variant, 3_prime_UTR_variant, complex_transcript_variant, non_coding_transcript_exon_variant Intergenic and intronic variants - upstream_gene_variant, downstream_gene_variant, intron_variant, intergenic_variant, NMD_transcript_variant, no_sequence_alteration Sequence Ontology (&quot;SO:&quot;) terms were converted to the variant classes, then the files were converted to BED, and then bigBed format. No functional annotations were provided by the EVA (e.g., missense, nonsense, etc). These were computed using UCSC's Variant Annotation Integrator (Hinrichs, et al., 2016). Amino-acid substitutions for missense variants are based on RefSeq alignments of mRNA transcripts, which do not always match the amino acids predicted from translating the genomic sequence. Therefore, in some instances, the variant and the genomic nucleotide and associated amino acid may be reversed. E.g., a Pro &gt; Arg change from the perspective of the mRNA would be Arg &gt; Pro from the persepective the genomic sequence. Also, in bosTau9, galGal5, rheMac8, danRer10 and danRer11 the mitochondrial sequence was removed or renamed to match UCSC. For complete documentation of the processing of these tracks, see the makedoc corresponding to the version of interest. For example, the EVA Release 6 MakeDoc. Data Access Note: It is not recommended to use LiftOver to convert SNPs between assemblies, and more information about how to convert SNPs between assemblies can be found on the following FAQ entry. The data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. For automated download and analysis, this annotation is stored in a bigBed file that can be downloaded from our download server. Use the corresponding version number for the track of interest, e.g. evaSnp6.bb. Individual regions or the whole genome annotation can be obtained using our tool bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tool can also be used to obtain only features within a given range, e.g. bigBedToBed https://hgdownload.soe.ucsc.edu/gbdb/galGal6/bbi/evaSnp6.bb -chrom=chr21 -start=0 -end=100000000 stdout Credits This track was produced from the European Variation Archive release data. Consequences were predicted using UCSC's Variant Annotation Integrator and NCBI's RefSeq as well as ensembl gene models. References Cezard T, Cunningham F, Hunt SE, Koylass B, Kumar N, Saunders G, Shen A, Silva AF, Tsukanov K, Venkataraman S et al. The European Variation Archive: a FAIR resource of genomic variation for all species. Nucleic Acids Res. 2021 Oct 28:gkab960. doi:10.1093/nar/gkab960. Epub ahead of print. PMID: 34718739. PMID: PMC8728205. Hinrichs AS, Raney BJ, Speir ML, Rhead B, Casper J, Karolchik D, Kuhn RM, Rosenbloom KR, Zweig AS, Haussler D, Kent WJ. UCSC Data Integrator and Variant Annotation Integrator. Bioinformatics. 2016 May 1;32(9):1430-2. PMID: 26740527; PMC: PMC4848401 
evaSnp5 EVA SNP Release 5 Short Genetic Variants from European Variant Archive Release 5 Variation and Repeats Description This track contains mappings of single nucleotide variants and small insertions and deletions (indels) from the European Variation Archive (EVA) Release 5 for the chicken galGal6 genome. The dbSNP database at NCBI no longer hosts non-human variants. Interpreting and Configuring the Graphical Display Variants are shown as single tick marks at most zoom levels. When viewing the track at or near base-level resolution, the displayed width of the SNP variant corresponds to the width of the variant in the reference sequence. Insertions are indicated by a single tick mark displayed between two nucleotides, single nucleotide polymorphisms are displayed as the width of a single base, and multiple nucleotide variants are represented by a block that spans two or more bases. The display is set to automatically collapse to dense visibility when there are more than 100k variants in the window. When the window size is more than 250k bp, the display is switched to density graph mode. Searching, details, and filtering Navigation to an individual variant can be accomplished by typing or copying the variant identifier (rsID) or the genomic coordinates into the Position/Search box on the Browser. A click on an item in the graphical display displays a page with data about that variant. Data fields include the Reference and Alternate Alleles, the class of the variant as reported by EVA, the source of the data, the amino acid change, if any, and the functional class as determined by UCSC's Variant Annotation Integrator. Variants can be filtered using the track controls to show subsets of the data by either EVA Sequence Ontology (SO) term, UCSC-generated functional effect, or by color, which bins the UCSC functional effects into general classes. Mouse-over Mousing over an item shows the ucscClass, which is the consequence according to the Variant Annotation Integrator, and the aaChange when one is available, which is the change in amino acid in HGVS.p terms. Items may have multiple ucscClasses, which will all be shown in the mouse-over in a comma-separated list. Likewise, multiple HGVS.p terms may be shown for each rsID separated by spaces describing all possible AA changes. Multiple items may appear due to different variant predictions on multiple gene transcripts. For all organisms the gene models used were the NCBI RefSeq curated when available, if not then ensembl genes, or finally UCSC mappings of RefSeq if neither of the previous models was possible. Track colors Variants are colored according to the most potentially deleterious functional effect prediction according to the Variant Annotation Integrator. Specific bins can be seen in the Methods section below. Color Variant Type Protein-altering variants and splice site variants Synonymous codon variants Non-coding transcript or Untranslated Region (UTR) variants Intergenic and intronic variants Sequence ontology (SO) Variants are classified by EVA into one of the following sequence ontology terms: substitution &mdash; A single nucleotide in the reference is replaced by another, alternate allele deletion &mdash; One or more nucleotides is deleted. The representation in the database is to display one additional nucleotide in both the Reference field (Ref) and the Alternate Allele field (Alt). E.g. a variant that is a deletion of an A maybe be represented as Ref = GA and Alt = G. insertion &mdash; One or more nucleotides is inserted. The representation in the database is to display one additional nucleotide in both the Reference field (Ref) and the Alternate Allele field (Alt). E.g. a variant that is an insertion of a T maybe be represented as Ref = G and Alt = GT delins &mdash; Similar to tandemRepeat, in that the runs of Ref and Alt Alleles are of different length, except that there is more than one type of nucleotide, e.g., Ref = CCAAAAACAAAAACA, Alt = ACAAAAAC. multipleNucleotideVariant &mdash; More than one nucleotide is substituted by an equal number of different nucleotides, e.g., Ref = AA, Alt = GC. sequence alteration &mdash; A parent term meant to signify a deviation from another sequence. Can be assigned to variants that have not been characterized yet. Methods Data were downloaded from the European Variation Archive EVA release 5 (2023-9-7) current_ids.vcf.gz files corresponding to the proper assembly. Chromosome names were converted to UCSC-style and the variants passed through the Variant Annotation Integrator to predict consequence. For every organism the NCBI RefSeq curated models were used when available, followed by ensembl genes, and finally UCSC mapping of RefSeq when neither of the previous models were possible. Variants were then colored according to their predicted consequence in the following fashion: Protein-altering variants and splice site variants - exon_loss_variant, frameshift_variant, inframe_deletion, inframe_insertion, initiator_codon_variant, missense_variant, splice_acceptor_variant, splice_donor_variant, splice_region_variant, stop_gained, stop_lost, coding_sequence_variant, transcript_ablation Synonymous codon variants - synonymous_variant, stop_retained_variant Non-coding transcript or Untranslated Region (UTR) variants - 5_prime_UTR_variant, 3_prime_UTR_variant, complex_transcript_variant, non_coding_transcript_exon_variant Intergenic and intronic variants - upstream_gene_variant, downstream_gene_variant, intron_variant, intergenic_variant, NMD_transcript_variant, no_sequence_alteration Sequence Ontology (&quot;SO:&quot;) terms were converted to the variant classes, then the files were converted to BED, and then bigBed format. No functional annotations were provided by the EVA (e.g., missense, nonsense, etc). These were computed using UCSC's Variant Annotation Integrator (Hinrichs, et al., 2016). Amino-acid substitutions for missense variants are based on RefSeq alignments of mRNA transcripts, which do not always match the amino acids predicted from translating the genomic sequence. Therefore, in some instances, the variant and the genomic nucleotide and associated amino acid may be reversed. E.g., a Pro &gt; Arg change from the perspective of the mRNA would be Arg &gt; Pro from the persepective the genomic sequence. Also, in bosTau9, galGal5, rheMac8, danRer10 and danRer11 the mitochondrial sequence was removed or renamed to match UCSC. For complete documentation of the processing of these tracks, read the EVA Release 5 MakeDoc. Data Access Note: It is not recommeneded to use LiftOver to convert SNPs between assemblies, and more information about how to convert SNPs between assemblies can be found on the following FAQ entry. The data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. For automated download and analysis, this annotation is stored in a bigBed file that can be downloaded from our download server. The file for this track is called evaSnp5.bb. Individual regions or the whole genome annotation can be obtained using our tool bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tool can also be used to obtain only features within a given range, e.g. bigBedToBed https://hgdownload.soe.ucsc.edu/gbdb/galGal6/bbi/evaSnp5.bb -chrom=chr21 -start=0 -end=100000000 stdout Credits This track was produced from the European Variation Archive release 5 data. Consequences were predicted using UCSC's Variant Annotation Integrator and NCBI's RefSeq as well as ensembl gene models. References Cezard T, Cunningham F, Hunt SE, Koylass B, Kumar N, Saunders G, Shen A, Silva AF, Tsukanov K, Venkataraman S et al. The European Variation Archive: a FAIR resource of genomic variation for all species. Nucleic Acids Res. 2021 Oct 28:gkab960. doi:10.1093/nar/gkab960. Epub ahead of print. PMID: 34718739. PMID: PMC8728205. Hinrichs AS, Raney BJ, Speir ML, Rhead B, Casper J, Karolchik D, Kuhn RM, Rosenbloom KR, Zweig AS, Haussler D, Kent WJ. UCSC Data Integrator and Variant Annotation Integrator. Bioinformatics. 2016 May 1;32(9):1430-2. PMID: 26740527; PMC: PMC4848401 
evaSnp4 EVA SNP Release 4 Short Genetic Variants from European Variant Archive Release 4 Variation and Repeats Description This track contains mappings of single nucleotide variants and small insertions and deletions (indels) from the European Variation Archive (EVA) Release 4 for the chicken galGal6 genome. The dbSNP database at NCBI no longer hosts non-human variants. Interpreting and Configuring the Graphical Display Variants are shown as single tick marks at most zoom levels. When viewing the track at or near base-level resolution, the displayed width of the SNP variant corresponds to the width of the variant in the reference sequence. Insertions are indicated by a single tick mark displayed between two nucleotides, single nucleotide polymorphisms are displayed as the width of a single base, and multiple nucleotide variants are represented by a block that spans two or more bases. The display is set to automatically collapse to dense visibility when there are more than 100k variants in the window. When the window size is more than 250k bp, the display is switched to density graph mode. Searching, details, and filtering Navigation to an individual variant can be accomplished by typing or copying the variant identifier (rsID) or the genomic coordinates into the Position/Search box on the Browser. A click on an item in the graphical display displays a page with data about that variant. Data fields include the Reference and Alternate Alleles, the class of the variant as reported by EVA, the source of the data, the amino acid change, if any, and the functional class as determined by UCSC's Variant Annotation Integrator. Variants can be filtered using the track controls to show subsets of the data by either EVA Sequence Ontology (SO) term, UCSC-generated functional effect, or by color, which bins the UCSC functional effects into general classes. Mouse-over Mousing over an item shows the ucscClass, which is the consequence according to the Variant Annotation Integrator, and the aaChange when one is available, which is the change in amino acid in HGVS.p terms. Items may have multiple ucscClasses, which will all be shown in the mouse-over in a comma-separated list. Likewise, multiple HGVS.p terms may be shown for each rsID separated by spaces describing all possible AA changes. Multiple items may appear due to different variant predictions on multiple gene transcripts. For all organisms the gene models used were the NCBI RefSeq curated when available, if not then ensembl genes, or finally UCSC mappings of RefSeq if neither of the previous models was possible. Track colors Variants are colored according to the most potentially deleterious functional effect prediction according to the Variant Annotation Integrator. Specific bins can be seen in the Methods section below. Color Variant Type Protein-altering variants and splice site variants Synonymous codon variants Non-coding transcript or Untranslated Region (UTR) variants Intergenic and intronic variants Sequence ontology (SO) Variants are classified by EVA into one of the following sequence ontology terms: substitution &mdash; A single nucleotide in the reference is replaced by another, alternate allele deletion &mdash; One or more nucleotides is deleted. The representation in the database is to display one additional nucleotide in both the Reference field (Ref) and the Alternate Allele field (Alt). E.g. a variant that is a deletion of an A maybe be represented as Ref = GA and Alt = G. insertion &mdash; One or more nucleotides is inserted. The representation in the database is to display one additional nucleotide in both the Reference field (Ref) and the Alternate Allele field (Alt). E.g. a variant that is an insertion of a T maybe be represented as Ref = G and Alt = GT delins &mdash; Similar to tandemRepeat, in that the runs of Ref and Alt Alleles are of different length, except that there is more than one type of nucleotide, e.g., Ref = CCAAAAACAAAAACA, Alt = ACAAAAAC. multipleNucleotideVariant &mdash; More than one nucleotide is substituted by an equal number of different nucleotides, e.g., Ref = AA, Alt = GC. sequence alteration &mdash; A parent term meant to signify a deviation from another sequence. Can be assigned to variants that have not been characterized yet. Methods Data were downloaded from the European Variation Archive EVA release 4 (2022-11-21) current_ids.vcf.gz files corresponding to the proper assembly. Chromosome names were converted to UCSC-style and the variants passed through the Variant Annotation Integrator to predict consequence. For every organism the NCBI RefSeq curated models were used when available, followed by ensembl genes, and finally UCSC mapping of RefSeq when neither of the previous models were possible. Variants were then colored according to their predicted consequence in the following fashion: Protein-altering variants and splice site variants - exon_loss_variant, frameshift_variant, inframe_deletion, inframe_insertion, initiator_codon_variant, missense_variant, splice_acceptor_variant, splice_donor_variant, splice_region_variant, stop_gained, stop_lost, coding_sequence_variant, transcript_ablation Synonymous codon variants - synonymous_variant, stop_retained_variant Non-coding transcript or Untranslated Region (UTR) variants - 5_prime_UTR_variant, 3_prime_UTR_variant, complex_transcript_variant, non_coding_transcript_exon_variant Intergenic and intronic variants - upstream_gene_variant, downstream_gene_variant, intron_variant, intergenic_variant, NMD_transcript_variant, no_sequence_alteration Sequence Ontology (&quot;SO:&quot;) terms were converted to the variant classes, then the files were converted to BED, and then bigBed format. No functional annotations were provided by the EVA (e.g., missense, nonsense, etc). These were computed using UCSC's Variant Annotation Integrator (Hinrichs, et al., 2016). Amino-acid substitutions for missense variants are based on RefSeq alignments of mRNA transcripts, which do not always match the amino acids predicted from translating the genomic sequence. Therefore, in some instances, the variant and the genomic nucleotide and associated amino acid may be reversed. E.g., a Pro &gt; Arg change from the perspective of the mRNA would be Arg &gt; Pro from the persepective the genomic sequence. Also, in bosTau9, galGal5, rheMac8, danRer10 and danRer11 the mitochondrial sequence was removed or renamed to match UCSC. For complete documentation of the processing of these tracks, read the EVA Release 4 MakeDoc. Data Access Note: It is not recommeneded to use LiftOver to convert SNPs between assemblies, and more information about how to convert SNPs between assemblies can be found on the following FAQ entry. The data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. For automated download and analysis, this annotation is stored in a bigBed file that can be downloaded from our download server. The file for this track is called evaSnp4.bb. Individual regions or the whole genome annotation can be obtained using our tool bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tool can also be used to obtain only features within a given range, e.g. bigBedToBed https://hgdownload.soe.ucsc.edu/gbdb/galGal6/bbi/evaSnp4.bb -chrom=chr21 -start=0 -end=100000000 stdout Credits This track was produced from the European Variation Archive release 4 data. Consequences were predicted using UCSC's Variant Annotation Integrator and NCBI's RefSeq as well as ensembl gene models. References Cezard T, Cunningham F, Hunt SE, Koylass B, Kumar N, Saunders G, Shen A, Silva AF, Tsukanov K, Venkataraman S et al. The European Variation Archive: a FAIR resource of genomic variation for all species. Nucleic Acids Res. 2021 Oct 28:gkab960. doi:10.1093/nar/gkab960. Epub ahead of print. PMID: 34718739. PMID: PMC8728205. Hinrichs AS, Raney BJ, Speir ML, Rhead B, Casper J, Karolchik D, Kuhn RM, Rosenbloom KR, Zweig AS, Haussler D, Kent WJ. UCSC Data Integrator and Variant Annotation Integrator. Bioinformatics. 2016 May 1;32(9):1430-2. PMID: 26740527; PMC: PMC4848401 
evaSnp EVA SNP Release 3 Short Genetic Variants from European Variant Archive Release 3 Variation and Repeats Description This track contains mappings of single nucleotide variants and small insertions and deletions (indels) from the European Variation Archive (EVA) Release 3 for the chicken galGal6 genome. The dbSNP database at NCBI no longer hosts non-human variants. Interpreting and Configuring the Graphical Display Variants are shown as single tick marks at most zoom levels. When viewing the track at or near base-level resolution, the displayed width of the SNP variant corresponds to the width of the variant in the reference sequence. Insertions are indicated by a single tick mark displayed between two nucleotides, single nucleotide polymorphisms are displayed as the width of a single base, and multiple nucleotide variants are represented by a block that spans two or more bases. The display is set to automatically collapse to dense visibility when there are more than 100k variants in the window. When the window size is more than 250k bp, the display is switched to density graph mode. Searching, details, and filtering Navigation to an individual variant can be accomplished by typing or copying the variant identifier (rsID) or the genomic coordinates into the Position/Search box on the Browser. A click on an item in the graphical display displays a page with data about that variant. Data fields include the Reference and Alternate Alleles, the class of the variant as reported by EVA, the source of the data, the amino acid change, if any, and the functional class as determined by UCSC's Variant Annotation Integrator. Variants can be filtered using the track controls to show subsets of the data by either EVA Sequence Ontology (SO) term, UCSC-generated functional effect, or by color, which bins the UCSC functional effects into general classes. Mouse-over Mousing over an item shows the ucscClass, which is the consequence according to the Variant Annotation Integrator, and the aaChange when one is available, which is the change in amino acid in HGVS.p terms. Items may have multiple ucscClasses, which will all be shown in the mouse-over in a comma-separated list. Likewise, multiple HGVS.p terms may be shown for each rsID separated by spaces describing all possible AA changes. Multiple items may appear due to different variant predictions on multiple gene transcripts. For all organisms the gene models used were ncbiRefSeqCurated, except for mm39 which used ncbiRefSeqSelect. Track colors Variants are colored according to the most potentially deleterious functional effect prediction according to the Variant Annotation Integrator. Specific bins can be seen in the Methods section below. Color Variant Type Protein-altering variants and splice site variants Synonymous codon variants Non-coding transcript or Untranslated Region (UTR) variants Intergenic and intronic variants Sequence ontology (SO) Variants are classified by EVA into one of the following sequence ontology terms: substitution &mdash; A single nucleotide in the reference is replaced by another, alternate allele deletion &mdash; One or more nucleotides is deleted. The representation in the database is to display one additional nucleotide in both the Reference field (Ref) and the Alternate Allele field (Alt). E.g. a variant that is a deletion of an A maybe be represented as Ref = GA and Alt = G. insertion &mdash; One or more nucleotides is inserted. The representation in the database is to display one additional nucleotide in both the Reference field (Ref) and the Alternate Allele field (Alt). E.g. a variant that is an insertion of a T maybe be represented as Ref = G and Alt = GT delins &mdash; Similar to tandemRepeat, in that the runs of Ref and Alt Alleles are of different length, except that there is more than one type of nucleotide, e.g., Ref = CCAAAAACAAAAACA, Alt = ACAAAAAC. multipleNucleotideVariant &mdash; More than one nucleotide is substituted by an equal number of different nucleotides, e.g., Ref = AA, Alt = GC. sequence alteration &mdash; A parent term meant to signify a deviation from another sequence. Can be assigned to variants that have not been characterized yet. Methods Data were downloaded from the European Variation Archive EVA release 3 (2022-02-24) current_ids.vcf.gz files corresponding to the proper assembly. Chromosome names were converted to UCSC-style, a few problematic variants were removed, and the variants passed through the Variant Annotation Integrator to predict consequence. For every organism the ncbiRefSeqCurated gene models were used to predict the consequences, except for mm39 which used the ncbiRefSeqSelect models. Variants were then colored according to their predicted consequence in the following fashion: Protein-altering variants and splice site variants - exon_loss_variant, frameshift_variant, inframe_deletion, inframe_insertion, initiator_codon_variant, missense_variant, splice_acceptor_variant, splice_donor_variant, splice_region_variant, stop_gained, stop_lost, coding_sequence_variant, transcript_ablation Synonymous codon variants - synonymous_variant, stop_retained_variant Non-coding transcript or Untranslated Region (UTR) variants - 5_prime_UTR_variant, 3_prime_UTR_variant, complex_transcript_variant, non_coding_transcript_exon_variant Intergenic and intronic variants - upstream_gene_variant, downstream_gene_variant, intron_variant, intergenic_variant, NMD_transcript_variant, no_sequence_alteration Sequence Ontology (&quot;SO:&quot;) terms were converted to the variant classes, then the files were converted to BED, and then bigBed format. No functional annotations were provided by the EVA (e.g., missense, nonsense, etc). These were computed using UCSC's Variant Annotation Integrator (Hinrichs, et al., 2016). Amino-acid substitutions for missense variants are based on RefSeq alignments of mRNA transcripts, which do not always match the amino acids predicted from translating the genomic sequence. Therefore, in some instances, the variant and the genomic nucleotide and associated amino acid may be reversed. E.g., a Pro > Arg change from the perspective of the mRNA would be Arg > Pro from the persepective the genomic sequence. For complete documentation of the processing of these tracks, read the EVA Release 3 MakeDoc. Data Access Note: It is not recommeneded to use LiftOver to convert SNPs between assemblies, and more information about how to convert SNPs between assemblies can be found on the following FAQ entry. The data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. For automated download and analysis, this annotation is stored in a bigBed file that can be downloaded from our download server. The file for this track is called evaSnp.bb. Individual regions or the whole genome annotation can be obtained using our tool bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tool can also be used to obtain only features within a given range, e.g. bigBedToBed https://hgdownload.soe.ucsc.edu/gbdb/galGal6/bbi/evaSnp.bb -chrom=chr21 -start=0 -end=100000000 stdout Credits This track was produced from the European Variation Archive release 3 data. Consequences were predicted using UCSC's Variant Annotation Integrator and NCBI's RefSeq gene models. References Cezard T, Cunningham F, Hunt SE, Koylass B, Kumar N, Saunders G, Shen A, Silva AF, Tsukanov K, Venkataraman S et al. The European Variation Archive: a FAIR resource of genomic variation for all species. Nucleic Acids Res. 2021 Oct 28:gkab960. doi:10.1093/nar/gkab960. Epub ahead of print. PMID: 34718739. PMID: PMC8728205. Hinrichs AS, Raney BJ, Speir ML, Rhead B, Casper J, Karolchik D, Kuhn RM, Rosenbloom KR, Zweig AS, Haussler D, Kent WJ. UCSC Data Integrator and Variant Annotation Integrator. Bioinformatics. 2016 May 1;32(9):1430-2. PMID: 26740527; PMC: PMC4848401 
fishChainNet Fish Chain/Net Fish Chain and Net Alignments Comparative Genomics Description Chain Track The chain track shows alignments of chicken (Mar. 2018 (GRCg6a/galGal6)/galGal6) to other genomes using a gap scoring system that allows longer gaps than traditional affine gap scoring systems. It can also tolerate gaps in both chicken and the other genome simultaneously. These &quot;double-sided&quot; gaps can be caused by local inversions and overlapping deletions in both species. The chain track displays boxes joined together by either single or double lines. The boxes represent aligning regions. Single lines indicate gaps that are largely due to a deletion in the chicken assembly or an insertion in the chicken assembly. Double lines represent more complex gaps that involve substantial sequence in both species. This may result from inversions, overlapping deletions, an abundance of local mutation, or an unsequenced gap in one species. In cases where multiple chains align over a particular region of the other genome, the chains with single-lined gaps are often due to processed pseudogenes, while chains with double-lined gaps are more often due to paralogs and unprocessed pseudogenes. In the "pack" and "full" display modes, the individual feature names indicate the chromosome, strand, and location (in thousands) of the match for each matching alignment. Net Track The net track shows the best chicken/other chain for every part of the other genome. It is useful for finding orthologous regions and for studying genome rearrangement. The chicken sequence used in this annotation is from the Mar. 2018 (GRCg6a/galGal6) (galGal6) assembly. Display Conventions and Configuration Multiple species are grouped together in a composite track. In the display and on the configuration page, an effort was made to group them loosely into "clades." These groupings are based on the taxonomic classification at NCBI, using the CommonTree tool. Some organisms may be pulled from a larger group into a subgroup, to emphasize a relationship. For example, members of an Order may be listed together, while other organisms in the same Superorder may be grouped separately under the Superorder name. Chain Track By default, the chains to chromosome-based assemblies are colored based on which chromosome they map to in the aligning organism. To turn off the coloring, check the &quot;off&quot; button next to: Color track based on chromosome. To display only the chains of one chromosome in the aligning organism, enter the name of that chromosome (e.g. chr4) in box next to: Filter by chromosome. Net Track In full display mode, the top-level (level 1) chains are the largest, highest-scoring chains that span this region. In many cases gaps exist in the top-level chain. When possible, these are filled in by other chains that are displayed at level 2. The gaps in level 2 chains may be filled by level 3 chains and so forth. In the graphical display, the boxes represent ungapped alignments; the lines represent gaps. Click on a box to view detailed information about the chain as a whole; click on a line to display information about the gap. The detailed information is useful in determining the cause of the gap or, for lower level chains, the genomic rearrangement. Individual items in the display are categorized as one of four types (other than gap): Top - the best, longest match. Displayed on level 1. Syn - line-ups on the same chromosome as the gap in the level above it. Inv - a line-up on the same chromosome as the gap above it, but in the opposite orientation. NonSyn - a match to a chromosome different from the gap in the level above. Methods Chain track Transposons that have been inserted since the chicken/other split were removed from the assemblies. The abbreviated genomes were aligned with lastz, and the transposons were added back in. The resulting alignments were converted into axt format using the lavToAxt program. The axt alignments were fed into axtChain, which organizes all alignments between a single chicken chromosome and a single chromosome from the other genome into a group and creates a kd-tree out of the gapless subsections (blocks) of the alignments. A dynamic program was then run over the kd-trees to find the maximally scoring chains of these blocks. See also: lastz parameters and other details (e.g., update time) and chain minimum score and gap parameters used in these alignments. Net track Chains were derived from lastz alignments, using the methods described on the chain tracks description pages, and sorted with the highest-scoring chains in the genome ranked first. The program chainNet was then used to place the chains one at a time, trimming them as necessary to fit into sections not already covered by a higher-scoring chain. During this process, a natural hierarchy emerged in which a chain that filled a gap in a higher-scoring chain was placed underneath that chain. The program netSyntenic was used to fill in information about the relationship between higher- and lower-level chains, such as whether a lower-level chain was syntenic or inverted relative to the higher-level chain. The program netClass was then used to fill in how much of the gaps and chains contained Ns (sequencing gaps) in one or both species and how much was filled with transposons inserted before and after the two organisms diverged. Credits Lastz (previously known as blastz) was developed at Pennsylvania State University by Minmei Hou, Scott Schwartz, Zheng Zhang, and Webb Miller with advice from Ross Hardison. Lineage-specific repeats were identified by Arian Smit and his RepeatMasker program. The axtChain program was developed at the University of California at Santa Cruz by Jim Kent with advice from Webb Miller and David Haussler. The browser display and database storage of the chains and nets were created by Robert Baertsch and Jim Kent. The chainNet, netSyntenic, and netClass programs were developed at the University of California Santa Cruz by Jim Kent. References Chiaromonte F, Yap VB, Miller W. Scoring pairwise genomic sequence alignments. Pac Symp Biocomput. 2002:115-26. PMID: 11928468 Kent WJ, Baertsch R, Hinrichs A, Miller W, Haussler D. Evolution's cauldron: duplication, deletion, and rearrangement in the mouse and human genomes. Proc Natl Acad Sci U S A. 2003 Sep 30;100(20):11484-9. PMID: 14500911; PMC: PMC208784 Schwartz S, Kent WJ, Smit A, Zhang Z, Baertsch R, Hardison RC, Haussler D, Miller W. Human-mouse alignments with BLASTZ. Genome Res. 2003 Jan;13(1):103-7. PMID: 12529312; PMC: PMC430961 
fishChainNetViewnet Nets Fish Chain and Net Alignments Comparative Genomics 
netFr3 fr3 Net Fugu (Oct. 2011 (FUGU5/fr3)) Alignment Net Comparative Genomics 
netTetNig2 Tetraodon Net Tetraodon (Mar. 2007 (Genoscope 8.0/tetNig2)) Alignment Net Comparative Genomics 
netMayZeb1 mayZeb1 Net Zebra mbuna (Mar 2012 (MetZeb1.1/mayZeb1)) Alignment Net Comparative Genomics 
netOryLat2 Medaka Net Medaka (Oct. 2005 (NIG/UT MEDAKA1/oryLat2)) Alignment Net Comparative Genomics 
netGasAcu1 Stickleback Net Stickleback (Feb. 2006 (Broad/gasAcu1)) Alignment Net Comparative Genomics 
netOreNil3 oreNil3 Net Nile tilapia (Nov. 2016 (ASM185804v2/oreNil3)) Alignment Net Comparative Genomics 
netAngJap1 angJap1 Net Japanese eel (18 Mar-2014 (japanese_eel_genome_v1_25_oct_2011_japonica_c401b400k25m200_sspacepremiumk3a02n24_extra.final.scaffolds/angJap1)) Alignment Net Comparative Genomics 
netDanRer11 Zebrafish Net Zebrafish (May 2017 (GRCz11/danRer11)) Alignment Net Comparative Genomics 
fishChainNetViewchain Chains Fish Chain and Net Alignments Comparative Genomics 
chainFr3 fr3 Chain Fugu (Oct. 2011 (FUGU5/fr3)) Chained Alignments Comparative Genomics 
chainTetNig2 Tetraodon Chain Tetraodon (Mar. 2007 (Genoscope 8.0/tetNig2)) Chained Alignments Comparative Genomics 
chainMayZeb1 mayZeb1 Chain Zebra mbuna (Mar 2012 (MetZeb1.1/mayZeb1)) Chained Alignments Comparative Genomics 
chainOryLat2 Medaka Chain Medaka (Oct. 2005 (NIG/UT MEDAKA1/oryLat2)) Chained Alignments Comparative Genomics 
chainGasAcu1 Stickleback Chain Stickleback (Feb. 2006 (Broad/gasAcu1)) Chained Alignments Comparative Genomics 
chainOreNil3 oreNil3 Chain Nile tilapia (Nov. 2016 (ASM185804v2/oreNil3)) Chained Alignments Comparative Genomics 
chainAngJap1 angJap1 Chain Japanese eel (18 Mar-2014 (japanese_eel_genome_v1_25_oct_2011_japonica_c401b400k25m200_sspacepremiumk3a02n24_extra.final.scaffolds/angJap1)) Chained Alignments Comparative Genomics 
chainDanRer11 Zebrafish Chain Zebrafish (May 2017 (GRCz11/danRer11)) Chained Alignments Comparative Genomics 
gap Gap Gap Locations Mapping and Sequencing Description This track shows the gaps in the Mar. 2018 chicken genome assembly. Genome assembly procedures are covered in the NCBI assembly documentation. NCBI also provides specific information about this assembly. The definition of the gaps in this assembly is from the AGP file delivered with the sequence. The NCBI document AGP Specification describes the format of the AGP file. Gaps are represented as black boxes in this track. If the relative order and orientation of the contigs on either side of the gap is supported by read pair data, it is a bridged gap and a white line is drawn through the black box representing the gap. This assembly contains the following principal types of gaps: centromere - gaps for centromeres are included when they can be reasonably localized (count: 18; all of size 500,000 bases) contig - gaps between contigs in scaffolds (count: 50; size range: 10 - 50,000 bases) scaffold - gaps between scaffolds in chromosome assemblies (count: 874; size range: 10 - 74,650 bases) 
gc5BaseBw GC Percent GC Percent in 5-Base Windows Mapping and Sequencing Description The GC percent track shows the percentage of G (guanine) and C (cytosine) bases in 5-base windows. High GC content is typically associated with gene-rich areas. This track may be configured in a variety of ways to highlight different apsects of the displayed information. Click the &quot;Graph configuration help&quot; link for an explanation of the configuration options. Credits The data and presentation of this graph were prepared by Hiram Clawson. 
genscan Genscan Genes Genscan Gene Predictions Genes and Gene Predictions Description This track shows predictions from the Genscan program written by Chris Burge. The predictions are based on transcriptional, translational and donor/acceptor splicing signals as well as the length and compositional distributions of exons, introns and intergenic regions. For more information on the different gene tracks, see our Genes FAQ. Display Conventions and Configuration This track follows the display conventions for gene prediction tracks. The track description page offers the following filter and configuration options: Color track by codons: Select the genomic codons option to color and label each codon in a zoomed-in display to facilitate validation and comparison of gene predictions. Go to the Coloring Gene Predictions and Annotations by Codon page for more information about this feature. Methods For a description of the Genscan program and the model that underlies it, refer to Burge and Karlin (1997) in the References section below. The splice site models used are described in more detail in Burge (1998) below. Credits Thanks to Chris Burge for providing the Genscan program. References Burge C. Modeling Dependencies in Pre-mRNA Splicing Signals. In: Salzberg S, Searls D, Kasif S, editors. Computational Methods in Molecular Biology. Amsterdam: Elsevier Science; 1998. p. 127-163. Burge C, Karlin S. Prediction of complete gene structures in human genomic DNA. J. Mol. Biol. 1997 Apr 25;268(1):78-94. PMID: 9149143 
grcIncidentDb GRC Incident GRC Incident Database Mapping and Sequencing Description This track shows locations in the chicken assembly where assembly problems have been noted or resolved, as reported by the Genome Reference Consortium (GRC). If you would like to report an assembly problem, please use the GRC issue reporting system. Methods Data for this track are extracted from the GRC incident database from the specific species *_issues.gff3 file. The track is synchronized once daily to incorporate new updates. Credits The data and presentation of this track were prepared by Hiram Clawson. 
ucscToINSDC INSDC Accession at INSDC - International Nucleotide Sequence Database Collaboration Mapping and Sequencing Description This track associates UCSC Genome Browser chromosome names to accession names from the International Nucleotide Sequence Database Collaboration (INSDC). The data were downloaded from the NCBI assembly database. Credits The data for this track was prepared by Hiram Clawson. 
nestedRepeats Interrupted Rpts Fragments of Interrupted Repeats Joined by RepeatMasker ID Variation and Repeats Description This track shows joined fragments of interrupted repeats extracted from the output of the RepeatMasker program which screens DNA sequences for interspersed repeats and low complexity DNA sequences using the Repbase Update library of repeats from the Genetic Information Research Institute (GIRI). Repbase Update is described in Jurka (2000) in the References section below. The detailed annotations from RepeatMasker are in the RepeatMasker track. This track shows fragments of original repeat insertions which have been interrupted by insertions of younger repeats or through local rearrangements. The fragments are joined using the ID column of RepeatMasker output. Display Conventions and Configuration In pack or full mode, each interrupted repeat is displayed as boxes (fragments) joined by horizontal lines, labeled with the repeat name. If all fragments are on the same strand, arrows are added to the horizontal line to indicate the strand. In dense or squish mode, labels and arrows are omitted and in dense mode, all items are collapsed to fit on a single row. Items are shaded according to the average identity score of their fragments. Usually, the shade of an item is similar to the shades of its fragments unless some fragments are much more diverged than others. The score displayed above is the average identity score, clipped to a range of 50% - 100% and then mapped to the range 0 - 1000 for shading in the browser. Methods UCSC has used the most current versions of the RepeatMasker software and repeat libraries available to generate these data. Note that these versions may be newer than those that are publicly available on the Internet. Data are generated using the RepeatMasker -s flag. Additional flags may be used for certain organisms. See the FAQ for more information. Credits Thanks to Arian Smit, Robert Hubley and GIRI for providing the tools and repeat libraries used to generate this track. References Smit AFA, Hubley R, Green P. RepeatMasker Open-3.0. http://www.repeatmasker.org. 1996-2010. Repbase Update is described in: Jurka J. Repbase Update: a database and an electronic journal of repetitive elements. Trends Genet. 2000 Sep;16(9):418-420. PMID: 10973072 For a discussion of repeats in mammalian genomes, see: Smit AF. Interspersed repeats and other mementos of transposable elements in mammalian genomes. Curr Opin Genet Dev. 1999 Dec;9(6):657-63. PMID: 10607616 Smit AF. The origin of interspersed repeats in the human genome. Curr Opin Genet Dev. 1996 Dec;6(6):743-8. PMID: 8994846 
microsat Microsatellite Microsatellites - Di-nucleotide and Tri-nucleotide Repeats Variation and Repeats Description This track displays regions that are likely to be useful as microsatellite markers. These are sequences of at least 15 perfect di-nucleotide and tri-nucleotide repeats and tend to be highly polymorphic in the population. Methods The data shown in this track are a subset of the Simple Repeats track, selecting only those repeats of period 2 and 3, with 100% identity and no indels and with at least 15 copies of the repeat. The Simple Repeats track is created using the Tandem Repeats Finder. For more information about this program, see Benson (1999). Credits Tandem Repeats Finder was written by Gary Benson. References Benson G. Tandem repeats finder: a program to analyze DNA sequences. Nucleic Acids Res. 1999 Jan 15;27(2):573-80. PMID: 9862982; PMC: PMC148217 
xenoMrna Other mRNAs Non-Chicken mRNAs from GenBank mRNA and EST Description This track displays translated blat alignments of vertebrate and invertebrate mRNA in GenBank from organisms other than chicken. Display Conventions and Configuration This track follows the display conventions for PSL alignment tracks. In dense display mode, the items that are more darkly shaded indicate matches of better quality. The strand information (+/-) for this track is in two parts. The first + indicates the orientation of the query sequence whose translated protein produced the match (here always 5' to 3', hence +). The second + or - indicates the orientation of the matching translated genomic sequence. Because the two orientations of a DNA sequence give different predicted protein sequences, there are four combinations. ++ is not the same as --, nor is +- the same as -+. The description page for this track has a filter that can be used to change the display mode, alter the color, and include/exclude a subset of items within the track. This may be helpful when many items are shown in the track display, especially when only some are relevant to the current task. To use the filter: Type a term in one or more of the text boxes to filter the mRNA display. For example, to apply the filter to all mRNAs expressed in a specific organ, type the name of the organ in the tissue box. To view the list of valid terms for each text box, consult the table in the Table Browser that corresponds to the factor on which you wish to filter. For example, the &quot;tissue&quot; table contains all the types of tissues that can be entered into the tissue text box. Multiple terms may be entered at once, separated by a space. Wildcards may also be used in the filter. If filtering on more than one value, choose the desired combination logic. If &quot;and&quot; is selected, only mRNAs that match all filter criteria will be highlighted. If &quot;or&quot; is selected, mRNAs that match any one of the filter criteria will be highlighted. Choose the color or display characteristic that should be used to highlight or include/exclude the filtered items. If &quot;exclude&quot; is chosen, the browser will not display mRNAs that match the filter criteria. If &quot;include&quot; is selected, the browser will display only those mRNAs that match the filter criteria. This track may also be configured to display codon coloring, a feature that allows the user to quickly compare mRNAs against the genomic sequence. For more information about this option, go to the Codon and Base Coloring for Alignment Tracks page. Several types of alignment gap may also be colored; for more information, go to the Alignment Insertion/Deletion Display Options page. Methods The mRNAs were aligned against the chicken genome using translated blat. When a single mRNA aligned in multiple places, the alignment having the highest base identity was found. Only those alignments having a base identity level within 1% of the best and at least 25% base identity with the genomic sequence were kept. Credits The mRNA track was produced at UCSC from mRNA sequence data submitted to the international public sequence databases by scientists worldwide. References Benson DA, Cavanaugh M, Clark K, Karsch-Mizrachi I, Lipman DJ, Ostell J, Sayers EW. GenBank. Nucleic Acids Res. 2013 Jan;41(Database issue):D36-42. PMID: 23193287; PMC: PMC3531190 Benson DA, Karsch-Mizrachi I, Lipman DJ, Ostell J, Wheeler DL. GenBank: update. Nucleic Acids Res. 2004 Jan 1;32(Database issue):D23-6. PMID: 14681350; PMC: PMC308779 Kent WJ. BLAT - the BLAST-like alignment tool. Genome Res. 2002 Apr;12(4):656-64. PMID: 11932250; PMC: PMC187518 
xenoRefGene Other RefSeq Non-Chicken RefSeq Genes Genes and Gene Predictions Description This track shows known protein-coding and non-protein-coding genes for organisms other than chicken, taken from the NCBI RNA reference sequences collection (RefSeq). The data underlying this track are updated weekly. Display Conventions and Configuration This track follows the display conventions for gene prediction tracks. The color shading indicates the level of review the RefSeq record has undergone: predicted (light), provisional (medium), reviewed (dark). The item labels and display colors of features within this track can be configured through the controls at the top of the track description page. Label: By default, items are labeled by gene name. Click the appropriate Label option to display the accession name instead of the gene name, show both the gene and accession names, or turn off the label completely. Codon coloring: This track contains an optional codon coloring feature that allows users to quickly validate and compare gene predictions. To display codon colors, select the genomic codons option from the Color track by codons pull-down menu. For more information about this feature, go to the Coloring Gene Predictions and Annotations by Codon page. Hide non-coding genes: By default, both the protein-coding and non-protein-coding genes are displayed. If you wish to see only the coding genes, click this box. Methods The RNAs were aligned against the chicken genome using blat; those with an alignment of less than 15% were discarded. When a single RNA aligned in multiple places, the alignment having the highest base identity was identified. Only alignments having a base identity level within 0.5% of the best and at least 25% base identity with the genomic sequence were kept. Credits This track was produced at UCSC from RNA sequence data generated by scientists worldwide and curated by the NCBI RefSeq project. References Kent WJ. BLAT--the BLAST-like alignment tool. Genome Res. 2002 Apr;12(4):656-64. PMID: 11932250; PMC: PMC187518 Pruitt KD, Brown GR, Hiatt SM, Thibaud-Nissen F, Astashyn A, Ermolaeva O, Farrell CM, Hart J, Landrum MJ, McGarvey KM et al. RefSeq: an update on mammalian reference sequences. Nucleic Acids Res. 2014 Jan;42(Database issue):D756-63. PMID: 24259432; PMC: PMC3965018 Pruitt KD, Tatusova T, Maglott DR. NCBI Reference Sequence (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res. 2005 Jan 1;33(Database issue):D501-4. PMID: 15608248; PMC: PMC539979 
ucscToRefSeq RefSeq Acc RefSeq Accession Mapping and Sequencing Description This track associates UCSC Genome Browser chromosome names to accession identifiers from the NCBI Reference Sequence Database (RefSeq). The data were downloaded from the NCBI assembly database. Credits The data for this track was prepared by Hiram Clawson. 
simpleRepeat Simple Repeats Simple Tandem Repeats by TRF Variation and Repeats Description This track displays simple tandem repeats (possibly imperfect repeats) located by Tandem Repeats Finder (TRF) which is specialized for this purpose. These repeats can occur within coding regions of genes and may be quite polymorphic. Repeat expansions are sometimes associated with specific diseases. Methods For more information about the TRF program, see Benson (1999). Credits TRF was written by Gary Benson. References Benson G. Tandem repeats finder: a program to analyze DNA sequences. Nucleic Acids Res. 1999 Jan 15;27(2):573-80. PMID: 9862982; PMC: PMC148217 
intronEst Spliced ESTs Chicken ESTs That Have Been Spliced mRNA and EST Description This track shows alignments between chicken expressed sequence tags (ESTs) in GenBank and the genome that show signs of splicing when aligned against the genome. ESTs are single-read sequences, typically about 500 bases in length, that usually represent fragments of transcribed genes. To be considered spliced, an EST must show evidence of at least one canonical intron (i.e., the genomic sequence between EST alignment blocks must be at least 32 bases in length and have GT/AG ends). By requiring splicing, the level of contamination in the EST databases is drastically reduced at the expense of eliminating many genuine 3' ESTs. For a display of all ESTs (including unspliced), see the chicken EST track. Display Conventions and Configuration This track follows the display conventions for PSL alignment tracks. In dense display mode, darker shading indicates a larger number of aligned ESTs. The strand information (+/-) indicates the direction of the match between the EST and the matching genomic sequence. It bears no relationship to the direction of transcription of the RNA with which it might be associated. The description page for this track has a filter that can be used to change the display mode, alter the color, and include/exclude a subset of items within the track. This may be helpful when many items are shown in the track display, especially when only some are relevant to the current task. To use the filter: Type a term in one or more of the text boxes to filter the EST display. For example, to apply the filter to all ESTs expressed in a specific organ, type the name of the organ in the tissue box. To view the list of valid terms for each text box, consult the table in the Table Browser that corresponds to the factor on which you wish to filter. For example, the &quot;tissue&quot; table contains all the types of tissues that can be entered into the tissue text box. Multiple terms may be entered at once, separated by a space. Wildcards may also be used in the filter. If filtering on more than one value, choose the desired combination logic. If &quot;and&quot; is selected, only ESTs that match all filter criteria will be highlighted. If &quot;or&quot; is selected, ESTs that match any one of the filter criteria will be highlighted. Choose the color or display characteristic that should be used to highlight or include/exclude the filtered items. If &quot;exclude&quot; is chosen, the browser will not display ESTs that match the filter criteria. If &quot;include&quot; is selected, the browser will display only those ESTs that match the filter criteria. This track may also be configured to display base labeling, a feature that allows the user to display all bases in the aligning sequence or only those that differ from the genomic sequence. For more information about this option, go to the Base Coloring for Alignment Tracks page. Several types of alignment gap may also be colored; for more information, go to the Alignment Insertion/Deletion Display Options page. Methods To make an EST, RNA is isolated from cells and reverse transcribed into cDNA. Typically, the cDNA is cloned into a plasmid vector and a read is taken from the 5' and/or 3' primer. For most &mdash; but not all &mdash; ESTs, the reverse transcription is primed by an oligo-dT, which hybridizes with the poly-A tail of mature mRNA. The reverse transcriptase may or may not make it to the 5' end of the mRNA, which may or may not be degraded. In general, the 3' ESTs mark the end of transcription reasonably well, but the 5' ESTs may end at any point within the transcript. Some of the newer cap-selected libraries cover transcription start reasonably well. Before the cap-selection techniques emerged, some projects used random rather than poly-A priming in an attempt to retrieve sequence distant from the 3' end. These projects were successful at this, but as a side effect also deposited sequences from unprocessed mRNA and perhaps even genomic sequences into the EST databases. Even outside of the random-primed projects, there is a degree of non-mRNA contamination. Because of this, a single unspliced EST should be viewed with considerable skepticism. To generate this track, chicken ESTs from GenBank were aligned against the genome using blat. Note that the maximum intron length allowed by blat is 750,000 bases, which may eliminate some ESTs with very long introns that might otherwise align. When a single EST aligned in multiple places, the alignment having the highest base identity was identified. Only alignments having a base identity level within 0.5% of the best and at least 96% base identity with the genomic sequence are displayed in this track. Credits This track was produced at UCSC from EST sequence data submitted to the international public sequence databases by scientists worldwide. References Benson DA, Cavanaugh M, Clark K, Karsch-Mizrachi I, Lipman DJ, Ostell J, Sayers EW. GenBank. Nucleic Acids Res. 2013 Jan;41(Database issue):D36-42. PMID: 23193287; PMC: PMC3531190 Benson DA, Karsch-Mizrachi I, Lipman DJ, Ostell J, Wheeler DL. GenBank: update. Nucleic Acids Res. 2004 Jan 1;32(Database issue):D23-6. PMID: 14681350; PMC: PMC308779 Kent WJ. BLAT - the BLAST-like alignment tool. Genome Res. 2002 Apr;12(4):656-64. PMID: 11932250; PMC: PMC187518 
uniprot UniProt UniProt SwissProt/TrEMBL Protein Annotations Genes and Gene Predictions Description This track shows protein sequences and annotations on them from the UniProt/SwissProt database, mapped to genomic coordinates. UniProt/SwissProt data has been curated from scientific publications by the UniProt staff, UniProt/TrEMBL data has been predicted by various computational algorithms. The annotations are divided into multiple subtracks, based on their &quot;feature type&quot; in UniProt. The first two subtracks below - one for SwissProt, one for TrEMBL - show the alignments of protein sequences to the genome, all other tracks below are the protein annotations mapped through these alignments to the genome. Track Name Description UCSC Alignment, SwissProt = curated protein sequences Protein sequences from SwissProt mapped to the genome. All other tracks are (start,end) SwissProt annotations on these sequences mapped through this alignment. Even protein sequences without a single curated annotation (splice isoforms) are visible in this track. Each UniProt protein has one main isoform, which is colored in dark. Alternative isoforms are sequences that do not have annotations on them and are colored in light-blue. They can be hidden with the TrEMBL/Isoform filter (see below). UCSC Alignment, TrEMBL = predicted protein sequences Protein sequences from TrEMBL mapped to the genome. All other tracks below are (start,end) TrEMBL annotations mapped to the genome using this track. This track is hidden by default. To show it, click its checkbox on the track configuration page. UniProt Signal Peptides Regions found in proteins destined to be secreted, generally cleaved from mature protein. UniProt Extracellular Domains Protein domains with the comment &quot;Extracellular&quot;. UniProt Transmembrane Domains Protein domains of the type &quot;Transmembrane&quot;. UniProt Cytoplasmic Domains Protein domains with the comment &quot;Cytoplasmic&quot;. UniProt Polypeptide Chains Polypeptide chain in mature protein after post-processing. UniProt Regions of Interest Regions that have been experimentally defined, such as the role of a region in mediating protein-protein interactions or some other biological process. UniProt Domains Protein domains, zinc finger regions and topological domains. UniProt Disulfide Bonds Disulfide bonds. UniProt Amino Acid Modifications Glycosylation sites, modified residues and lipid moiety-binding regions. UniProt Amino Acid Mutations Mutagenesis sites and sequence variants. UniProt Protein Primary/Secondary Structure Annotations Beta strands, helices, coiled-coil regions and turns. UniProt Sequence Conflicts Differences between Genbank sequences and the UniProt sequence. UniProt Repeats Regions of repeated sequence motifs or repeated domains. UniProt Other Annotations All other annotations, e.g. compositional bias For consistency and convenience for users of mutation-related tracks, the subtrack &quot;UniProt/SwissProt Variants&quot; is a copy of the track &quot;UniProt Variants&quot; in the track group &quot;Phenotype and Literature&quot;, or &quot;Variation and Repeats&quot;, depending on the assembly. Display Conventions and Configuration Genomic locations of UniProt/SwissProt annotations are labeled with a short name for the type of annotation (e.g. &quot;glyco&quot;, &quot;disulf bond&quot;, &quot;Signal peptide&quot; etc.). A click on them shows the full annotation and provides a link to the UniProt/SwissProt record for more details. TrEMBL annotations are always shown in light blue, except in the Signal Peptides, Extracellular Domains, Transmembrane Domains, and Cytoplamsic domains subtracks. Mouse over a feature to see the full UniProt annotation comment. For variants, the mouse over will show the full name of the UniProt disease acronym. The subtracks for domains related to subcellular location are sorted from outside to inside of the cell: Signal peptide, extracellular, transmembrane, and cytoplasmic. Features in the &quot;UniProt Modifications&quot; (modified residues) track are drawn in light green. Disulfide bonds are shown in dark grey. Topological domains in maroon and zinc finger regions in olive green. Duplicate annotations are removed as far as possible: if a TrEMBL annotation has the same genome position and same feature type, comment, disease and mutated amino acids as a SwissProt annotation, it is not shown again. Two annotations mapped through different protein sequence alignments but with the same genome coordinates are only shown once. On the configuration page of this track, you can choose to hide any TrEMBL annotations. This filter will also hide the UniProt alternative isoform protein sequences because both types of information are less relevant to most users. Please contact us if you want more detailed filtering features. Note that for the human hg38 assembly and SwissProt annotations, there also is a public track hub prepared by UniProt itself, with genome annotations maintained by UniProt using their own mapping method based on those Gencode/Ensembl gene models that are annotated in UniProt for a given protein. For proteins that differ from the genome, UniProt's mapping method will, in most cases, map a protein and its annotations to an unexpected location (see below for details on UCSC's mapping method). Methods Briefly, UniProt protein sequences were aligned to the transcripts associated with the protein, the top-scoring alignments were retained, and the result was projected to the genome through a transcript-to-genome alignment. Depending on the genome, the transcript-genome alignments was either provided by the source database (NBCI RefSeq), created at UCSC (UCSC RefSeq) or derived from the transcripts (Ensembl/Augustus). The transcript set is NCBI RefSeq for hg38, UCSC RefSeq for hg19 (due to alt/fix haplotype misplacements in the NCBI RefSeq set on hg19). For other genomes, RefSeq, Ensembl and Augustus are tried, in this order. The resulting protein-genome alignments of this process are available in the file formats for liftOver or pslMap from our data archive (see "Data Access" section below). An important step of the mapping process protein -&gt; transcript -&gt; genome is filtering the alignment from protein to transcript. Due to differences between the UniProt proteins and the transcripts (proteins were made many years before the transcripts were made, and human genomes have variants), the transcript with the highest BLAST score when aligning the protein to all transcripts is not always the correct transcript for a protein sequence. Therefore, the protein sequence is aligned to only a very short list of one or sometimes more transcripts, selected by a three-step procedure: Use transcripts directly annotated by UniProt: for organisms that have a RefSeq transcript track, proteins are aligned to the RefSeq transcripts that are annotated by UniProt for this particular protein. Use transcripts for NCBI Gene ID annotated by UniProt: If no transcripts are annotated on the protein, or the annotated ones have been deprecated by NCBI, but a NCBI Gene ID is annotated, the RefSeq transcripts for this Gene ID are used. This can result in multiple matching transcripts for a protein. Use best matching transcript: If no NCBI Gene is annotated, then BLAST scores are used to pick the transcripts. There can be multiple transcripts for one protein, as their coding sequences can be identical. All transcripts within 1% of the highest observed BLAST score are used. For strategy 2 and 3, many of the transcripts found do not differ in coding sequence, so the resulting alignments on the genome will be identical. Therefore, any identical alignments are removed in a final filtering step. The details page of these alignments will contain a list of all transcripts that result in the same protein-genome alignment. On hg38, only a handful of edge cases (pseudogenes, very recently added proteins) remain in 2023 where strategy 3 has to be used. In other words, when an NCBI or UCSC RefSeq track is used for the mapping and to align a protein sequence to the correct transcript, we use a three stage process: If UniProt has annotated a given RefSeq transcript for a given protein sequence, the protein is aligned to this transcript. Any difference in the version suffix is tolerated in this comparison. If no transcript is annotated or the transcript cannot be found in the NCBI/UCSC RefSeq track, the UniProt-annotated NCBI Gene ID is resolved to a set of NCBI RefSeq transcript IDs via the most current version of NCBI genes tables. Only the top match of the resulting alignments and all others within 1% of its score are used for the mapping. If no transcript can be found after step (2), the protein is aligned to all transcripts, the top match, and all others within 1% of its score are used. This system was designed to resolve the problem of incorrect mappings of proteins, mostly on hg38, due to differences between the SwissProt sequences and the genome reference sequence, which has changed since the proteins were defined. The problem is most pronounced for gene families composed of either very repetitive or very similar proteins. To make sure that the alignments always go to the best chromosome location, all _alt and _fix reference patch sequences are ignored for the alignment, so the patches are entirely free of UniProt annotations. Please contact us if you have feedback on this process or example edge cases. We are not aware of a way to evaluate the results completely and in an automated manner. Proteins were aligned to transcripts with TBLASTN, converted to PSL, filtered with pslReps (93% query coverage, keep alignments within top 1% score), lifted to genome positions with pslMap and filtered again with pslReps. UniProt annotations were obtained from the UniProt XML file. The UniProt annotations were then mapped to the genome through the alignment described above using the pslMap program. This approach draws heavily on the LS-SNP pipeline by Mark Diekhans. Like all Genome Browser source code, the main script used to build this track can be found on Github. Older releases This track is automatically updated on an ongoing basis, every 2-3 months. The current version name is always shown on the track details page, it includes the release of UniProt, the version of the transcript set and a unique MD5 that is based on the protein sequences, the transcript sequences, the mapping file between both and the transcript-genome alignment. The exact transcript that was used for the alignment is shown when clicking a protein alignment in one of the two alignment tracks. For reproducibility of older analysis results and for manual inspection, previous versions of this track are available for browsing in the form of the UCSC UniProt Archive Track Hub (click this link to connect the hub now). The underlying data of all releases of this track (past and current) can be obtained from our downloads server, including the UniProt protein-to-genome alignment. Data Access The raw data of the current track can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the genome annotation is stored in a bigBed file that can be downloaded from the download server. The exact filenames can be found in the track configuration file. Annotations can be converted to ASCII text by our tool bigBedToBed which can be compiled from the source code or downloaded as a precompiled binary for your system. Instructions for downloading source code and binaries can be found here. The tool can also be used to obtain only features within a given range, for example: bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/galGal6/uniprot/unipStruct.bb -chrom=chr6 -start=0 -end=1000000 stdout Please refer to our mailing list archives for questions, or our Data Access FAQ for more information. Lifting from UniProt to genome coordinates in pipelines To facilitate mapping protein coordinates to the genome, we provide the alignment files in formats that are suitable for our command line tools. Our command line programs liftOver or pslMap can be used to map coordinates on protein sequences to genome coordinates. The filenames are unipToGenome.over.chain.gz (liftOver) and unipToGenomeLift.psl.gz (pslMap). Example commands: wget -q https://hgdownload.soe.ucsc.edu/goldenPath/archive/hg38/uniprot/2022_03/unipToGenome.over.chain.gz wget -q https://hgdownload.soe.ucsc.edu/admin/exe/linux.x86_64/liftOver chmod a+x liftOver echo 'Q99697 1 10 annotationOnProtein' &gt; prot.bed liftOver prot.bed unipToGenome.over.chain.gz genome.bed cat genome.bed Credits This track was created by Maximilian Haeussler at UCSC, with a lot of input from Chris Lee, Mark Diekhans and Brian Raney, feedback from the UniProt staff, Alejo Mujica, Regeneron Pharmaceuticals and Pia Riestra, GeneDx. Thanks to UniProt for making all data available for download. References UniProt Consortium. Reorganizing the protein space at the Universal Protein Resource (UniProt). Nucleic Acids Res. 2012 Jan;40(Database issue):D71-5. PMID: 22102590; PMC: PMC3245120 Yip YL, Scheib H, Diemand AV, Gattiker A, Famiglietti LM, Gasteiger E, Bairoch A. The Swiss-Prot variant page and the ModSNP database: a resource for sequence and structure information on human protein variants. Hum Mutat. 2004 May;23(5):464-70. PMID: 15108278 
unipConflict Seq. Conflicts UniProt Sequence Conflicts Genes and Gene Predictions 
unipRepeat Repeats UniProt Repeats Genes and Gene Predictions 
unipStruct Structure UniProt Protein Primary/Secondary Structure Annotations Genes and Gene Predictions 
unipOther Other Annot. UniProt Other Annotations Genes and Gene Predictions 
unipMut Mutations UniProt Amino Acid Mutations Genes and Gene Predictions 
unipModif AA Modifications UniProt Amino Acid Modifications Genes and Gene Predictions 
unipDomain Domains UniProt Domains Genes and Gene Predictions 
unipDisulfBond Disulf. Bonds UniProt Disulfide Bonds Genes and Gene Predictions 
unipChain Chains UniProt Mature Protein Products (Polypeptide Chains) Genes and Gene Predictions 
unipLocCytopl Cytoplasmic UniProt Cytoplasmic Domains Genes and Gene Predictions 
unipLocTransMemb Transmembrane UniProt Transmembrane Domains Genes and Gene Predictions 
unipInterest Interest UniProt Regions of Interest Genes and Gene Predictions 
unipLocExtra Extracellular UniProt Extracellular Domain Genes and Gene Predictions 
unipLocSignal Signal Peptide UniProt Signal Peptides Genes and Gene Predictions 
unipAliTrembl TrEMBL Aln. UCSC alignment of TrEMBL proteins to genome Genes and Gene Predictions 
unipAliSwissprot SwissProt Aln. UCSC alignment of SwissProt proteins to genome (dark blue: main isoform, light blue: alternative isoforms) Genes and Gene Predictions 
vertebrateChainNet Vertebrate Chain/Net Vertebrate Chain and Net Alignments Comparative Genomics Description Chain Track The chain track shows alignments of chicken (Mar. 2018 (GRCg6a/galGal6)/galGal6) to other genomes using a gap scoring system that allows longer gaps than traditional affine gap scoring systems. It can also tolerate gaps in both chicken and the other genome simultaneously. These &quot;double-sided&quot; gaps can be caused by local inversions and overlapping deletions in both species. The chain track displays boxes joined together by either single or double lines. The boxes represent aligning regions. Single lines indicate gaps that are largely due to a deletion in the chicken assembly or an insertion in the chicken assembly. Double lines represent more complex gaps that involve substantial sequence in both species. This may result from inversions, overlapping deletions, an abundance of local mutation, or an unsequenced gap in one species. In cases where multiple chains align over a particular region of the other genome, the chains with single-lined gaps are often due to processed pseudogenes, while chains with double-lined gaps are more often due to paralogs and unprocessed pseudogenes. In the "pack" and "full" display modes, the individual feature names indicate the chromosome, strand, and location (in thousands) of the match for each matching alignment. Net Track The net track shows the best chicken/other chain for every part of the other genome. It is useful for finding orthologous regions and for studying genome rearrangement. The chicken sequence used in this annotation is from the Mar. 2018 (GRCg6a/galGal6) (galGal6) assembly. Display Conventions and Configuration Multiple species are grouped together in a composite track. In the display and on the configuration page, an effort was made to group them loosely into "clades." These groupings are based on the taxonomic classification at NCBI, using the CommonTree tool. Some organisms may be pulled from a larger group into a subgroup, to emphasize a relationship. For example, members of an Order may be listed together, while other organisms in the same Superorder may be grouped separately under the Superorder name. Chain Track By default, the chains to chromosome-based assemblies are colored based on which chromosome they map to in the aligning organism. To turn off the coloring, check the &quot;off&quot; button next to: Color track based on chromosome. To display only the chains of one chromosome in the aligning organism, enter the name of that chromosome (e.g. chr4) in box next to: Filter by chromosome. Net Track In full display mode, the top-level (level 1) chains are the largest, highest-scoring chains that span this region. In many cases gaps exist in the top-level chain. When possible, these are filled in by other chains that are displayed at level 2. The gaps in level 2 chains may be filled by level 3 chains and so forth. In the graphical display, the boxes represent ungapped alignments; the lines represent gaps. Click on a box to view detailed information about the chain as a whole; click on a line to display information about the gap. The detailed information is useful in determining the cause of the gap or, for lower level chains, the genomic rearrangement. Individual items in the display are categorized as one of four types (other than gap): Top - the best, longest match. Displayed on level 1. Syn - line-ups on the same chromosome as the gap in the level above it. Inv - a line-up on the same chromosome as the gap above it, but in the opposite orientation. NonSyn - a match to a chromosome different from the gap in the level above. Methods Chain track Transposons that have been inserted since the chicken/other split were removed from the assemblies. The abbreviated genomes were aligned with lastz, and the transposons were added back in. The resulting alignments were converted into axt format using the lavToAxt program. The axt alignments were fed into axtChain, which organizes all alignments between a single chicken chromosome and a single chromosome from the other genome into a group and creates a kd-tree out of the gapless subsections (blocks) of the alignments. A dynamic program was then run over the kd-trees to find the maximally scoring chains of these blocks. See also: lastz parameters and other details (e.g., update time) and chain minimum score and gap parameters used in these alignments. Net track Chains were derived from lastz alignments, using the methods described on the chain tracks description pages, and sorted with the highest-scoring chains in the genome ranked first. The program chainNet was then used to place the chains one at a time, trimming them as necessary to fit into sections not already covered by a higher-scoring chain. During this process, a natural hierarchy emerged in which a chain that filled a gap in a higher-scoring chain was placed underneath that chain. The program netSyntenic was used to fill in information about the relationship between higher- and lower-level chains, such as whether a lower-level chain was syntenic or inverted relative to the higher-level chain. The program netClass was then used to fill in how much of the gaps and chains contained Ns (sequencing gaps) in one or both species and how much was filled with transposons inserted before and after the two organisms diverged. Credits Lastz (previously known as blastz) was developed at Pennsylvania State University by Minmei Hou, Scott Schwartz, Zheng Zhang, and Webb Miller with advice from Ross Hardison. Lineage-specific repeats were identified by Arian Smit and his RepeatMasker program. The axtChain program was developed at the University of California at Santa Cruz by Jim Kent with advice from Webb Miller and David Haussler. The browser display and database storage of the chains and nets were created by Robert Baertsch and Jim Kent. The chainNet, netSyntenic, and netClass programs were developed at the University of California Santa Cruz by Jim Kent. References Chiaromonte F, Yap VB, Miller W. Scoring pairwise genomic sequence alignments. Pac Symp Biocomput. 2002:115-26. PMID: 11928468 Kent WJ, Baertsch R, Hinrichs A, Miller W, Haussler D. Evolution's cauldron: duplication, deletion, and rearrangement in the mouse and human genomes. Proc Natl Acad Sci U S A. 2003 Sep 30;100(20):11484-9. PMID: 14500911; PMC: PMC208784 Schwartz S, Kent WJ, Smit A, Zhang Z, Baertsch R, Hardison RC, Haussler D, Miller W. Human-mouse alignments with BLASTZ. Genome Res. 2003 Jan;13(1):103-7. PMID: 12529312; PMC: PMC430961 
vertebrateChainNetViewnet Nets Vertebrate Chain and Net Alignments Comparative Genomics 
netLetCam1 letCam1 Net Arctic lamprey (Sep 2013 (LetJap1.0/letCam1)) Alignment Net Comparative Genomics 
netPetMar3 Lamprey Net Lamprey (Dec. 2017 (Pmar_germline 1.0/petMar3)) Alignment Net Comparative Genomics 
netNanPar1 nanPar1 Net Tibetan frog (Mar. 2015 (BGI_ZX_2015/nanPar1)) Alignment Net Comparative Genomics 
netXenLae2 African clawed frog Net African clawed frog (Aug. 2016 (Xenopus_laevis_v2/xenLae2)) Alignment Net Comparative Genomics 
netXenTro9 X. tropicalis Net X. tropicalis (Jul. 2016 (Xenopus_tropicalis_v9.1/xenTro9)) Alignment Net Comparative Genomics 
netThaSir1 Garter snake Net Garter snake (Jun. 2015 (Thamnophis_sirtalis-6.0/thaSir1)) Alignment Net Comparative Genomics 
netAnoCar2 Lizard Net Lizard (May 2010 (Broad AnoCar2.0/anoCar2)) Alignment Net Comparative Genomics 
netPytBiv1 pytBiv1 Net Burmese python (Sep. 2013 (Python_molurus_bivittatus-5.0.2/pytBiv1)) Alignment Net Comparative Genomics 
netApaSpi1 apaSpi1 Net Spiny softshell turtle (May 2013 (ASM38561v1/apaSpi1)) Alignment Net Comparative Genomics 
netPelSin1 pelSin1 Net Chinese softshell turtle (Oct 2011 (PelSin_1.0/pelSin1)) Alignment Net Comparative Genomics 
netAllMis1 American alligator Net American alligator (Aug. 2012 (allMis0.2/allMis1)) Alignment Net Comparative Genomics 
netAllSin1 allSin1 Net Chinese alligator (29 Aug 2013 (ASM45574v1/allSin1)) Alignment Net Comparative Genomics 
netCheMyd1 cheMyd1 Net Green seaturtle (Mar 2013 (CheMyd_1.0/cheMyd1)) Alignment Net Comparative Genomics 
netChrPic2 chrPic2 Net Painted turtle (Mar. 2014 (v3.0.3/chrPic2)) Alignment Net Comparative Genomics 
netMm10 Mouse Net Mouse (Dec. 2011 (GRCm38/mm10)) Alignment Net Comparative Genomics 
netHg38 hg38 Net Human (Dec. 2013 (GRCh38/hg38)) Alignment Net Comparative Genomics 
vertebrateChainNetViewchain Chains Vertebrate Chain and Net Alignments Comparative Genomics 
chainLetCam1 letCam1 Chain Arctic lamprey (Sep 2013 (LetJap1.0/letCam1)) Chained Alignments Comparative Genomics 
chainPetMar3 Lamprey Chain Lamprey (Dec. 2017 (Pmar_germline 1.0/petMar3)) Chained Alignments Comparative Genomics 
chainNanPar1 nanPar1 Chain Tibetan frog (Mar. 2015 (BGI_ZX_2015/nanPar1)) Chained Alignments Comparative Genomics 
chainXenLae2 African clawed frog Chain African clawed frog (Aug. 2016 (Xenopus_laevis_v2/xenLae2)) Chained Alignments Comparative Genomics 
chainXenTro9 X. tropicalis Chain X. tropicalis (Jul. 2016 (Xenopus_tropicalis_v9.1/xenTro9)) Chained Alignments Comparative Genomics 
chainThaSir1 Garter snake Chain Garter snake (Jun. 2015 (Thamnophis_sirtalis-6.0/thaSir1)) Chained Alignments Comparative Genomics 
chainAnoCar2 Lizard Chain Lizard (May 2010 (Broad AnoCar2.0/anoCar2)) Chained Alignments Comparative Genomics 
chainPytBiv1 pytBiv1 Chain Burmese python (Sep. 2013 (Python_molurus_bivittatus-5.0.2/pytBiv1)) Chained Alignments Comparative Genomics 
chainApaSpi1 apaSpi1 Chain Spiny softshell turtle (May 2013 (ASM38561v1/apaSpi1)) Chained Alignments Comparative Genomics 
chainPelSin1 pelSin1 Chain Chinese softshell turtle (Oct 2011 (PelSin_1.0/pelSin1)) Chained Alignments Comparative Genomics 
chainAllMis1 American alligator Chain American alligator (Aug. 2012 (allMis0.2/allMis1)) Chained Alignments Comparative Genomics 
chainAllSin1 allSin1 Chain Chinese alligator (29 Aug 2013 (ASM45574v1/allSin1)) Chained Alignments Comparative Genomics 
chainCheMyd1 cheMyd1 Chain Green seaturtle (Mar 2013 (CheMyd_1.0/cheMyd1)) Chained Alignments Comparative Genomics 
chainChrPic2 chrPic2 Chain Painted turtle (Mar. 2014 (v3.0.3/chrPic2)) Chained Alignments Comparative Genomics 
chainMm10 Mouse Chain Mouse (Dec. 2011 (GRCm38/mm10)) Chained Alignments Comparative Genomics 
chainHg38 hg38 Chain Human (Dec. 2013 (GRCh38/hg38)) Chained Alignments Comparative Genomics 
windowmaskerSdust WM + SDust Genomic Intervals Masked by WindowMasker + SDust Variation and Repeats Description This track depicts masked sequence as determined by WindowMasker. The WindowMasker tool is included in the NCBI C++ toolkit. The source code for the entire toolkit is available from the NCBI FTP site. Methods To create this track, WindowMasker was run with the following parameters: windowmasker -mk_counts true -input galGal6.fa -output wm_counts windowmasker -ustat wm_counts -sdust true -input galGal6.fa -output repeats.bed The repeats.bed (BED3) file was loaded into the &quot;windowmaskerSdust&quot; table for this track. References Morgulis A, Gertz EM, Sch&auml;ffer AA, Agarwala R. WindowMasker: window-based masker for sequenced genomes. Bioinformatics. 2006 Jan 15;22(2):134-41. PMID: 16287941 
chainNetGCF_016699485.2 Chicken Chain/Net Chicken (Jan. 2021 (GRCg7b/GCF_016699485.2)), Chain and Net Alignments Comparative Genomics Description Chain Track The chain track shows alignments of chicken (Mar. 2018 (GRCg6a/galGal6)/galGal6) to other genomes using a gap scoring system that allows longer gaps than traditional affine gap scoring systems. It can also tolerate gaps in both chicken and the other genome simultaneously. These &quot;double-sided&quot; gaps can be caused by local inversions and overlapping deletions in both species. The chain track displays boxes joined together by either single or double lines. The boxes represent aligning regions. Single lines indicate gaps that are largely due to a deletion in the GCF_016699485.2 assembly or an insertion in the chicken assembly. Double lines represent more complex gaps that involve substantial sequence in both species. This may result from inversions, overlapping deletions, an abundance of local mutation, or an unsequenced gap in one species. In cases where multiple chains align over a particular region of the other genome, the chains with single-lined gaps are often due to processed pseudogenes, while chains with double-lined gaps are more often due to paralogs and unprocessed pseudogenes. In the "pack" and "full" display modes, the individual feature names indicate the chromosome, strand, and location (in thousands) of the match for each matching alignment. Net Track The net track shows the best chicken/other chain for every part of the other genome. It is useful for finding orthologous regions and for studying genome rearrangement. The chicken sequence used in this annotation is from the Mar. 2018 (GRCg6a/galGal6) (galGal6) assembly. Display Conventions and Configuration Multiple species are grouped together in a composite track. In the display and on the configuration page, an effort was made to group them loosely into "clades." These groupings are based on the taxonomic classification at NCBI, using the CommonTree tool. Some organisms may be pulled from a larger group into a subgroup, to emphasize a relationship. For example, members of an Order may be listed together, while other organisms in the same Superorder may be grouped separately under the Superorder name. Chain Track By default, the chains to chromosome-based assemblies are colored based on which chromosome they map to in the aligning organism. To turn off the coloring, check the &quot;off&quot; button next to: Color track based on chromosome. To display only the chains of one chromosome in the aligning organism, enter the name of that chromosome (e.g. chr4) in box next to: Filter by chromosome. Net Track In full display mode, the top-level (level 1) chains are the largest, highest-scoring chains that span this region. In many cases gaps exist in the top-level chain. When possible, these are filled in by other chains that are displayed at level 2. The gaps in level 2 chains may be filled by level 3 chains and so forth. In the graphical display, the boxes represent ungapped alignments; the lines represent gaps. Click on a box to view detailed information about the chain as a whole; click on a line to display information about the gap. The detailed information is useful in determining the cause of the gap or, for lower level chains, the genomic rearrangement. Individual items in the display are categorized as one of four types (other than gap): Top - the best, longest match. Displayed on level 1. Syn - line-ups on the same chromosome as the gap in the level above it. Inv - a line-up on the same chromosome as the gap above it, but in the opposite orientation. NonSyn - a match to a chromosome different from the gap in the level above. Methods Chain track Transposons that have been inserted since the chicken/other split were removed from the assemblies. The abbreviated genomes were aligned with lastz, and the transposons were added back in. The resulting alignments were converted into axt format using the lavToAxt program. The axt alignments were fed into axtChain, which organizes all alignments between a single chicken chromosome and a single chromosome from the other genome into a group and creates a kd-tree out of the gapless subsections (blocks) of the alignments. A dynamic program was then run over the kd-trees to find the maximally scoring chains of these blocks. See also: lastz parameters and other details (e.g., update time) and chain minimum score and gap parameters used in these alignments. Net track Chains were derived from lastz alignments, using the methods described on the chain tracks description pages, and sorted with the highest-scoring chains in the genome ranked first. The program chainNet was then used to place the chains one at a time, trimming them as necessary to fit into sections not already covered by a higher-scoring chain. During this process, a natural hierarchy emerged in which a chain that filled a gap in a higher-scoring chain was placed underneath that chain. The program netSyntenic was used to fill in information about the relationship between higher- and lower-level chains, such as whether a lower-level chain was syntenic or inverted relative to the higher-level chain. The program netClass was then used to fill in how much of the gaps and chains contained Ns (sequencing gaps) in one or both species and how much was filled with transposons inserted before and after the two organisms diverged. Credits Lastz (previously known as blastz) was developed at Pennsylvania State University by Minmei Hou, Scott Schwartz, Zheng Zhang, and Webb Miller with advice from Ross Hardison. Lineage-specific repeats were identified by Arian Smit and his RepeatMasker program. The axtChain program was developed at the University of California at Santa Cruz by Jim Kent with advice from Webb Miller and David Haussler. The browser display and database storage of the chains and nets were created by Robert Baertsch and Jim Kent. The chainNet, netSyntenic, and netClass programs were developed at the University of California Santa Cruz by Jim Kent. References Chiaromonte F, Yap VB, Miller W. Scoring pairwise genomic sequence alignments. Pac Symp Biocomput. 2002:115-26. PMID: 11928468 Kent WJ, Baertsch R, Hinrichs A, Miller W, Haussler D. Evolution's cauldron: duplication, deletion, and rearrangement in the mouse and human genomes. Proc Natl Acad Sci U S A. 2003 Sep 30;100(20):11484-9. PMID: 14500911; PMC: PMC208784 Schwartz S, Kent WJ, Smit A, Zhang Z, Baertsch R, Hardison RC, Haussler D, Miller W. Human-mouse alignments with BLASTZ. Genome Res. 2003 Jan;13(1):103-7. PMID: 12529312; PMC: PMC430961 
mafNetGCF_016699485.2Viewnet Net Chicken (Jan. 2021 (GRCg7b/GCF_016699485.2)), Chain and Net Alignments Comparative Genomics 
synNetGCF_016699485.2 Chicken synNet Chicken (Jan. 2021 (GRCg7b/GCF_016699485.2)) Syntenic Net Alignment Comparative Genomics 
rbestNetGCF_016699485.2 Chicken rbestNet Chicken (Jan. 2021 (GRCg7b/GCF_016699485.2)) Reciprocal Best Net Alignment Comparative Genomics 
netGCF_016699485.2 Chicken net Chicken (Jan. 2021 (GRCg7b/GCF_016699485.2)) Net Alignment Comparative Genomics 
chainNetGCF_016699485.2Viewchain Chain Chicken (Jan. 2021 (GRCg7b/GCF_016699485.2)), Chain and Net Alignments Comparative Genomics 
chainSynGCF_016699485.2 Chicken synChain Chicken (Jan. 2021 (GRCg7b/GCF_016699485.2)) Syntenic Chained Alignments Comparative Genomics 
chainRBestGCF_016699485.2 Chicken rbChain Chicken (Jan. 2021 (GRCg7b/GCF_016699485.2)) Reciprocal Best Chained Alignments Comparative Genomics 
chainGCF_016699485.2 Chicken Chain Chicken (Jan. 2021 (GRCg7b/GCF_016699485.2)) Chained Alignments Comparative Genomics 
chainNetMm39 Mouse Chain/Net Mouse (Jun. 2020 (GRCm39/mm39)), Chain and Net Alignments Comparative Genomics Description Chain Track The chain track shows alignments of chicken (Mar. 2018 (GRCg6a/galGal6)/galGal6) to other genomes using a gap scoring system that allows longer gaps than traditional affine gap scoring systems. It can also tolerate gaps in both chicken and the other genome simultaneously. These &quot;double-sided&quot; gaps can be caused by local inversions and overlapping deletions in both species. The chain track displays boxes joined together by either single or double lines. The boxes represent aligning regions. Single lines indicate gaps that are largely due to a deletion in the mouse assembly or an insertion in the chicken assembly. Double lines represent more complex gaps that involve substantial sequence in both species. This may result from inversions, overlapping deletions, an abundance of local mutation, or an unsequenced gap in one species. In cases where multiple chains align over a particular region of the other genome, the chains with single-lined gaps are often due to processed pseudogenes, while chains with double-lined gaps are more often due to paralogs and unprocessed pseudogenes. In the "pack" and "full" display modes, the individual feature names indicate the chromosome, strand, and location (in thousands) of the match for each matching alignment. Net Track The net track shows the best chicken/other chain for every part of the other genome. It is useful for finding orthologous regions and for studying genome rearrangement. The chicken sequence used in this annotation is from the Mar. 2018 (GRCg6a/galGal6) (galGal6) assembly. Display Conventions and Configuration Multiple species are grouped together in a composite track. In the display and on the configuration page, an effort was made to group them loosely into "clades." These groupings are based on the taxonomic classification at NCBI, using the CommonTree tool. Some organisms may be pulled from a larger group into a subgroup, to emphasize a relationship. For example, members of an Order may be listed together, while other organisms in the same Superorder may be grouped separately under the Superorder name. Chain Track By default, the chains to chromosome-based assemblies are colored based on which chromosome they map to in the aligning organism. To turn off the coloring, check the &quot;off&quot; button next to: Color track based on chromosome. To display only the chains of one chromosome in the aligning organism, enter the name of that chromosome (e.g. chr4) in box next to: Filter by chromosome. Net Track In full display mode, the top-level (level 1) chains are the largest, highest-scoring chains that span this region. In many cases gaps exist in the top-level chain. When possible, these are filled in by other chains that are displayed at level 2. The gaps in level 2 chains may be filled by level 3 chains and so forth. In the graphical display, the boxes represent ungapped alignments; the lines represent gaps. Click on a box to view detailed information about the chain as a whole; click on a line to display information about the gap. The detailed information is useful in determining the cause of the gap or, for lower level chains, the genomic rearrangement. Individual items in the display are categorized as one of four types (other than gap): Top - the best, longest match. Displayed on level 1. Syn - line-ups on the same chromosome as the gap in the level above it. Inv - a line-up on the same chromosome as the gap above it, but in the opposite orientation. NonSyn - a match to a chromosome different from the gap in the level above. Methods Chain track Transposons that have been inserted since the chicken/other split were removed from the assemblies. The abbreviated genomes were aligned with lastz, and the transposons were added back in. The resulting alignments were converted into axt format using the lavToAxt program. The axt alignments were fed into axtChain, which organizes all alignments between a single chicken chromosome and a single chromosome from the other genome into a group and creates a kd-tree out of the gapless subsections (blocks) of the alignments. A dynamic program was then run over the kd-trees to find the maximally scoring chains of these blocks. See also: lastz parameters and other details (e.g., update time) and chain minimum score and gap parameters used in these alignments. Net track Chains were derived from lastz alignments, using the methods described on the chain tracks description pages, and sorted with the highest-scoring chains in the genome ranked first. The program chainNet was then used to place the chains one at a time, trimming them as necessary to fit into sections not already covered by a higher-scoring chain. During this process, a natural hierarchy emerged in which a chain that filled a gap in a higher-scoring chain was placed underneath that chain. The program netSyntenic was used to fill in information about the relationship between higher- and lower-level chains, such as whether a lower-level chain was syntenic or inverted relative to the higher-level chain. The program netClass was then used to fill in how much of the gaps and chains contained Ns (sequencing gaps) in one or both species and how much was filled with transposons inserted before and after the two organisms diverged. Credits Lastz (previously known as blastz) was developed at Pennsylvania State University by Minmei Hou, Scott Schwartz, Zheng Zhang, and Webb Miller with advice from Ross Hardison. Lineage-specific repeats were identified by Arian Smit and his RepeatMasker program. The axtChain program was developed at the University of California at Santa Cruz by Jim Kent with advice from Webb Miller and David Haussler. The browser display and database storage of the chains and nets were created by Robert Baertsch and Jim Kent. The chainNet, netSyntenic, and netClass programs were developed at the University of California Santa Cruz by Jim Kent. References Chiaromonte F, Yap VB, Miller W. Scoring pairwise genomic sequence alignments. Pac Symp Biocomput. 2002:115-26. PMID: 11928468 Kent WJ, Baertsch R, Hinrichs A, Miller W, Haussler D. Evolution's cauldron: duplication, deletion, and rearrangement in the mouse and human genomes. Proc Natl Acad Sci U S A. 2003 Sep 30;100(20):11484-9. PMID: 14500911; PMC: PMC208784 Schwartz S, Kent WJ, Smit A, Zhang Z, Baertsch R, Hardison RC, Haussler D, Miller W. Human-mouse alignments with BLASTZ. Genome Res. 2003 Jan;13(1):103-7. PMID: 12529312; PMC: PMC430961 
chainNetMm39Viewnet Net Mouse (Jun. 2020 (GRCm39/mm39)), Chain and Net Alignments Comparative Genomics 
netMm39 Mouse Net Mouse (Jun. 2020 (GRCm39/mm39)) Alignment Net Comparative Genomics Description Chain Track The chain track shows alignments of chicken (Mar. 2018 (GRCg6a/galGal6)/galGal6) to other genomes using a gap scoring system that allows longer gaps than traditional affine gap scoring systems. It can also tolerate gaps in both chicken and the other genome simultaneously. These &quot;double-sided&quot; gaps can be caused by local inversions and overlapping deletions in both species. The chain track displays boxes joined together by either single or double lines. The boxes represent aligning regions. Single lines indicate gaps that are largely due to a deletion in the mouse assembly or an insertion in the chicken assembly. Double lines represent more complex gaps that involve substantial sequence in both species. This may result from inversions, overlapping deletions, an abundance of local mutation, or an unsequenced gap in one species. In cases where multiple chains align over a particular region of the other genome, the chains with single-lined gaps are often due to processed pseudogenes, while chains with double-lined gaps are more often due to paralogs and unprocessed pseudogenes. In the "pack" and "full" display modes, the individual feature names indicate the chromosome, strand, and location (in thousands) of the match for each matching alignment. Net Track The net track shows the best chicken/other chain for every part of the other genome. It is useful for finding orthologous regions and for studying genome rearrangement. The chicken sequence used in this annotation is from the Mar. 2018 (GRCg6a/galGal6) (galGal6) assembly. Display Conventions and Configuration Multiple species are grouped together in a composite track. In the display and on the configuration page, an effort was made to group them loosely into "clades." These groupings are based on the taxonomic classification at NCBI, using the CommonTree tool. Some organisms may be pulled from a larger group into a subgroup, to emphasize a relationship. For example, members of an Order may be listed together, while other organisms in the same Superorder may be grouped separately under the Superorder name. Chain Track By default, the chains to chromosome-based assemblies are colored based on which chromosome they map to in the aligning organism. To turn off the coloring, check the &quot;off&quot; button next to: Color track based on chromosome. To display only the chains of one chromosome in the aligning organism, enter the name of that chromosome (e.g. chr4) in box next to: Filter by chromosome. Net Track In full display mode, the top-level (level 1) chains are the largest, highest-scoring chains that span this region. In many cases gaps exist in the top-level chain. When possible, these are filled in by other chains that are displayed at level 2. The gaps in level 2 chains may be filled by level 3 chains and so forth. In the graphical display, the boxes represent ungapped alignments; the lines represent gaps. Click on a box to view detailed information about the chain as a whole; click on a line to display information about the gap. The detailed information is useful in determining the cause of the gap or, for lower level chains, the genomic rearrangement. Individual items in the display are categorized as one of four types (other than gap): Top - the best, longest match. Displayed on level 1. Syn - line-ups on the same chromosome as the gap in the level above it. Inv - a line-up on the same chromosome as the gap above it, but in the opposite orientation. NonSyn - a match to a chromosome different from the gap in the level above. Methods Chain track Transposons that have been inserted since the chicken/other split were removed from the assemblies. The abbreviated genomes were aligned with lastz, and the transposons were added back in. The resulting alignments were converted into axt format using the lavToAxt program. The axt alignments were fed into axtChain, which organizes all alignments between a single chicken chromosome and a single chromosome from the other genome into a group and creates a kd-tree out of the gapless subsections (blocks) of the alignments. A dynamic program was then run over the kd-trees to find the maximally scoring chains of these blocks. See also: lastz parameters and other details (e.g., update time) and chain minimum score and gap parameters used in these alignments. Net track Chains were derived from lastz alignments, using the methods described on the chain tracks description pages, and sorted with the highest-scoring chains in the genome ranked first. The program chainNet was then used to place the chains one at a time, trimming them as necessary to fit into sections not already covered by a higher-scoring chain. During this process, a natural hierarchy emerged in which a chain that filled a gap in a higher-scoring chain was placed underneath that chain. The program netSyntenic was used to fill in information about the relationship between higher- and lower-level chains, such as whether a lower-level chain was syntenic or inverted relative to the higher-level chain. The program netClass was then used to fill in how much of the gaps and chains contained Ns (sequencing gaps) in one or both species and how much was filled with transposons inserted before and after the two organisms diverged. Credits Lastz (previously known as blastz) was developed at Pennsylvania State University by Minmei Hou, Scott Schwartz, Zheng Zhang, and Webb Miller with advice from Ross Hardison. Lineage-specific repeats were identified by Arian Smit and his RepeatMasker program. The axtChain program was developed at the University of California at Santa Cruz by Jim Kent with advice from Webb Miller and David Haussler. The browser display and database storage of the chains and nets were created by Robert Baertsch and Jim Kent. The chainNet, netSyntenic, and netClass programs were developed at the University of California Santa Cruz by Jim Kent. References Chiaromonte F, Yap VB, Miller W. Scoring pairwise genomic sequence alignments. Pac Symp Biocomput. 2002:115-26. PMID: 11928468 Kent WJ, Baertsch R, Hinrichs A, Miller W, Haussler D. Evolution's cauldron: duplication, deletion, and rearrangement in the mouse and human genomes. Proc Natl Acad Sci U S A. 2003 Sep 30;100(20):11484-9. PMID: 14500911; PMC: PMC208784 Schwartz S, Kent WJ, Smit A, Zhang Z, Baertsch R, Hardison RC, Haussler D, Miller W. Human-mouse alignments with BLASTZ. Genome Res. 2003 Jan;13(1):103-7. PMID: 12529312; PMC: PMC430961 
chainNetMm39Viewchain Chain Mouse (Jun. 2020 (GRCm39/mm39)), Chain and Net Alignments Comparative Genomics 
chainMm39 Mouse Chain Mouse (Jun. 2020 (GRCm39/mm39)) Chained Alignments Comparative Genomics Description Chain Track The chain track shows alignments of chicken (Mar. 2018 (GRCg6a/galGal6)/galGal6) to other genomes using a gap scoring system that allows longer gaps than traditional affine gap scoring systems. It can also tolerate gaps in both chicken and the other genome simultaneously. These &quot;double-sided&quot; gaps can be caused by local inversions and overlapping deletions in both species. The chain track displays boxes joined together by either single or double lines. The boxes represent aligning regions. Single lines indicate gaps that are largely due to a deletion in the mouse assembly or an insertion in the chicken assembly. Double lines represent more complex gaps that involve substantial sequence in both species. This may result from inversions, overlapping deletions, an abundance of local mutation, or an unsequenced gap in one species. In cases where multiple chains align over a particular region of the other genome, the chains with single-lined gaps are often due to processed pseudogenes, while chains with double-lined gaps are more often due to paralogs and unprocessed pseudogenes. In the "pack" and "full" display modes, the individual feature names indicate the chromosome, strand, and location (in thousands) of the match for each matching alignment. Net Track The net track shows the best chicken/other chain for every part of the other genome. It is useful for finding orthologous regions and for studying genome rearrangement. The chicken sequence used in this annotation is from the Mar. 2018 (GRCg6a/galGal6) (galGal6) assembly. Display Conventions and Configuration Multiple species are grouped together in a composite track. In the display and on the configuration page, an effort was made to group them loosely into "clades." These groupings are based on the taxonomic classification at NCBI, using the CommonTree tool. Some organisms may be pulled from a larger group into a subgroup, to emphasize a relationship. For example, members of an Order may be listed together, while other organisms in the same Superorder may be grouped separately under the Superorder name. Chain Track By default, the chains to chromosome-based assemblies are colored based on which chromosome they map to in the aligning organism. To turn off the coloring, check the &quot;off&quot; button next to: Color track based on chromosome. To display only the chains of one chromosome in the aligning organism, enter the name of that chromosome (e.g. chr4) in box next to: Filter by chromosome. Net Track In full display mode, the top-level (level 1) chains are the largest, highest-scoring chains that span this region. In many cases gaps exist in the top-level chain. When possible, these are filled in by other chains that are displayed at level 2. The gaps in level 2 chains may be filled by level 3 chains and so forth. In the graphical display, the boxes represent ungapped alignments; the lines represent gaps. Click on a box to view detailed information about the chain as a whole; click on a line to display information about the gap. The detailed information is useful in determining the cause of the gap or, for lower level chains, the genomic rearrangement. Individual items in the display are categorized as one of four types (other than gap): Top - the best, longest match. Displayed on level 1. Syn - line-ups on the same chromosome as the gap in the level above it. Inv - a line-up on the same chromosome as the gap above it, but in the opposite orientation. NonSyn - a match to a chromosome different from the gap in the level above. Methods Chain track Transposons that have been inserted since the chicken/other split were removed from the assemblies. The abbreviated genomes were aligned with lastz, and the transposons were added back in. The resulting alignments were converted into axt format using the lavToAxt program. The axt alignments were fed into axtChain, which organizes all alignments between a single chicken chromosome and a single chromosome from the other genome into a group and creates a kd-tree out of the gapless subsections (blocks) of the alignments. A dynamic program was then run over the kd-trees to find the maximally scoring chains of these blocks. See also: lastz parameters and other details (e.g., update time) and chain minimum score and gap parameters used in these alignments. Net track Chains were derived from lastz alignments, using the methods described on the chain tracks description pages, and sorted with the highest-scoring chains in the genome ranked first. The program chainNet was then used to place the chains one at a time, trimming them as necessary to fit into sections not already covered by a higher-scoring chain. During this process, a natural hierarchy emerged in which a chain that filled a gap in a higher-scoring chain was placed underneath that chain. The program netSyntenic was used to fill in information about the relationship between higher- and lower-level chains, such as whether a lower-level chain was syntenic or inverted relative to the higher-level chain. The program netClass was then used to fill in how much of the gaps and chains contained Ns (sequencing gaps) in one or both species and how much was filled with transposons inserted before and after the two organisms diverged. Credits Lastz (previously known as blastz) was developed at Pennsylvania State University by Minmei Hou, Scott Schwartz, Zheng Zhang, and Webb Miller with advice from Ross Hardison. Lineage-specific repeats were identified by Arian Smit and his RepeatMasker program. The axtChain program was developed at the University of California at Santa Cruz by Jim Kent with advice from Webb Miller and David Haussler. The browser display and database storage of the chains and nets were created by Robert Baertsch and Jim Kent. The chainNet, netSyntenic, and netClass programs were developed at the University of California Santa Cruz by Jim Kent. References Chiaromonte F, Yap VB, Miller W. Scoring pairwise genomic sequence alignments. Pac Symp Biocomput. 2002:115-26. PMID: 11928468 Kent WJ, Baertsch R, Hinrichs A, Miller W, Haussler D. Evolution's cauldron: duplication, deletion, and rearrangement in the mouse and human genomes. Proc Natl Acad Sci U S A. 2003 Sep 30;100(20):11484-9. PMID: 14500911; PMC: PMC208784 Schwartz S, Kent WJ, Smit A, Zhang Z, Baertsch R, Hardison RC, Haussler D, Miller W. Human-mouse alignments with BLASTZ. Genome Res. 2003 Jan;13(1):103-7. PMID: 12529312; PMC: PMC430961