\nThis track contains mappings of single nucleotide variants\nand small insertions and deletions (indels)\nfrom the European Variation Archive\n(EVA)\nRelease 3 for the mouse mm39 genome. The dbSNP database at NCBI no longer\nhosts non-human variants.\n
\n\n\nVariants are shown as single tick marks at most zoom levels.\nWhen viewing the track at or near base-level resolution, the displayed\nwidth of the SNP variant corresponds to the width of the variant in the\nreference sequence. Insertions are indicated by a single tick mark displayed\nbetween two nucleotides, single nucleotide polymorphisms are displayed as the\nwidth of a single base, and multiple nucleotide variants are represented by a\nblock that spans two or more bases. The display is set to automatically collapse to \ndense visibility when there are more than 100k variants in the window. \nWhen the window size is more than 250k bp, the display is switched to density graph mode.\n
\n\n\nNavigation to an individual variant can be accomplished by typing or copying\nthe variant identifier (rsID) or the genomic coordinates into the Position/Search box on the \nBrowser.
\n\n\nA click on an item in the graphical display displays a page with data about\nthat variant. Data fields include the Reference and Alternate Alleles, the\nclass of the variant as reported by EVA, the source of the data, the amino acid\nchange, if any, and the functional class as determined by UCSC's Variant Annotation\nIntegrator.\n
\n\nVariants can be filtered using the track controls to show subsets of the \ndata by either EVA Sequence Ontology (SO) term, UCSC-generated functional effect, or\nby color, which bins the UCSC functional effects into general classes.
\n\n\nMousing over an item shows the ucscClass, which is the consequence according to the\nVariant Annotation Integrator, and\nthe aaChange when one is available, which is the change in amino acid in HGVS.p\nterms. Items may have multiple ucscClasses, which will all be shown in the mouse-over\nin a comma-separated list. Likewise, multiple HGVS.p terms may be shown for each rsID\nseparated by spaces describing all possible AA changes.
\n\nMultiple items may appear due to different variant predictions on multiple gene transcripts.\nFor all organisms the gene models used were ncbiRefSeqCurated, except for mm39 which\nused ncbiRefSeqSelect.
\n\n\n\nVariants are colored according to the most potentially deleterious functional effect prediction\naccording to the Variant Annotation Integrator. Specific bins can be seen in the Methods section\nbelow.\n
\n\n\n
| Color | \nVariant Type | \n
|---|---|
| Protein-altering variants and splice site variants | |
| Synonymous codon variants | |
| Non-coding transcript or Untranslated Region (UTR) variants | |
| Intergenic and intronic variants |
\nVariants are classified by EVA into one of the following sequence ontology terms:\n
\n\n\nData were downloaded from the European Variation Archive EVA release 3 (2022-02-24)\ncurrent_ids.vcf.gz files corresponding to the proper assembly.
\n\nChromosome names were converted to UCSC-style, a few problematic variants were removed,\nand the variants passed through the\nVariant Annotation Integrator to\npredict consequence. For every organism the ncbiRefSeqCurated gene models were used to\npredict the consequences, except for mm39 which used the ncbiRefSeqSelect models.
\n\nVariants were then colored according to their predicted consequence in the following fashion:\n
\nSequence Ontology ("SO:")\nterms were converted to the variant classes, then the files were converted to BED,\nand then bigBed format.\n
\n\nNo functional annotations were provided by the EVA (e.g., missense, nonsense, etc).\nThese were computed using UCSC's Variant Annotation Integrator (Hinrichs, et al., 2016).\nAmino-acid substitutions for missense variants are based\non RefSeq alignments of mRNA transcripts, which do not always match the amino acids\npredicted from translating the genomic sequence. Therefore, in some instances, the\nvariant and the genomic nucleotide and associated amino acid may be reversed.\nE.g., a Pro > Arg change from the perspective of the mRNA would be Arg > Pro from\nthe persepective the genomic sequence.\nFor complete documentation of the processing of these tracks, read the\n\nEVA Release 3 MakeDoc.
\n\n\nNote: It is not recommeneded to use LiftOver to convert SNPs between assemblies,\nand more information about how to convert SNPs between assemblies can be found on the following\nFAQ entry.
\n\nThe data can be explored interactively with the Table Browser,\nor the Data Integrator. For automated analysis, the data may be\nqueried from our REST API. Please refer to our\nmailing list archives\nfor questions, or our Data Access FAQ for more\ninformation.
\n\n\nFor automated download and analysis, this annotation is stored in a bigBed file that\ncan be downloaded from our download server. The file for this track is called evaSnp.bb.\nIndividual regions or the whole genome annotation can be obtained using our tool\nbigBedToBed which can be compiled from the source code or downloaded as a precompiled\nbinary for your system. Instructions for downloading source code and binaries can be found\nhere.\nThe tool can also be used to obtain only features within a given range, e.g.\n
\nbigBedToBed https://hgdownload.soe.ucsc.edu/gbdb/mm39/bbi/evaSnp.bb -chrom=chr21 -start=0 -end=100000000 stdout\n
\nThis track was produced from the European\nVariation Archive release 3 data. Consequences were predicted using UCSC's Variant Annotation\nIntegrator and NCBI's RefSeq gene models. \n
\n\n\nCezard T, Cunningham F, Hunt SE, Koylass B, Kumar N, Saunders G, Shen A, Silva AF,\nTsukanov K, Venkataraman S et al. The European Variation Archive: a FAIR resource of genomic variation for all\nspecies. Nucleic Acids Res. 2021 Oct 28:gkab960.\ndoi:10.1093/nar/gkab960.\nEpub ahead of print. PMID: 34718739. PMID: PMC8728205.\n
\n\nHinrichs AS, Raney BJ, Speir ML, Rhead B, Casper J, Karolchik D, Kuhn RM, Rosenbloom KR, Zweig AS,\nHaussler D, Kent WJ.\nUCSC Data Integrator and Variant Annotation Integrator.\nBioinformatics. 2016 May 1;32(9):1430-2.\nPMID: 26740527; PMC:\nPMC4848401\n
\n" } }, "description": "Short Genetic Variants from European Variant Archive Release 3", "category": ["Variation and Repeats"], "displays": [ { "type": "LinearBasicDisplay", "displayId": "mm39-evaSnp-LinearBasicDisplay", "mouseover": "jexl:`Ref/Alt allele(s): ${get(feature,'ref')}>${get(feature,'alt')}\nThe GENCODE Genes track (version M39, June 2026) shows high-quality manual\nannotations merged with evidence-based automated annotations across the entire\nhuman genome generated by the\nGENCODE project.\nBy default, only the basic gene set is\ndisplayed, which is a subset of the comprehensive gene set. The basic set represents transcripts\nthat GENCODE believes will be useful to the majority of users.
\n\n\nThe track includes protein-coding genes, non-coding RNA genes, and pseudo-genes, though pseudo-genes\nare not displayed by default. It contains annotations on the reference chromosomes as well as\nassembly patches and alternative loci (haplotypes).
\n\n\nThe VM39 release was derived from the GTF file that contains annotations only on the main\nchromosomes. Statistics for this build and information on how they were generated can be found on\nthe GENCODE site.\n\n
\n\n\nFor more information on the different gene tracks, see our Genes FAQ.
\n\n\nBy default, this track displays only the basic GENCODE set, splice variants, and non-coding genes.\nIt includes options to display the entire GENCODE set and pseudogenes. To customize these\noptions, the respective boxes can be checked or unchecked at the top of this description page. \n\n
\nThis track also includes a variety of labels which identify the transcripts when visibility is set\nto "full" or "pack". Gene symbols (e.g. NIPA1) are displayed by default, but\nadditional options include GENCODE Transcript ID (ENSMUST00000052204.6), UCSC Known Gene ID\n(uc009hdu.3), UniProt Display ID (Q8BHK1). Additional information about gene\nand transcript names can be found in our\nFAQ.
\n\n\nThis track, in general, follows the display conventions for gene prediction tracks. The exons for\nputative non-coding genes and untranslated regions are represented by relatively thin blocks, while\nthose for coding open reading frames are thicker. \n
Coloring for the gene annotations is based on the annotation type:
\n\nThis track contains an optional codon coloring feature that allows users to\nquickly validate and compare gene predictions. There is also an option to display the data as\na density graph, which\ncan be helpful for visualizing the distribution of items over a region.
\n\n\nThe GENCODE VM39 track was built from the GENCODE downloads comprehensive gene annotation (all regions) file \ngencode.vM39.chr_patch_hapl_scaff.annotation.gff3.gz. Data from other sources \nwere correlated with the GENCODE data to build association tables.
\nThe GENCODE Genes transcripts are annotated in numerous tables, each of which is also available as a\ndownloadable\nfile.\n\n
\nOne can see a full list of the associated tables in the Table Browser by selecting GENCODE Genes from the track menu; this list\nis then available on the table menu.\n\n\n
\nGENCODE Genes and its associated tables can be explored interactively using the\nREST API, the\nTable Browser or the\nData Integrator. \nThe genePred format files for mm39 are available from our \n\ndownloads directory or in our\n\nGTF download directory. \nAll the tables can also be queried directly from our public MySQL\nservers, with more information available on our\nhelp page as well as on\nour blog.
\n\n\nThe GENCODE Genes track was produced at UCSC from the GENCODE comprehensive gene set using a\ncomputational pipeline developed by Jim Kent and Brian Raney. This version of the track\nwas generated by Jonathan Casper.
\n\n\n\nMudge JM, Carbonell-Sala S, Diekhans M, Martinez JG, Hunt T, Jungreis I, Loveland JE, Arnan C,\nBarnes I, Bennett R et al.\n\nGENCODE 2025: reference gene annotation for human and mouse.\nNucleic Acids Res. 2025 Jan 6;53(D1):D966-D975.\nPMID: 39565199; PMC:
A full list of GENCODE publications is available\nat The GENCODE\nProject web site.\n
\n\nGENCODE data are available for use without restrictions.
\n", "idXref": "kgAlias kgID alias", "intronGap": "12", "isGencode3": "on", "itemRgb": "on", "labelFields": "geneName,name,geneName2,name2", "longLabel": "GENCODE VM39", "maxItems": "50000", "priority": "1", "searchIndex": "name", "shortLabel": "GENCODE VM39", "track": "knownGene", "type": "bigGenePred", "visibility": "pack" } }, "description": "GENCODE VM39", "category": ["Genes and Gene Predictions"], "displays": [ { "type": "LinearBasicDisplay", "displayId": "mm39-knownGene-LinearBasicDisplay", "labels": { "name": "jexl:get(feature,'name')" } } ] }, { "trackId": "mm39-jaspar2026", "name": "JASPAR Transcription Factors - JASPAR 2026 TFBS", "type": "FeatureTrack", "assemblyNames": ["mm39"], "adapter": { "type": "BigBedAdapter", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/mm39/jaspar/JASPAR2026.bb" }, "metadata": { "ucsc": { "bigDataUrl": "/gbdb/mm39/jaspar/JASPAR2026.bb", "filter.score": "400", "filterByRange.score": "0:1000", "filterValues.TFName": "Ahr::Arnt,Alx1,ALX3,Alx4,Ar,ARGFX,Arid3a,Arid3b,Arid5a,Arnt,ARNT2,ARNT::HIF1A,Arntl,Arx,ASCL1,Ascl2,Atf1,ATF2,Atf3,ATF3,ATF4,ATF6,ATF7,Atoh1,ATOH7,BACH1,Bach1::Mafk,BACH2,Banp,BARHL1,BARHL2,BARX1,BARX2,BATF,BATF3,BATF::JUN,BCL11A,Bcl11B,BCL6,BCL6B,Bhlha15,BHLHA15,BHLHE22,BHLHE23,BHLHE40,BHLHE41,BNC2,BSX,CASZ1,CDX1,CDX2,CDX4,CEBPA,CEBPB,CEBPD,CEBPE,CEBPG,CGGBP1,CLOCK,CREB1,CREB3,CREB3L1,Creb3l2,CREB3L3,CREB3L4,Creb5,CREM,Crx,CTCF,CTCFL,CUX1,CUX2,DBP,Ddit3::Cebpa,DLX1,Dlx2,Dlx3,Dlx4,Dlx5,DLX6,Dmbx1,Dmrt1,DMRT3,DMRTA1,DMRTA2,Dmrtb1,DMRTC2,DMTF1,DNTTIP1,DPRX,DRGX,Dux,DUX4,DUXA,Duxbl1,E2F1,E2F2,E2F3,E2F4,E2F6,E2F7,E2F8,EBF1,Ebf2,EBF3,Ebf4,EGR1,EGR2,EGR3,EGR4,EHF,ELF1,ELF2,ELF3,ELF4,Elf5,ELK1,ELK1::HOXA1,ELK1::HOXB13,ELK1::SREBF2,ELK3,ELK4,EMX1,EMX2,EN1,EN2,EOMES,EPAS1,ERF,ERF::FIGLA,ERF::FOXI1,ERF::FOXO1,ERF::HOXB13,ERF::NHLH1,ERF::SREBF2,Erg,ESR1,ESR2,ESRRA,ESRRB,Esrrg,ESX1,ETS1,ETS2,ETV1,ETV2,ETV2::DRGX,ETV2::FIGLA,ETV2::FOXI1,ETV2::HOXB13,ETV3,ETV4,ETV5,ETV5::DRGX,ETV5::FIGLA,ETV5::FOXI1,ETV5::FOXO1,ETV5::HOXA2,ETV6,ETV7,EVX1,EVX2,EWSR1-FLI1,FAM200B,FERD3L,FEV,FEZF2,FIGLA,FIZ1,FLI1,FLI1::DRGX,FLI1::FOXI1,FLYWCH1,FOS,Fosb,FOSB::JUN,FOSB::JUNB,FOS::JUN,FOS::JUNB,FOS::JUND,FOSL1,FOSL1::JUN,FOSL1::JUNB,FOSL1::JUND,FOSL2,FOSL2::JUN,FOSL2::JUNB,FOSL2::JUND,FOXA1,FOXA2,FOXA3,FOXB1,FOXC1,FOXC2,FOXD1,FOXD2,FOXD3,FOXE1,Foxf1,FOXF2,FOXG1,FOXH1,FOXI1,Foxj2,FOXJ2::ELF1,Foxj3,FOXK1,FOXK2,FOXL1,Foxl2,FOXM1,Foxn1,FOXN3,Foxo1,FOXO1::ELF1,FOXO1::ELK1,FOXO1::ELK3,FOXO1::FLI1,Foxo3,FOXO4,FOXO6,FOXP1,FOXP2,FOXP3,FOXP4,Foxq1,FOXS1,GABPA,GATA1,GATA1::TAL1,GATA2,Gata3,GATA4,GATA5,GATA6,GBX1,GBX2,GCM1,GCM2,GFI1,Gfi1B,Gli1,Gli2,GLI3,GLIS1,GLIS2,GLIS3,Gmeb1,GMEB2,GRHL1,GRHL2,GSC,GSC2,GSC::TBX21,GSX1,GSX2,Hand1,Hand1::Tcf3,HAND2,HELT,HES1,HES2,HES5,HES6,HES7,HESX1,HEY1,HEY2,Hic1,HIC2,HIF1A,HIF3A,HINFP,HLF,HMBOX1,Hmga1,Hmx1,Hmx2,Hmx3,Hnf1A,HNF1A,HNF1B,HNF4A,HNF4G,HOMEZ,HOXA1,HOXA10,Hoxa11,Hoxa13,HOXA2,HOXA3,HOXA4,HOXA5,HOXA6,HOXA7,HOXA9,HOXB1,HOXB13,HOXB2,HOXB2::ELK1,HOXB3,HOXB4,HOXB5,HOXB6,HOXB7,HOXB8,HOXB9,HOXC10,HOXC11,HOXC12,HOXC13,HOXC4,HOXC8,HOXC9,HOXD1,HOXD10,HOXD11,HOXD12,HOXD12::ELK1,Hoxd13,HOXD3,HOXD4,HOXD8,HOXD9,HSF1,HSF2,HSF4,HSF5,IKZF1,IKZF2,Ikzf3,INSM1,Irf1,IRF2,IRF3,IRF4,IRF5,IRF6,IRF7,IRF8,IRF9,IRX1,IRX2,IRX5,Isl1,ISL2,ISX,JDP2,Jun,JUN,JUNB,JUND,JUN::JUNB,KLF1,KLF10,KLF11,KLF12,KLF13,KLF14,KLF15,KLF16,KLF17,KLF2,KLF3,KLF4,KLF5,KLF6,KLF7,KLF8,KLF9,LBX1,LBX2,Lef1,LEUTX,Lhx1,LHX2,Lhx3,Lhx4,LHX5,LHX6,Lhx8,LHX9,LIN54,LMX1A,LMX1B,MAF,MAFA,Mafb,MAFF,Mafg,MAFG::NRF1,MAFK,MAF::NFE2,MAX,MAX::MYC,MAZ,Mecom,MEF2A,MEF2B,MEF2C,MEF2D,MEIS1,MEIS2,MEIS3,MEOX1,MEOX2,MGA,MGA::EVX1,MITF,mix-a,MIXL1,MKX,MLX,Mlxip,MLXIPL,MNT,MNX1,MSANTD1,MSANTD3,MSANTD4,MSC,Msgn1,MSX1,MSX2,Msx3,MTF1,MXI1,MYB,MYBL1,MYBL2,MYC,MYCN,MYF5,MYF6,MYOD1,MYOG,MYPOP,MYT1,MYT1L,MZF1,NACC2,Nanog,NEUROD1,Neurod2,NEUROG1,NEUROG2,Nfat5,Nfatc1,Nfatc2,NFATC3,NFATC4,NFE2,Nfe2l2,NFIA,NFIB,NFIC,NFIC::TLX1,NFIL3,NFIX,NFKB1,NFKB2,NFYA,NFYB,NFYC,NHLH1,NHLH2,Nkx2-1,NKX2-2,NKX2-3,NKX2-4,NKX2-5,NKX2-8,Nkx3-1,Nkx3-2,NKX6-1,NKX6-2,NKX6-3,Nobox,NOTO,Npas2,NPAS3,Npas4,NR1D1,NR1D2,Nr1H2,NR1H2::RXRA,Nr1h3,Nr1h3::Rxra,Nr1H4,NR1H4::RXRA,NR1I2,NR1I3,NR2C1,NR2C2,Nr2e1,Nr2e3,NR2F1,NR2F2,Nr2f6,Nr2F6,NR2F6,NR3C1,NR3C2,NR4A1,NR4A2,NR4A2::RXRA,Nr5a1,Nr5a2,NR6A1,Nrf1,NRL,OLIG1,Olig2,OLIG2,OLIG3,ONECUT1,ONECUT2,ONECUT3,OSR1,OSR2,OTX1,OTX2,OVOL1,OVOL2,PATZ1,PAX1,PAX2,PAX3,PAX4,PAX5,PAX6,Pax7,PAX8,PAX9,PBX1,PBX2,PBX3,PDX1,Pgr,PGR,PHOX2A,PHOX2B,PITX1,PITX2,PITX3,PKNOX1,PKNOX2,PLAG1,Plagl1,PLAGL2,POGK,POU1F1,POU2F1,POU2F1::SOX2,POU2F2,POU2F3,POU3F1,POU3F2,POU3F3,POU3F4,POU4F1,POU4F2,POU4F3,POU5F1,POU5F1B,Pou5f1::Sox2,POU6F1,POU6F2,Ppara,PPARA::RXRA,PPARD,PPARG,Pparg::Rxra,PRDM1,PRDM13,Prdm14,Prdm15,Prdm4,Prdm5,PRDM9,PROP1,PROX1,PRRX1,PRRX2,Ptf1A,RARA,RARA::RXRA,RARA::RXRG,Rarb,RARB,Rarg,RARG,RAX,RAX2,RBPJ,REL,RELA,RELB,REST,RFX1,RFX2,RFX3,RFX4,RFX5,Rfx6,RFX7,Rhox11,RHOXF1,RORA,RORB,RORC,RREB1,Runx1,RUNX2,RUNX3,Rxra,RXRA::VDR,RXRB,RXRG,SALL3,SATB1,SCAND3,SCRT1,SCRT2,SHOX,Shox2,SIX1,SIX2,Six3,Six4,SLC2A4RG,SMAD2,SMAD3,Smad4,SMAD5,SNAI1,SNAI2,SNAI3,SOHLH2,Sox1,SOX10,Sox11,SOX12,SOX13,SOX14,SOX15,Sox17,SOX18,SOX2,SOX21,Sox3,SOX30,SOX4,Sox5,Sox6,Sox7,SOX8,SOX9,SP1,SP140L,SP2,SP3,SP4,SP5,SP8,SP9,SPDEF,Spi1,SPIB,SPIC,Spz1,SREBF1,SREBF2,SRF,SRY,STAT1,STAT1::STAT2,Stat2,STAT3,Stat4,Stat5a,Stat5a::Stat5b,Stat5b,Stat6,TAL1::TCF3,TBP,TBR1,TBX1,TBX15,TBX18,TBX19,TBX2,TBX20,TBX21,TBX3,TBX4,TBX5,Tbx6,TBXT,Tcf12,TCF12,Tcf21,TCF21,TCF3,TCF4,TCF7,TCF7L1,TCF7L2,TCFL5,TEAD1,TEAD2,TEAD3,TEAD4,TEF,TFAP2A,TFAP2B,TFAP2C,TFAP2E,TFAP4,TFAP4::ETV1,TFAP4::FLI1,TFCP2,Tfcp2l1,TFDP1,TFE3,TFEB,TFEC,TGIF1,TGIF2,TGIF2LX,TGIF2LY,THAP1,Thap11,THRA,THRB,TIGD3,TIGD4,TIGD5,TIGD7,TLX2,TLX3,TP53,TP63,TP73,TPRX1,TRPS1,TWIST1,Twist2,UNCX,USF1,USF2,USF3,VAX1,VAX2,Vdr,VENTX,VEZF1,VSX1,VSX2,Wt1,XBP1,Yy1,YY2,ZBED1,ZBED2,ZBED4,ZBED5,ZBTB11,ZBTB12,ZBTB14,ZBTB17,ZBTB18,Zbtb2,ZBTB21,ZBTB24,ZBTB26,ZBTB32,ZBTB33,ZBTB40,ZBTB41,ZBTB47,ZBTB48,ZBTB5,ZBTB6,ZBTB7A,ZBTB7B,ZBTB7C,ZBTB8A,ZBTB8B,ZEB1,ZEB2,ZFP14,ZFP28,ZFP3,Zfp335,ZFP42,ZFP57,Zfp809,Zfp961,ZFTA,Zfx,ZGLP1,ZIC1,Zic1::Zic2,Zic2,Zic3,ZIC4,ZIC5,ZIM3,ZKSCAN1,ZKSCAN3,ZKSCAN4,ZKSCAN5,ZNF121,ZNF131,ZNF134,ZNF135,ZNF136,ZNF140,ZNF142,ZNF143,ZNF146,ZNF148,ZNF157,ZNF16,ZNF175,ZNF18,ZNF184,ZNF189,ZNF20,ZNF211,ZNF213,ZNF214,ZNF215,ZNF226,ZNF234,ZNF24,ZNF250,ZNF251,ZNF257,ZNF260,ZNF263,ZNF274,ZNF275,ZNF281,ZNF282,ZNF286B,ZNF317,ZNF320,ZNF322,ZNF324,ZNF331,ZNF335,ZNF341,ZNF343,ZNF347,ZNF35,ZNF354A,ZNF354C,ZNF362,ZNF367,ZNF382,ZNF384,ZNF395,ZNF407,ZNF410,ZNF416,ZNF417,ZNF418,ZNF423,ZNF43,ZNF436,ZNF449,ZNF454,ZNF460,ZNF470,ZNF471,ZNF490,ZNF493,ZNF497,ZNF500,ZNF510,ZNF518B,ZNF524,ZNF528,ZNF530,ZNF536,ZNF547,ZNF549,ZNF551,ZNF558,ZNF568,ZNF57,ZNF574,ZNF582,ZNF596,ZNF606,ZNF610,ZNF623,ZNF648,ZNF652,ZNF66,ZNF667,ZNF669,ZNF672,ZNF675,ZNF676,ZNF677,ZNF678,ZNF680,ZNF682,ZNF683,ZNF684,ZNF689,ZNF692,ZNF696,ZNF699,ZNF70,ZNF700,ZNF701,ZNF707,ZNF708,ZNF721,ZNF724,ZNF726,ZNF728,ZNF732,ZNF740,ZNF746,ZNF75A,ZNF75D,ZNF76,ZNF766,ZNF768,ZNF770,ZNF772,ZNF773,ZNF775,ZNF780B,ZNF784,ZNF8,ZNF800,ZNF814,ZNF816,ZNF827,ZNF831,ZNF836,ZNF841,ZNF85,ZNF850,ZNF853,ZNF865,ZNF878,ZNF93,ZSCAN16,ZSCAN2,ZSCAN21,ZSCAN25,ZSCAN29,ZSCAN31,ZSCAN4,ZXDA,ZXDB,ZXDC", "labelFields": "TFName", "longLabel": "JASPAR CORE 2026 - Predicted Transcription Factor Binding Sites", "maxItems": "100000", "motifPwmTable": "hgFixed.jasparCore2026", "parent": "jaspar on", "priority": "1", "shortLabel": "JASPAR 2026 TFBS", "showCfg": "on", "track": "jaspar2026", "type": "bigBed 6 +", "visibility": "pack", "html": "" } }, "description": "JASPAR CORE 2026 - Predicted Transcription Factor Binding Sites", "category": ["Regulation"], "displays": [ { "type": "LinearBasicDisplay", "displayId": "mm39-jaspar2026-LinearBasicDisplay", "labels": { "name": "jexl:get(feature,'TFName')" }, "jexlFilters": [ "get(feature,'gbkey')!='Src'", "get(feature,'score') >= 400" ] } ] }, { "trackId": "mm39-ReMapDensity", "name": "ReMap ChIP-seq - ReMap density", "type": "QuantitativeTrack", "assemblyNames": ["mm39"], "adapter": { "type": "BigWigAdapter", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/mm39/reMap/reMapDensity2022.bw" }, "metadata": { "ucsc": { "autoScale": "on", "bigDataUrl": "/gbdb/mm39/reMap/reMapDensity2022.bw", "html": "\nThis track represents the ReMap Atlas of regulatory regions, which consists of a\nlarge-scale integrative analysis of all Public ChIP-seq data for transcriptional\nregulators from GEO, ArrayExpress, and ENCODE. \n
\n\n\nBelow is a schematic diagram of the types of regulatory regions: \n
\nThis release of ReMap (2022) presents the analysis of 5,505 quality controlled\nmouse ChIP-seq (n=7,317 before QCs) from public sources (GEO & ENCODE). Those\nChIP-seq data sets have been mapped to the GRCm38/mm10 mouse assembly. The data\nset is defined as a ChIP-seq experiment in a given series (e.g. GSE122715),\nfor a given TF (e.g. USF1), in a particular biological condition (i.e. cell\nline, tissue type, disease state, or experimental conditions; e.g. mESC).\nData sets were labeled by concatenating these three pieces of information, such\nas GSE122715.USF1.mESC.\n
\nThose merged analyses cover a total of 656 DNA-binding proteins\n(transcriptional regulators) such as a variety of transcription factors (TFs),\ntranscription co-activators (TCFs), and chromatin-remodeling factors (CRFs) for\n123 million peaks.\n
\n\n\nAvailable ENCODE ChIP-seq data sets for transcriptional regulators from the\nENCODE portal were processed with the\nstandardized ReMap pipeline. The list of ENCODE data was retrieved as FASTQ files from the\nENCODE portal\nusing filters. Metadata information in JSON format and FASTQ files were retrieved using the Python\nrequests module.\n
\n\n\nBoth Public and ENCODE data were processed similarly. Bowtie 2 (PMC3322381) (version 2.2.9) with options -end-to-end -sensitive was used to align all\nreads on the genome. Biological and technical\nreplicates for each unique combination of GSE/TF/Cell type or Biological condition\nwere used for peak calling. TFBS were identified using MACS2 peak-calling tool\n(PMC3120977) (version 2.1.1.2) in order to follow ENCODE ChIP-seq guidelines,\nwith stringent thresholds (MACS2 default thresholds, p-value: 1e-5). An input data\nset was used when available.\n
\n\n\n\nTo assess the quality of public data sets, a score was computed based on the\ncross-correlation and the FRiP (fraction of reads in peaks) metrics developed by\nthe ENCODE Consortium (https://genome.ucsc.edu/ENCODE/qualityMetrics.html). Two\nthresholds were defined for each of the two cross-correlation ratios (NSC,\nnormalized strand coefficient: 1.05 and 1.10; RSC, relative strand coefficient:\n0.8 and 1.0). Detailed descriptions of the ENCODE quality coefficients can be\nfound at https://genome.ucsc.edu/ENCODE/qualityMetrics.html. The\nphantompeak tools suite was used\n(https://code.google.com/p/phantompeakqualtools/) to compute\nRSC and NSC.\n
\n\nPlease refer to the ReMap 2022, 2020, and 2018 publications for more details\n(citation below).\n
\n\n\n\n\nReMap Atlas of regulatory regions data can be explored interactively with the\nTable Browser and cross-referenced with the \nData Integrator. For programmatic access,\nthe track can be accessed using the Genome Browser's\nREST API.\nReMap annotations can be downloaded from the\nGenome Browser's download server\nas a bigBed file. This compressed binary format can be remotely queried through\ncommand line utilities. Please note that some of the download files can be quite large.
\n\n\nIndividual BED files for specific TFs, cells/biotypes, or data sets can be\nfound and downloaded on the ReMap website.\n
\n\n\n\n\nChèneby J, Gheorghe M, Artufel M, Mathelier A, Ballester B.\n\nReMap 2018: an updated atlas of regulatory regions from an integrative analysis of DNA-binding ChIP-\nseq experiments.\nNucleic Acids Res. 2018 Jan 4;46(D1):D267-D275.\nPMID: 29126285; PMC:
\nChèneby J, Ménétrier Z, Mestdagh M, Rosnet T, Douida A, Rhalloussi W, Bergon A, Lopez\nF, Ballester B.\n\nReMap 2020: a database of regulatory regions from an integrative analysis of Human and Arabidopsis\nDNA-binding sequencing experiments.\nNucleic Acids Res. 2020 Jan 8;48(D1):D180-D188.\nPMID: 31665499; PMC:
\nGriffon A, Barbier Q, Dalino J, van Helden J, Spicuglia S, Ballester B.\n\nIntegrative analysis of public ChIP-seq experiments reveals a complex multi-cell regulatory\nlandscape.\nNucleic Acids Res. 2015 Feb 27;43(4):e27.\nPMID: 25477382; PMC:
\nHammal F, de Langen P, Bergon A, Lopez F, Ballester B.\n\nReMap 2022: a database of Human, Mouse, Drosophila and Arabidopsis regulatory regions from an\nintegrative analysis of DNA-binding sequencing experiments.\nNucleic Acids Res. 2022 Jan 7;50(D1):D316-D325.\nPMID: 34751401; PMC:
\nThis track contains mappings of single nucleotide variants\nand small insertions and deletions (indels)\nfrom the European Variation Archive\n(EVA)\nRelease 4 for the mouse mm39 genome. The dbSNP database at NCBI no longer\nhosts non-human variants.\n
\n\n\nVariants are shown as single tick marks at most zoom levels.\nWhen viewing the track at or near base-level resolution, the displayed\nwidth of the SNP variant corresponds to the width of the variant in the\nreference sequence. Insertions are indicated by a single tick mark displayed\nbetween two nucleotides, single nucleotide polymorphisms are displayed as the\nwidth of a single base, and multiple nucleotide variants are represented by a\nblock that spans two or more bases. The display is set to automatically collapse to \ndense visibility when there are more than 100k variants in the window. \nWhen the window size is more than 250k bp, the display is switched to density graph mode.\n
\n\n\nNavigation to an individual variant can be accomplished by typing or copying\nthe variant identifier (rsID) or the genomic coordinates into the Position/Search box on the \nBrowser.
\n\n\nA click on an item in the graphical display displays a page with data about\nthat variant. Data fields include the Reference and Alternate Alleles, the\nclass of the variant as reported by EVA, the source of the data, the amino acid\nchange, if any, and the functional class as determined by UCSC's Variant Annotation\nIntegrator.\n
\n\nVariants can be filtered using the track controls to show subsets of the \ndata by either EVA Sequence Ontology (SO) term, UCSC-generated functional effect, or\nby color, which bins the UCSC functional effects into general classes.
\n\n\nMousing over an item shows the ucscClass, which is the consequence according to the\nVariant Annotation Integrator, and\nthe aaChange when one is available, which is the change in amino acid in HGVS.p\nterms. Items may have multiple ucscClasses, which will all be shown in the mouse-over\nin a comma-separated list. Likewise, multiple HGVS.p terms may be shown for each rsID\nseparated by spaces describing all possible AA changes.
\n\nMultiple items may appear due to different variant predictions on multiple gene transcripts.\nFor all organisms the gene models used were the NCBI RefSeq curated when available, if not then \nensembl genes, or finally UCSC mappings of RefSeq if neither of the previous models was possible.\n
\n\n\nVariants are colored according to the most potentially deleterious functional effect prediction\naccording to the Variant Annotation Integrator. Specific bins can be seen in the Methods section\nbelow.\n
\n\n\n
| Color | \nVariant Type | \n
|---|---|
| Protein-altering variants and splice site variants | |
| Synonymous codon variants | |
| Non-coding transcript or Untranslated Region (UTR) variants | |
| Intergenic and intronic variants |
\nVariants are classified by EVA into one of the following sequence ontology terms:\n
\n\n\nData were downloaded from the European Variation Archive EVA release 4 (2022-11-21)\ncurrent_ids.vcf.gz files corresponding to the proper assembly.
\n\nChromosome names were converted to UCSC-style\nand the variants passed through the\nVariant Annotation Integrator to\npredict consequence. For every organism the NCBI RefSeq curated models were used when available, \nfollowed by ensembl genes, and finally UCSC mapping of RefSeq when neither of the previous models\nwere possible.
\n\nVariants were then colored according to their predicted consequence in the following fashion:\n
\nSequence Ontology ("SO:")\nterms were converted to the variant classes, then the files were converted to BED,\nand then bigBed format.\n
\n\nNo functional annotations were provided by the EVA (e.g., missense, nonsense, etc).\nThese were computed using UCSC's Variant Annotation Integrator (Hinrichs, et al., 2016).\nAmino-acid substitutions for missense variants are based\non RefSeq alignments of mRNA transcripts, which do not always match the amino acids\npredicted from translating the genomic sequence. Therefore, in some instances, the\nvariant and the genomic nucleotide and associated amino acid may be reversed.\nE.g., a Pro > Arg change from the perspective of the mRNA would be Arg > Pro from\nthe persepective the genomic sequence. Also, in bosTau9, galGal5, rheMac8, \ndanRer10 and danRer11 the mitochondrial sequence was removed or renamed to match UCSC. \nFor complete documentation of the processing of these tracks, read the\n\nEVA Release 4 MakeDoc.
\n\n\nNote: It is not recommeneded to use LiftOver to convert SNPs between assemblies,\nand more information about how to convert SNPs between assemblies can be found on the following\nFAQ entry.
\n\nThe data can be explored interactively with the Table Browser,\nor the Data Integrator. For automated analysis, the data may be\nqueried from our REST API. Please refer to our\nmailing list archives\nfor questions, or our Data Access FAQ for more\ninformation.
\n\n\nFor automated download and analysis, this annotation is stored in a bigBed file that\ncan be downloaded from our download server. The file for this track is called evaSnp4.bb.\nIndividual regions or the whole genome annotation can be obtained using our tool\nbigBedToBed which can be compiled from the source code or downloaded as a precompiled\nbinary for your system. Instructions for downloading source code and binaries can be found\nhere.\nThe tool can also be used to obtain only features within a given range, e.g.\n
\nbigBedToBed https://hgdownload.soe.ucsc.edu/gbdb/mm39/bbi/evaSnp4.bb -chrom=chr21 -start=0 -end=100000000 stdout\n
\nThis track was produced from the European\nVariation Archive release 4 data. Consequences were predicted using UCSC's Variant Annotation\nIntegrator and NCBI's RefSeq as well as ensembl gene models. \n
\n\n\nCezard T, Cunningham F, Hunt SE, Koylass B, Kumar N, Saunders G, Shen A, Silva AF,\nTsukanov K, Venkataraman S et al. The European Variation Archive: a FAIR resource of genomic variation for all\nspecies. Nucleic Acids Res. 2021 Oct 28:gkab960.\ndoi:10.1093/nar/gkab960.\nEpub ahead of print. PMID: 34718739. PMID: PMC8728205.\n
\n\nHinrichs AS, Raney BJ, Speir ML, Rhead B, Casper J, Karolchik D, Kuhn RM, Rosenbloom KR, Zweig AS,\nHaussler D, Kent WJ.\nUCSC Data Integrator and Variant Annotation Integrator.\nBioinformatics. 2016 May 1;32(9):1430-2.\nPMID: 26740527; PMC:\nPMC4848401\n
\n" } }, "description": "Short Genetic Variants from European Variant Archive Release 4", "category": ["Variation and Repeats"], "displays": [ { "type": "LinearBasicDisplay", "displayId": "mm39-evaSnp4-LinearBasicDisplay", "mouseover": "jexl:`Ref/Alt allele(s): ${get(feature,'ref')}>${get(feature,'alt')}\nThe RefSeq mRNAs gene track for the mouse (Jun. 2020 (GRCm39/mm39))\ngenome assembly displays translated blat alignments of vertebrate and\ninvertebrate mRNA in\n GenBank.\n
\n\n\nTotal genome size: 2,654,624,157 (not counting gaps)
\nGene count: 22,442
\nBases in genes: 838,462,469 (txStart to txEnd)
\nGenes percent genome coverage: % 31.585
\nBases in exons: 53,564,706
\nExons percent genome coverage: % 2.018
\n
\nPlease note, the name searching system is not completely case insensitive.\nWhen in doubt, enter search names in all lower case to find gene names.\n
\n\n\nThe mRNAs were aligned against the mouse (Jun. 2020 (GRCm39/mm39)) genome using\ntranslated blat. When a single mRNA aligned in multiple places, the alignment\nhaving the highest base identity was found. Only those alignments having a base\nidentity level within 1% of the best and at least 25% base identity with the\ngenomic sequence were kept.\n
\n\n\nSpecifically, the translated blat command is:\n
\nblat -noHead -q=rnax -t=dnax -mask=lower target.fa query.fa target.query.psl\n\nwhere target.fa is one of the chromosome sequence of the genome assembly,\nand the query.fa is the mRNAs from RefSeq\n\nThe resulting PSL outputs are filtered:\n
\npslCDnaFilter -minId=0.35 -minCover=0.25 -globalNearBest=0.0100 -minQSize=20 \n -ignoreIntrons -repsAsMatch -ignoreNs -bestOverlap \n all.results.psl mm39.xenoRefGene.psl\n\nThe filtered mm39.xenoRefGene.psl is converted to\ngenePred data to display for this track.\n\n\n
\nThe mRNA track was produced at UCSC from mRNA sequence data\nsubmitted to the international public sequence databases by\nscientists worldwide.\n
\n\n\nBenson DA, Cavanaugh M, Clark K, Karsch-Mizrachi I, Lipman DJ, Ostell J, Sayers EW.\n\nGenBank.\nNucleic Acids Res. 2013 Jan;41(Database issue):D36-42.\nPMID: 23193287; PMC:
\nBenson DA, Karsch-Mizrachi I, Lipman DJ, Ostell J, Wheeler DL.\nGenBank: update.\nNucleic Acids Res. 2004 Jan 1;32(Database issue):D23-6.\nPMID: 14681350; PMC:
\nKent WJ.\nBLAT - the BLAST-like alignment tool.\nGenome Res. 2002 Apr;12(4):656-64.\nPMID: 11932250; PMC:
\nThis track represents the ReMap Atlas of regulatory regions, which consists of a\nlarge-scale integrative analysis of all Public ChIP-seq data for transcriptional\nregulators from GEO, ArrayExpress, and ENCODE. \n
\n\n\nBelow is a schematic diagram of the types of regulatory regions: \n
\nThis release of ReMap (2022) presents the analysis of 5,505 quality controlled\nmouse ChIP-seq (n=7,317 before QCs) from public sources (GEO & ENCODE). Those\nChIP-seq data sets have been mapped to the GRCm38/mm10 mouse assembly. The data\nset is defined as a ChIP-seq experiment in a given series (e.g. GSE122715),\nfor a given TF (e.g. USF1), in a particular biological condition (i.e. cell\nline, tissue type, disease state, or experimental conditions; e.g. mESC).\nData sets were labeled by concatenating these three pieces of information, such\nas GSE122715.USF1.mESC.\n
\nThose merged analyses cover a total of 656 DNA-binding proteins\n(transcriptional regulators) such as a variety of transcription factors (TFs),\ntranscription co-activators (TCFs), and chromatin-remodeling factors (CRFs) for\n123 million peaks.\n
\n\n\nAvailable ENCODE ChIP-seq data sets for transcriptional regulators from the\nENCODE portal were processed with the\nstandardized ReMap pipeline. The list of ENCODE data was retrieved as FASTQ files from the\nENCODE portal\nusing filters. Metadata information in JSON format and FASTQ files were retrieved using the Python\nrequests module.\n
\n\n\nBoth Public and ENCODE data were processed similarly. Bowtie 2 (PMC3322381) (version 2.2.9) with options -end-to-end -sensitive was used to align all\nreads on the genome. Biological and technical\nreplicates for each unique combination of GSE/TF/Cell type or Biological condition\nwere used for peak calling. TFBS were identified using MACS2 peak-calling tool\n(PMC3120977) (version 2.1.1.2) in order to follow ENCODE ChIP-seq guidelines,\nwith stringent thresholds (MACS2 default thresholds, p-value: 1e-5). An input data\nset was used when available.\n
\n\n\n\nTo assess the quality of public data sets, a score was computed based on the\ncross-correlation and the FRiP (fraction of reads in peaks) metrics developed by\nthe ENCODE Consortium (https://genome.ucsc.edu/ENCODE/qualityMetrics.html). Two\nthresholds were defined for each of the two cross-correlation ratios (NSC,\nnormalized strand coefficient: 1.05 and 1.10; RSC, relative strand coefficient:\n0.8 and 1.0). Detailed descriptions of the ENCODE quality coefficients can be\nfound at https://genome.ucsc.edu/ENCODE/qualityMetrics.html. The\nphantompeak tools suite was used\n(https://code.google.com/p/phantompeakqualtools/) to compute\nRSC and NSC.\n
\n\nPlease refer to the ReMap 2022, 2020, and 2018 publications for more details\n(citation below).\n
\n\n\n\n\nReMap Atlas of regulatory regions data can be explored interactively with the\nTable Browser and cross-referenced with the \nData Integrator. For programmatic access,\nthe track can be accessed using the Genome Browser's\nREST API.\nReMap annotations can be downloaded from the\nGenome Browser's download server\nas a bigBed file. This compressed binary format can be remotely queried through\ncommand line utilities. Please note that some of the download files can be quite large.
\n\n\nIndividual BED files for specific TFs, cells/biotypes, or data sets can be\nfound and downloaded on the ReMap website.\n
\n\n\n\n\nChèneby J, Gheorghe M, Artufel M, Mathelier A, Ballester B.\n\nReMap 2018: an updated atlas of regulatory regions from an integrative analysis of DNA-binding ChIP-\nseq experiments.\nNucleic Acids Res. 2018 Jan 4;46(D1):D267-D275.\nPMID: 29126285; PMC:
\nChèneby J, Ménétrier Z, Mestdagh M, Rosnet T, Douida A, Rhalloussi W, Bergon A, Lopez\nF, Ballester B.\n\nReMap 2020: a database of regulatory regions from an integrative analysis of Human and Arabidopsis\nDNA-binding sequencing experiments.\nNucleic Acids Res. 2020 Jan 8;48(D1):D180-D188.\nPMID: 31665499; PMC:
\nGriffon A, Barbier Q, Dalino J, van Helden J, Spicuglia S, Ballester B.\n\nIntegrative analysis of public ChIP-seq experiments reveals a complex multi-cell regulatory\nlandscape.\nNucleic Acids Res. 2015 Feb 27;43(4):e27.\nPMID: 25477382; PMC:
\nHammal F, de Langen P, Bergon A, Lopez F, Ballester B.\n\nReMap 2022: a database of Human, Mouse, Drosophila and Arabidopsis regulatory regions from an\nintegrative analysis of DNA-binding sequencing experiments.\nNucleic Acids Res. 2022 Jan 7;50(D1):D316-D325.\nPMID: 34751401; PMC:
\nThis track contains mappings of single nucleotide variants\nand small insertions and deletions (indels)\nfrom the European Variation Archive\n(EVA)\nRelease 5 for the mouse mm39 genome. The dbSNP database at NCBI no longer\nhosts non-human variants.\n
\n\n\nVariants are shown as single tick marks at most zoom levels.\nWhen viewing the track at or near base-level resolution, the displayed\nwidth of the SNP variant corresponds to the width of the variant in the\nreference sequence. Insertions are indicated by a single tick mark displayed\nbetween two nucleotides, single nucleotide polymorphisms are displayed as the\nwidth of a single base, and multiple nucleotide variants are represented by a\nblock that spans two or more bases. The display is set to automatically collapse to \ndense visibility when there are more than 100k variants in the window. \nWhen the window size is more than 250k bp, the display is switched to density graph mode.\n
\n\n\nNavigation to an individual variant can be accomplished by typing or copying\nthe variant identifier (rsID) or the genomic coordinates into the Position/Search box on the \nBrowser.
\n\n\nA click on an item in the graphical display displays a page with data about\nthat variant. Data fields include the Reference and Alternate Alleles, the\nclass of the variant as reported by EVA, the source of the data, the amino acid\nchange, if any, and the functional class as determined by UCSC's Variant Annotation\nIntegrator.\n
\n\nVariants can be filtered using the track controls to show subsets of the \ndata by either EVA Sequence Ontology (SO) term, UCSC-generated functional effect, or\nby color, which bins the UCSC functional effects into general classes.
\n\n\nMousing over an item shows the ucscClass, which is the consequence according to the\nVariant Annotation Integrator, and\nthe aaChange when one is available, which is the change in amino acid in HGVS.p\nterms. Items may have multiple ucscClasses, which will all be shown in the mouse-over\nin a comma-separated list. Likewise, multiple HGVS.p terms may be shown for each rsID\nseparated by spaces describing all possible AA changes.
\n\nMultiple items may appear due to different variant predictions on multiple gene transcripts.\nFor all organisms the gene models used were the NCBI RefSeq curated when available, if not then \nensembl genes, or finally UCSC mappings of RefSeq if neither of the previous models was possible.\n
\n\n\nVariants are colored according to the most potentially deleterious functional effect prediction\naccording to the Variant Annotation Integrator. Specific bins can be seen in the Methods section\nbelow.\n
\n\n\n
| Color | \nVariant Type | \n
|---|---|
| Protein-altering variants and splice site variants | |
| Synonymous codon variants | |
| Non-coding transcript or Untranslated Region (UTR) variants | |
| Intergenic and intronic variants |
\nVariants are classified by EVA into one of the following sequence ontology terms:\n
\n\n\nData were downloaded from the European Variation Archive EVA release 5 (2023-9-7)\ncurrent_ids.vcf.gz files corresponding to the proper assembly.
\n\nChromosome names were converted to UCSC-style\nand the variants passed through the\nVariant Annotation Integrator to\npredict consequence. For every organism the NCBI RefSeq curated models were used when available, \nfollowed by ensembl genes, and finally UCSC mapping of RefSeq when neither of the previous models\nwere possible.
\n\nVariants were then colored according to their predicted consequence in the following fashion:\n
\nSequence Ontology ("SO:")\nterms were converted to the variant classes, then the files were converted to BED,\nand then bigBed format.\n
\n\nNo functional annotations were provided by the EVA (e.g., missense, nonsense, etc).\nThese were computed using UCSC's Variant Annotation Integrator (Hinrichs, et al., 2016).\nAmino-acid substitutions for missense variants are based\non RefSeq alignments of mRNA transcripts, which do not always match the amino acids\npredicted from translating the genomic sequence. Therefore, in some instances, the\nvariant and the genomic nucleotide and associated amino acid may be reversed.\nE.g., a Pro > Arg change from the perspective of the mRNA would be Arg > Pro from\nthe persepective the genomic sequence. Also, in bosTau9, galGal5, rheMac8, \ndanRer10 and danRer11 the mitochondrial sequence was removed or renamed to match UCSC. \nFor complete documentation of the processing of these tracks, read the\n\nEVA Release 5 MakeDoc.
\n\n\nNote: It is not recommeneded to use LiftOver to convert SNPs between assemblies,\nand more information about how to convert SNPs between assemblies can be found on the following\nFAQ entry.
\n\nThe data can be explored interactively with the Table Browser,\nor the Data Integrator. For automated analysis, the data may be\nqueried from our REST API. Please refer to our\nmailing list archives\nfor questions, or our Data Access FAQ for more\ninformation.
\n\n\nFor automated download and analysis, this annotation is stored in a bigBed file that\ncan be downloaded from our download server. The file for this track is called evaSnp5.bb.\nIndividual regions or the whole genome annotation can be obtained using our tool\nbigBedToBed which can be compiled from the source code or downloaded as a precompiled\nbinary for your system. Instructions for downloading source code and binaries can be found\nhere.\nThe tool can also be used to obtain only features within a given range, e.g.\n
\nbigBedToBed https://hgdownload.soe.ucsc.edu/gbdb/mm39/bbi/evaSnp5.bb -chrom=chr21 -start=0 -end=100000000 stdout\n
\nThis track was produced from the European\nVariation Archive release 5 data. Consequences were predicted using UCSC's Variant Annotation\nIntegrator and NCBI's RefSeq as well as ensembl gene models. \n
\n\n\nCezard T, Cunningham F, Hunt SE, Koylass B, Kumar N, Saunders G, Shen A, Silva AF,\nTsukanov K, Venkataraman S et al. The European Variation Archive: a FAIR resource of genomic variation for all\nspecies. Nucleic Acids Res. 2021 Oct 28:gkab960.\ndoi:10.1093/nar/gkab960.\nEpub ahead of print. PMID: 34718739. PMID: PMC8728205.\n
\n\nHinrichs AS, Raney BJ, Speir ML, Rhead B, Casper J, Karolchik D, Kuhn RM, Rosenbloom KR, Zweig AS,\nHaussler D, Kent WJ.\nUCSC Data Integrator and Variant Annotation Integrator.\nBioinformatics. 2016 May 1;32(9):1430-2.\nPMID: 26740527; PMC:\nPMC4848401\n
\n" } }, "description": "Short Genetic Variants from European Variant Archive Release 5", "category": ["Variation and Repeats"], "displays": [ { "type": "LinearBasicDisplay", "displayId": "mm39-evaSnp5-LinearBasicDisplay", "mouseover": "jexl:`Ref/Alt allele(s): ${get(feature,'ref')}>${get(feature,'alt')}\nThese tracks contain mappings of single nucleotide variants\nand small insertions and deletions (indels)\nfrom the European Variation Archive\n(EVA) \nfor the mouse mm39 genome. The dbSNP database at NCBI no longer\nhosts non-human variants.\n
\n\n\nVariants are shown as single tick marks at most zoom levels.\nWhen viewing the track at or near base-level resolution, the displayed\nwidth of the SNP variant corresponds to the width of the variant in the\nreference sequence. Insertions are indicated by a single tick mark displayed\nbetween two nucleotides, single nucleotide polymorphisms are displayed as the\nwidth of a single base, and multiple nucleotide variants are represented by a\nblock that spans two or more bases. The display is set to automatically collapse to \ndense visibility when there are more than 100k variants in the window. \nWhen the window size is more than 250k bp, the display is switched to density graph mode.\n
\n\n\nNavigation to an individual variant can be accomplished by typing or copying\nthe variant identifier (rsID) or the genomic coordinates into the Position/Search box on the \nBrowser.
\n\n\nA click on an item in the graphical display displays a page with data about\nthat variant. Data fields include the Reference and Alternate Alleles, the\nclass of the variant as reported by EVA, the source of the data, the amino acid\nchange, if any, and the functional class as determined by UCSC's Variant Annotation\nIntegrator.\n
\n\nVariants can be filtered using the track controls to show subsets of the \ndata by either EVA Sequence Ontology (SO) term, UCSC-generated functional effect, or\nby color, which bins the UCSC functional effects into general classes.
\n\n\nMousing over an item shows the ucscClass, which is the consequence according to the\nVariant Annotation Integrator, and\nthe aaChange when one is available, which is the change in amino acid in HGVS.p\nterms.
\n\nStarting with EVA SNP Release 9, each variant is annotated with the single\nmost-severe consequence ranked by Sequence Ontology severity, so the ucscClass\nfield contains one term per variant. In releases 3 through 8, the ucscClass\nfield instead contained a comma-separated list of every consequence predicted\nacross overlapping transcripts. Multiple HGVS.p terms may still appear for a\nsingle rsID when more than one transcript yields a non-equivalent protein\nchange.
\n\nFor all organisms, the gene models used were the NCBI RefSeq curated when available, if not, then\nensembl genes, or finally, UCSC mappings of RefSeq if neither of the previous models was possible.\n
\n\n\nVariants are colored according to the most potentially deleterious functional effect prediction\naccording to the Variant Annotation Integrator. Specific bins can be seen in the Methods section\nbelow.\n
\n\n\n
| Color | \nVariant Type | \n
|---|---|
| Protein-altering variants and splice site variants | |
| Synonymous codon variants | |
| Non-coding transcript or Untranslated Region (UTR) variants | |
| Intergenic and intronic variants |
\nStarting with EVA SNP Release 9, the variant class labels stored in each\nbigBed and exposed by the Variant Class filter follow the short\nSequence Ontology names used by modern VCF and dbSNP exports\n(SNV, deletion, insertion, indel, MNV,\nsequence_alteration). Releases 3 through 8 retain their original\nlabels (substitution, delins, multipleNucleotideVariant,\nsequence alteration) because those bigBeds were built before the\nlabel refresh. The underlying SO terms and the biology they describe are\nunchanged; only the displayed names differ.
\n\n\nVariants are classified by EVA into one of the following sequence ontology terms:\n
\n\n\nData were downloaded from the European Variation Archive EVA\ncurrent_ids.vcf.gz files corresponding to the proper assembly.
\n\nChromosome names were converted to UCSC-style,\nand the variants were passed through the\nVariant Annotation Integrator to\npredict consequence. For every organism, the NCBI RefSeq curated models were used when available,\nfollowed by ensembl genes, and finally UCSC mapping of RefSeq when neither of the previous models\nwere possible.
\n\nVariants were then colored according to their predicted consequence in the following fashion:\n
\nSequence Ontology ("SO:")\nterms were converted to the variant classes, then the files were converted to BED,\nand then to bigBed format.\n
\n\nNo functional annotations were provided by the EVA (e.g., missense, nonsense, etc).\nThese were computed using UCSC's Variant Annotation Integrator (Hinrichs, et al., 2016).\nAmino-acid substitutions for missense variants are based\non RefSeq alignments of mRNA transcripts, which do not always match the amino acids\npredicted from translating the genomic sequence. Therefore, in some instances, the\nvariant and the genomic nucleotide and associated amino acid may be reversed.\nE.g., a Pro > Arg change from the perspective of the mRNA would be Arg > Pro from\nthe perspective of the genomic sequence. Also, in bosTau9, galGal5, rheMac10,\nand danRer11, the mitochondrial sequence was removed or renamed to match UCSC.\nFor complete documentation of the processing of these tracks, see the makedoc corresponding\nto the version of interest. For example, the\n\nEVA Release 9 MakeDoc.
\n\n\nNote: It is not recommended to use LiftOver to convert SNPs between assemblies,\nand more information about how to convert SNPs between assemblies can be found on the following\nFAQ entry.
\n\nThe data can be explored interactively with the Table Browser\nor the Data Integrator. For automated analysis, the data may be\nqueried from our REST API. Please refer to our\nmailing list archives\nfor questions or our Data Access FAQ for more\ninformation.
\n\n\nFor automated download and analysis, this annotation is stored in a bigBed file that\ncan be downloaded from our download server. Use the corresponding version number for the track\nof interest, e.g., evaSnp9.bb.\nIndividual regions or the whole genome annotation can be obtained using our tool\nbigBedToBed, which can be compiled from the source code or downloaded as a precompiled\nbinary for your system. Instructions for downloading source code and binaries can be found\nhere.\nThe tool can also be used to obtain only features within a given range, e.g.\n
\nbigBedToBed https://hgdownload.soe.ucsc.edu/gbdb/mm39/bbi/evaSnp9.bb -chrom=chr21 -start=0 -end=100000000 stdout\n
\nThis track was produced from the European\nVariation Archive release data. Consequences were predicted using UCSC's Variant Annotation\nIntegrator and NCBI's RefSeq, as well as ensembl gene models.\n
\n\n\nCezard T, Cunningham F, Hunt SE, Koylass B, Kumar N, Saunders G, Shen A, Silva AF,\nTsukanov K, Venkataraman S et al. The European Variation Archive: a FAIR resource of genomic variation for all\nspecies. Nucleic Acids Res. 2021 Oct 28:gkab960.\ndoi:10.1093/nar/gkab960.\nEpub ahead of print. PMID: 34718739. PMID: PMC8728205.\n
\n\nHinrichs AS, Raney BJ, Speir ML, Rhead B, Casper J, Karolchik D, Kuhn RM, Rosenbloom KR, Zweig AS,\nHaussler D, Kent WJ.\nUCSC Data Integrator and Variant Annotation Integrator.\nBioinformatics. 2016 May 1;32(9):1430-2.\nPMID: 26740527; PMC:\nPMC4848401\n
\n", "itemRgb": "on", "longLabel": "Short Genetic Variants from European Variant Archive Release 6", "maxItems": "1000000", "maxWindowCoverage": "250000", "mouseOver": "Ref/Alt allele(s): $ref>$alt\nThese tracks contain mappings of single nucleotide variants\nand small insertions and deletions (indels)\nfrom the European Variation Archive\n(EVA) \nfor the mouse mm39 genome. The dbSNP database at NCBI no longer\nhosts non-human variants.\n
\n\n\nVariants are shown as single tick marks at most zoom levels.\nWhen viewing the track at or near base-level resolution, the displayed\nwidth of the SNP variant corresponds to the width of the variant in the\nreference sequence. Insertions are indicated by a single tick mark displayed\nbetween two nucleotides, single nucleotide polymorphisms are displayed as the\nwidth of a single base, and multiple nucleotide variants are represented by a\nblock that spans two or more bases. The display is set to automatically collapse to \ndense visibility when there are more than 100k variants in the window. \nWhen the window size is more than 250k bp, the display is switched to density graph mode.\n
\n\n\nNavigation to an individual variant can be accomplished by typing or copying\nthe variant identifier (rsID) or the genomic coordinates into the Position/Search box on the \nBrowser.
\n\n\nA click on an item in the graphical display displays a page with data about\nthat variant. Data fields include the Reference and Alternate Alleles, the\nclass of the variant as reported by EVA, the source of the data, the amino acid\nchange, if any, and the functional class as determined by UCSC's Variant Annotation\nIntegrator.\n
\n\nVariants can be filtered using the track controls to show subsets of the \ndata by either EVA Sequence Ontology (SO) term, UCSC-generated functional effect, or\nby color, which bins the UCSC functional effects into general classes.
\n\n\nMousing over an item shows the ucscClass, which is the consequence according to the\nVariant Annotation Integrator, and\nthe aaChange when one is available, which is the change in amino acid in HGVS.p\nterms.
\n\nStarting with EVA SNP Release 9, each variant is annotated with the single\nmost-severe consequence ranked by Sequence Ontology severity, so the ucscClass\nfield contains one term per variant. In releases 3 through 8, the ucscClass\nfield instead contained a comma-separated list of every consequence predicted\nacross overlapping transcripts. Multiple HGVS.p terms may still appear for a\nsingle rsID when more than one transcript yields a non-equivalent protein\nchange.
\n\nFor all organisms, the gene models used were the NCBI RefSeq curated when available, if not, then\nensembl genes, or finally, UCSC mappings of RefSeq if neither of the previous models was possible.\n
\n\n\nVariants are colored according to the most potentially deleterious functional effect prediction\naccording to the Variant Annotation Integrator. Specific bins can be seen in the Methods section\nbelow.\n
\n\n\n
| Color | \nVariant Type | \n
|---|---|
| Protein-altering variants and splice site variants | |
| Synonymous codon variants | |
| Non-coding transcript or Untranslated Region (UTR) variants | |
| Intergenic and intronic variants |
\nStarting with EVA SNP Release 9, the variant class labels stored in each\nbigBed and exposed by the Variant Class filter follow the short\nSequence Ontology names used by modern VCF and dbSNP exports\n(SNV, deletion, insertion, indel, MNV,\nsequence_alteration). Releases 3 through 8 retain their original\nlabels (substitution, delins, multipleNucleotideVariant,\nsequence alteration) because those bigBeds were built before the\nlabel refresh. The underlying SO terms and the biology they describe are\nunchanged; only the displayed names differ.
\n\n\nVariants are classified by EVA into one of the following sequence ontology terms:\n
\n\n\nData were downloaded from the European Variation Archive EVA\ncurrent_ids.vcf.gz files corresponding to the proper assembly.
\n\nChromosome names were converted to UCSC-style,\nand the variants were passed through the\nVariant Annotation Integrator to\npredict consequence. For every organism, the NCBI RefSeq curated models were used when available,\nfollowed by ensembl genes, and finally UCSC mapping of RefSeq when neither of the previous models\nwere possible.
\n\nVariants were then colored according to their predicted consequence in the following fashion:\n
\nSequence Ontology ("SO:")\nterms were converted to the variant classes, then the files were converted to BED,\nand then to bigBed format.\n
\n\nNo functional annotations were provided by the EVA (e.g., missense, nonsense, etc).\nThese were computed using UCSC's Variant Annotation Integrator (Hinrichs, et al., 2016).\nAmino-acid substitutions for missense variants are based\non RefSeq alignments of mRNA transcripts, which do not always match the amino acids\npredicted from translating the genomic sequence. Therefore, in some instances, the\nvariant and the genomic nucleotide and associated amino acid may be reversed.\nE.g., a Pro > Arg change from the perspective of the mRNA would be Arg > Pro from\nthe perspective of the genomic sequence. Also, in bosTau9, galGal5, rheMac10,\nand danRer11, the mitochondrial sequence was removed or renamed to match UCSC.\nFor complete documentation of the processing of these tracks, see the makedoc corresponding\nto the version of interest. For example, the\n\nEVA Release 9 MakeDoc.
\n\n\nNote: It is not recommended to use LiftOver to convert SNPs between assemblies,\nand more information about how to convert SNPs between assemblies can be found on the following\nFAQ entry.
\n\nThe data can be explored interactively with the Table Browser\nor the Data Integrator. For automated analysis, the data may be\nqueried from our REST API. Please refer to our\nmailing list archives\nfor questions or our Data Access FAQ for more\ninformation.
\n\n\nFor automated download and analysis, this annotation is stored in a bigBed file that\ncan be downloaded from our download server. Use the corresponding version number for the track\nof interest, e.g., evaSnp9.bb.\nIndividual regions or the whole genome annotation can be obtained using our tool\nbigBedToBed, which can be compiled from the source code or downloaded as a precompiled\nbinary for your system. Instructions for downloading source code and binaries can be found\nhere.\nThe tool can also be used to obtain only features within a given range, e.g.\n
\nbigBedToBed https://hgdownload.soe.ucsc.edu/gbdb/mm39/bbi/evaSnp9.bb -chrom=chr21 -start=0 -end=100000000 stdout\n
\nThis track was produced from the European\nVariation Archive release data. Consequences were predicted using UCSC's Variant Annotation\nIntegrator and NCBI's RefSeq, as well as ensembl gene models.\n
\n\n\nCezard T, Cunningham F, Hunt SE, Koylass B, Kumar N, Saunders G, Shen A, Silva AF,\nTsukanov K, Venkataraman S et al. The European Variation Archive: a FAIR resource of genomic variation for all\nspecies. Nucleic Acids Res. 2021 Oct 28:gkab960.\ndoi:10.1093/nar/gkab960.\nEpub ahead of print. PMID: 34718739. PMID: PMC8728205.\n
\n\nHinrichs AS, Raney BJ, Speir ML, Rhead B, Casper J, Karolchik D, Kuhn RM, Rosenbloom KR, Zweig AS,\nHaussler D, Kent WJ.\nUCSC Data Integrator and Variant Annotation Integrator.\nBioinformatics. 2016 May 1;32(9):1430-2.\nPMID: 26740527; PMC:\nPMC4848401\n
\n", "itemRgb": "on", "longLabel": "Short Genetic Variants from European Variant Archive Release 7", "maxItems": "1000000", "maxWindowCoverage": "250000", "mouseOver": "Ref/Alt allele(s): $ref>$alt\nThese tracks contain mappings of single nucleotide variants\nand small insertions and deletions (indels)\nfrom the European Variation Archive\n(EVA) \nfor the mouse mm39 genome. The dbSNP database at NCBI no longer\nhosts non-human variants.\n
\n\n\nVariants are shown as single tick marks at most zoom levels.\nWhen viewing the track at or near base-level resolution, the displayed\nwidth of the SNP variant corresponds to the width of the variant in the\nreference sequence. Insertions are indicated by a single tick mark displayed\nbetween two nucleotides, single nucleotide polymorphisms are displayed as the\nwidth of a single base, and multiple nucleotide variants are represented by a\nblock that spans two or more bases. The display is set to automatically collapse to \ndense visibility when there are more than 100k variants in the window. \nWhen the window size is more than 250k bp, the display is switched to density graph mode.\n
\n\n\nNavigation to an individual variant can be accomplished by typing or copying\nthe variant identifier (rsID) or the genomic coordinates into the Position/Search box on the \nBrowser.
\n\n\nA click on an item in the graphical display displays a page with data about\nthat variant. Data fields include the Reference and Alternate Alleles, the\nclass of the variant as reported by EVA, the source of the data, the amino acid\nchange, if any, and the functional class as determined by UCSC's Variant Annotation\nIntegrator.\n
\n\nVariants can be filtered using the track controls to show subsets of the \ndata by either EVA Sequence Ontology (SO) term, UCSC-generated functional effect, or\nby color, which bins the UCSC functional effects into general classes.
\n\n\nMousing over an item shows the ucscClass, which is the consequence according to the\nVariant Annotation Integrator, and\nthe aaChange when one is available, which is the change in amino acid in HGVS.p\nterms.
\n\nStarting with EVA SNP Release 9, each variant is annotated with the single\nmost-severe consequence ranked by Sequence Ontology severity, so the ucscClass\nfield contains one term per variant. In releases 3 through 8, the ucscClass\nfield instead contained a comma-separated list of every consequence predicted\nacross overlapping transcripts. Multiple HGVS.p terms may still appear for a\nsingle rsID when more than one transcript yields a non-equivalent protein\nchange.
\n\nFor all organisms, the gene models used were the NCBI RefSeq curated when available, if not, then\nensembl genes, or finally, UCSC mappings of RefSeq if neither of the previous models was possible.\n
\n\n\nVariants are colored according to the most potentially deleterious functional effect prediction\naccording to the Variant Annotation Integrator. Specific bins can be seen in the Methods section\nbelow.\n
\n\n\n
| Color | \nVariant Type | \n
|---|---|
| Protein-altering variants and splice site variants | |
| Synonymous codon variants | |
| Non-coding transcript or Untranslated Region (UTR) variants | |
| Intergenic and intronic variants |
\nStarting with EVA SNP Release 9, the variant class labels stored in each\nbigBed and exposed by the Variant Class filter follow the short\nSequence Ontology names used by modern VCF and dbSNP exports\n(SNV, deletion, insertion, indel, MNV,\nsequence_alteration). Releases 3 through 8 retain their original\nlabels (substitution, delins, multipleNucleotideVariant,\nsequence alteration) because those bigBeds were built before the\nlabel refresh. The underlying SO terms and the biology they describe are\nunchanged; only the displayed names differ.
\n\n\nVariants are classified by EVA into one of the following sequence ontology terms:\n
\n\n\nData were downloaded from the European Variation Archive EVA\ncurrent_ids.vcf.gz files corresponding to the proper assembly.
\n\nChromosome names were converted to UCSC-style,\nand the variants were passed through the\nVariant Annotation Integrator to\npredict consequence. For every organism, the NCBI RefSeq curated models were used when available,\nfollowed by ensembl genes, and finally UCSC mapping of RefSeq when neither of the previous models\nwere possible.
\n\nVariants were then colored according to their predicted consequence in the following fashion:\n
\nSequence Ontology ("SO:")\nterms were converted to the variant classes, then the files were converted to BED,\nand then to bigBed format.\n
\n\nNo functional annotations were provided by the EVA (e.g., missense, nonsense, etc).\nThese were computed using UCSC's Variant Annotation Integrator (Hinrichs, et al., 2016).\nAmino-acid substitutions for missense variants are based\non RefSeq alignments of mRNA transcripts, which do not always match the amino acids\npredicted from translating the genomic sequence. Therefore, in some instances, the\nvariant and the genomic nucleotide and associated amino acid may be reversed.\nE.g., a Pro > Arg change from the perspective of the mRNA would be Arg > Pro from\nthe perspective of the genomic sequence. Also, in bosTau9, galGal5, rheMac10,\nand danRer11, the mitochondrial sequence was removed or renamed to match UCSC.\nFor complete documentation of the processing of these tracks, see the makedoc corresponding\nto the version of interest. For example, the\n\nEVA Release 9 MakeDoc.
\n\n\nNote: It is not recommended to use LiftOver to convert SNPs between assemblies,\nand more information about how to convert SNPs between assemblies can be found on the following\nFAQ entry.
\n\nThe data can be explored interactively with the Table Browser\nor the Data Integrator. For automated analysis, the data may be\nqueried from our REST API. Please refer to our\nmailing list archives\nfor questions or our Data Access FAQ for more\ninformation.
\n\n\nFor automated download and analysis, this annotation is stored in a bigBed file that\ncan be downloaded from our download server. Use the corresponding version number for the track\nof interest, e.g., evaSnp9.bb.\nIndividual regions or the whole genome annotation can be obtained using our tool\nbigBedToBed, which can be compiled from the source code or downloaded as a precompiled\nbinary for your system. Instructions for downloading source code and binaries can be found\nhere.\nThe tool can also be used to obtain only features within a given range, e.g.\n
\nbigBedToBed https://hgdownload.soe.ucsc.edu/gbdb/mm39/bbi/evaSnp9.bb -chrom=chr21 -start=0 -end=100000000 stdout\n
\nThis track was produced from the European\nVariation Archive release data. Consequences were predicted using UCSC's Variant Annotation\nIntegrator and NCBI's RefSeq, as well as ensembl gene models.\n
\n\n\nCezard T, Cunningham F, Hunt SE, Koylass B, Kumar N, Saunders G, Shen A, Silva AF,\nTsukanov K, Venkataraman S et al. The European Variation Archive: a FAIR resource of genomic variation for all\nspecies. Nucleic Acids Res. 2021 Oct 28:gkab960.\ndoi:10.1093/nar/gkab960.\nEpub ahead of print. PMID: 34718739. PMID: PMC8728205.\n
\n\nHinrichs AS, Raney BJ, Speir ML, Rhead B, Casper J, Karolchik D, Kuhn RM, Rosenbloom KR, Zweig AS,\nHaussler D, Kent WJ.\nUCSC Data Integrator and Variant Annotation Integrator.\nBioinformatics. 2016 May 1;32(9):1430-2.\nPMID: 26740527; PMC:\nPMC4848401\n
\n", "itemRgb": "on", "longLabel": "Short Genetic Variants from European Variant Archive Release 8", "maxItems": "1000000", "maxWindowCoverage": "250000", "mouseOver": "Ref/Alt allele(s): $ref>$alt\nThe GC percent track shows the percentage of G (guanine) and C (cytosine) bases\nin 5-base windows. High GC content is typically associated with\ngene-rich areas.\n
\n\nThis track may be configured in a variety of ways to highlight different\napsects of the displayed information. Click the\n"Graph configuration help"\nlink for an explanation of the configuration options.\n\n
The data and presentation of this graph were prepared by\nHiram Clawson.\n
\n\n", "longLabel": "GC Percent in 5-Base Windows", "maxHeightPixels": "128:36:16", "shortLabel": "GC Percent", "track": "gc5BaseBw", "type": "bigWig 0 100", "viewLimits": "30:70", "visibility": "hide", "windowingFunction": "Mean" } }, "description": "GC Percent in 5-Base Windows", "category": ["Mapping and Sequencing"] }, { "trackId": "mm39-grcIncidentDb", "name": "GRC Incident", "type": "FeatureTrack", "assemblyNames": ["mm39"], "adapter": { "type": "BigBedAdapter", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/mm39/bbi/grcIncidentDb/Mm39.grcIncidentDb.bb" }, "metadata": { "ucsc": { "group": "map", "longLabel": "GRC Incident Database", "shortLabel": "GRC Incident", "track": "grcIncidentDb", "type": "bigBed 4 +", "url": "https://www.ncbi.nlm.nih.gov/projects/genome/assembly/grc/issue_detail.cgi?id=$$", "urlLabel": "GRC Incident:", "visibility": "hide", "html": "\nThis track shows locations in the mouse assembly where assembly\nproblems have been noted or resolved, as reported by the\nGenome Reference Consortium (GRC). \n
\n\nIf you would like to report an assembly problem, please use the GRC\nissue reporting system.\n
\n\n\nData for this track are extracted from the GRC\nincident database from the specific species *_issues.gff3 file.\nThe track is synchronized once daily to incorporate new updates. \n
\n\nThe data and presentation of this track were prepared by\nHiram Clawson.\n
\n" } }, "description": "GRC Incident Database", "category": ["Mapping and Sequencing"] }, { "trackId": "mm39-HLTOGAannotvHg38v1", "name": "TOGA vs. hg38", "type": "FeatureTrack", "assemblyNames": ["mm39"], "adapter": { "type": "BigBedAdapter", "uri": "https://hgdownload.soe.ucsc.edu/gbdb/mm39/TOGAvHg38v1/HLTOGAannotVsHg38v1.bb" }, "metadata": { "ucsc": { "bigDataUrl": "/gbdb/mm39/TOGAvHg38v1/HLTOGAannotVsHg38v1.bb", "group": "genes", "html": "\nTOGA\n(Tool to infer Orthologs from Genome Alignments)\nis a homology-based method that integrates gene annotation, inferring\northologs and classifying genes as intact or lost.\n
\n\n\nAs input, TOGA uses a gene annotation of a reference species\n(human/hg38 for mammals, chicken/galGal6 for birds) and\na whole genome alignment between the reference and query genome.\n
\n\nTOGA implements a novel paradigm that relies on alignments of intronic\nand intergenic regions and uses machine learning to accurately distinguish\northologs from paralogs or processed pseudogenes.\n
\n\nTo annotate genes,\nCESAR 2.0\nis used to determine the positions and boundaries of coding exons of a\nreference transcript in the orthologous genomic locus in the query species.\n
\n\n\nEach annotated transcript is shown in a color-coded classification as\n
\nClicking on a transcript provides additional information about the orthology\nclassification, inactivating mutations, the protein sequence and protein/exon\nalignments.\n
\n\n\nThis data was prepared by the Michael Hiller Lab\n
\n\n\nThe TOGA software is available from\ngithub.com/hillerlab/TOGA\n
\n\n\nKirilenko BM, Munegowda C, Osipova E, Jebb D, Sharma V, Blumer M, Morales AE, Ahmed AW, Kontopoulos\nDG, Hilgers L et al.\n\nIntegrating gene annotation with orthology inference at scale.\nScience. 2023 Apr 28;380(6643):eabn3107.\nPMID: 37104600; PMC:
This track shows potential enhancers whose activity was experimentally validated in transgenic\nmice. Most of these noncoding elements were selected for testing based on their extreme conservation\nin other vertebrates or epigenomic evidence (ChIP-Seq) of putative enhancer marks. More information\ncan be found on the VISTA Enhancer Browser\npage.\n
\n\nItems appearing in blue (positive) indicate that a\nreproducible pattern was observed in the in vivo enhancer assay under at least one of the\ntested conditions. Items appearing in gray (negative) indicate\nthat NO reproducible pattern was observed in the in vivo enhancer assay under any of the tested\nconditions. This does not exclude the possibility that this region is a reproducible enhancer active\nunder different conditions, for example at an earlier or later timepoint in development.
\n\nExcerpted from the Vista Enhancer Mouse Enhancer Screen Handbook and Methods page at the Lawrence Berkeley\nNational Laboratory (LBNL) website:\n
Most enhancer candidate sequences are identified by extreme evolutionary sequence conservation or\nby ChIP-seq. Detailed information related to enhancer identification by extreme evolutionary\nconservation can be found in the following publications:\n
\nDetailed information related to enhancer identification by ChIP-seq can be found in the\nfollowing publications:
\nSee the Transgenic Mouse Assay section for experimental procedures that were used to perform the\ntransgenic assays: Mouse Enhancer Screen Handbook and Methods \n\n
UCSC converted the\nvista-data bed files for\nhg38 and mm10 into bigBed format using the bedToBigBed utility. The data for mm39 was lifted over\nfrom mm10. The data for hg19 was lifted over from hg38.
\n\n\nVISTA Enhancers data can be explored interactively with the\nTable Browser and cross-referenced with the\nData Integrator. For programmatic access, the track can be\naccessed using the Genome Browser's REST API. ReMap\nannotations can be downloaded from the Genome Browser's\ndownload server\nas a bigBed file. This compressed binary format can be remotely queried through\ncommand line utilities. Please note that some of the download files can be quite large.
\n\nThanks to the Lawrence Berkeley National Laboratory for providing this data.
\n\n\n\nKosicki M, Baltoumas FA, Kelman G, Boverhof J, Ong Y, Cook LE, Dickel DE, Pavlopoulos GA, Pennacchio\nLA, Visel A.\n\nVISTA Enhancer browser: an updated database of tissue-specific developmental enhancers.\nNucleic Acids Res. 2025 Jan 6;53(D1):D324-D330.\nPMID: 39470740; PMC:
\nVisel A, Minovitsky S, Dubchak I, Pennacchio LA.\n\nVISTA Enhancer Browser--a database of tissue-specific human enhancers.\nNucleic Acids Res. 2007 Jan;35(Database issue):D88-92.\nPMID: 17130149; PMC:
CpG islands are associated with genes, particularly housekeeping\ngenes, in vertebrates. CpG islands are typically common near\ntranscription start sites and may be associated with promoter\nregions. Normally a C (cytosine) base followed immediately by a \nG (guanine) base (a CpG) is rare in\nvertebrate DNA because the Cs in such an arrangement tend to be\nmethylated. This methylation helps distinguish the newly synthesized\nDNA strand from the parent strand, which aids in the final stages of\nDNA proofreading after duplication. However, over evolutionary time,\nmethylated Cs tend to turn into Ts because of spontaneous\ndeamination. The result is that CpGs are relatively rare unless\nthere is selective pressure to keep them or a region is not methylated\nfor some other reason, perhaps having to do with the regulation of gene\nexpression. CpG islands are regions where CpGs are present at\nsignificantly higher levels than is typical for the genome as a whole.
\n\n\nThe unmasked version of the track displays potential CpG islands\nthat exist in repeat regions and would otherwise not be visible\nin the repeat masked version.\n
\n\n\nBy default, only the masked version of the track is displayed. To view the\nunmasked version, change the visibility settings in the track controls at\nthe top of this page.\n
\n\nCpG islands were predicted by searching the sequence one base at a\ntime, scoring each dinucleotide (+17 for CG and -1 for others) and\nidentifying maximally scoring segments. Each segment was then\nevaluated for the following criteria:\n\n
\nThe entire genome sequence, masking areas included, was\nused for the construction of the track Unmasked CpG.\nThe track CpG Islands is constructed on the sequence after\nall masked sequence is removed.\n
\n\nThe CpG count is the number of CG dinucleotides in the island. \nThe Percentage CpG is the ratio of CpG nucleotide bases\n(twice the CpG count) to the length. The ratio of observed to expected \nCpG is calculated according to the formula (cited in \nGardiner-Garden et al. (1987)):\n\n
Obs/Exp CpG = Number of CpG * N / (Number of C * Number of G)\n\nwhere N = length of sequence.\n
\nThe calculation of the track data is performed by the following command sequence:\n
\ntwoBitToFa assembly.2bit stdout | maskOutFa stdin hard stdout \n | cpg_lh /dev/stdin 2> cpg_lh.err \n | awk '{$2 = $2 - 1; width = $3 - $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\", $1, $2, $3, $5, $6, width, $6, width*$7*0.01, 100.0*2*$6/width, $7, $9);}' \n | sort -k1,1 -k2,2n > cpgIsland.bed\n\nThe unmasked track data is constructed from\ntwoBitToFa -noMask output for the twoBitToFa command.\n\n\n\nCpG islands and its associated tables can be explored interactively using the\nREST API, the\nTable Browser or the\nData Integrator.\nAll the tables can also be queried directly from our public MySQL\nservers, with more information available on our\nhelp page as well as on\nour blog.
\n\nThe source for the cpg_lh program can be obtained from\nsrc/utils/cpgIslandExt/.\nThe cpg_lh program binary can be obtained from: http://hgdownload.soe.ucsc.edu/admin/exe/linux.x86_64/cpg_lh (choose \"save file\")\n
\n\nThis track was generated using a modification of a program developed by G. Micklem and L. Hillier \n(unpublished).
\n\n\nGardiner-Garden M, Frommer M.\n\nCpG islands in vertebrate genomes.\nJ Mol Biol. 1987 Jul 20;196(2):261-82.\nPMID: 3656447\n
\n", "longLabel": "CpG Islands (Islands < 300 Bases are Light Green)", "parent": "cpgIslandSuper pack", "priority": "1", "shortLabel": "CpG Islands", "track": "cpgIslandExt" } }, "description": "CpG Islands (Islands < 300 Bases are Light Green)", "category": ["Regulation"] }, { "type": "FeatureTrack", "trackId": "mm39-cpgIslandExtUnmasked", "name": "CpG Islands - Unmasked CpG", "assemblyNames": ["mm39"], "adapter": { "type": "BedTabixAdapter", "bedGzLocation": { "uri": "cpgIslandExtUnmasked.bed.gz" }, "index": { "indexType": "CSI", "location": { "uri": "cpgIslandExtUnmasked.bed.gz.csi" } } }, "metadata": { "ucsc": { "html": "CpG islands are associated with genes, particularly housekeeping\ngenes, in vertebrates. CpG islands are typically common near\ntranscription start sites and may be associated with promoter\nregions. Normally a C (cytosine) base followed immediately by a \nG (guanine) base (a CpG) is rare in\nvertebrate DNA because the Cs in such an arrangement tend to be\nmethylated. This methylation helps distinguish the newly synthesized\nDNA strand from the parent strand, which aids in the final stages of\nDNA proofreading after duplication. However, over evolutionary time,\nmethylated Cs tend to turn into Ts because of spontaneous\ndeamination. The result is that CpGs are relatively rare unless\nthere is selective pressure to keep them or a region is not methylated\nfor some other reason, perhaps having to do with the regulation of gene\nexpression. CpG islands are regions where CpGs are present at\nsignificantly higher levels than is typical for the genome as a whole.
\n\n\nThe unmasked version of the track displays potential CpG islands\nthat exist in repeat regions and would otherwise not be visible\nin the repeat masked version.\n
\n\n\nBy default, only the masked version of the track is displayed. To view the\nunmasked version, change the visibility settings in the track controls at\nthe top of this page.\n
\n\nCpG islands were predicted by searching the sequence one base at a\ntime, scoring each dinucleotide (+17 for CG and -1 for others) and\nidentifying maximally scoring segments. Each segment was then\nevaluated for the following criteria:\n\n
\nThe entire genome sequence, masking areas included, was\nused for the construction of the track Unmasked CpG.\nThe track CpG Islands is constructed on the sequence after\nall masked sequence is removed.\n
\n\nThe CpG count is the number of CG dinucleotides in the island. \nThe Percentage CpG is the ratio of CpG nucleotide bases\n(twice the CpG count) to the length. The ratio of observed to expected \nCpG is calculated according to the formula (cited in \nGardiner-Garden et al. (1987)):\n\n
Obs/Exp CpG = Number of CpG * N / (Number of C * Number of G)\n\nwhere N = length of sequence.\n
\nThe calculation of the track data is performed by the following command sequence:\n
\ntwoBitToFa assembly.2bit stdout | maskOutFa stdin hard stdout \n | cpg_lh /dev/stdin 2> cpg_lh.err \n | awk '{$2 = $2 - 1; width = $3 - $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\", $1, $2, $3, $5, $6, width, $6, width*$7*0.01, 100.0*2*$6/width, $7, $9);}' \n | sort -k1,1 -k2,2n > cpgIsland.bed\n\nThe unmasked track data is constructed from\ntwoBitToFa -noMask output for the twoBitToFa command.\n\n\n\nCpG islands and its associated tables can be explored interactively using the\nREST API, the\nTable Browser or the\nData Integrator.\nAll the tables can also be queried directly from our public MySQL\nservers, with more information available on our\nhelp page as well as on\nour blog.
\n\nThe source for the cpg_lh program can be obtained from\nsrc/utils/cpgIslandExt/.\nThe cpg_lh program binary can be obtained from: http://hgdownload.soe.ucsc.edu/admin/exe/linux.x86_64/cpg_lh (choose \"save file\")\n
\n\nThis track was generated using a modification of a program developed by G. Micklem and L. Hillier \n(unpublished).
\n\n\nGardiner-Garden M, Frommer M.\n\nCpG islands in vertebrate genomes.\nJ Mol Biol. 1987 Jul 20;196(2):261-82.\nPMID: 3656447\n
\n", "longLabel": "CpG Islands on All Sequence (Islands < 300 Bases are Light Green)", "parent": "cpgIslandSuper hide", "priority": "2", "shortLabel": "Unmasked CpG", "track": "cpgIslandExtUnmasked" } }, "description": "CpG Islands on All Sequence (Islands < 300 Bases are Light Green)", "category": ["Regulation"] }, { "type": "FeatureTrack", "trackId": "mm39-cytoBand", "name": "Chromosome Band", "assemblyNames": ["mm39"], "adapter": { "type": "BedTabixAdapter", "bedGzLocation": { "uri": "cytoBand.bed.gz" }, "index": { "indexType": "CSI", "location": { "uri": "cytoBand.bed.gz.csi" } } }, "metadata": { "ucsc": { "altColor": "150,50,50", "group": "map", "longLabel": "Chromosome Bands Based On Microscopy", "shortLabel": "Chromosome Band", "track": "cytoBand", "type": "bed 4 +", "visibility": "hide", "html": "The chromosome band track represents the approximate \nlocation of bands seen on Giemsa-stained chromosomes.\n
\nData are derived from the ideogram.gz file downloaded from the NCBI ftp \nsite ftp://ftp.ncbi.nlm.nih.gov/pub/gdp/\n(NCBI current version only).\nBand lengths are typically estimated based on FISH or other\nmolecular markers interpreted via microscopy. \n\n
We would like to thank NCBI for providing this information.\nPlease direct any inquires into the exact method used for each organism\nto NCBI.\n" } }, "description": "Chromosome Bands Based On Microscopy", "category": ["Mapping and Sequencing"] }, { "type": "FeatureTrack", "trackId": "mm39-gap", "name": "Gap", "assemblyNames": ["mm39"], "adapter": { "type": "BedTabixAdapter", "bedGzLocation": { "uri": "gap.bed.gz" }, "index": { "indexType": "CSI", "location": { "uri": "gap.bed.gz.csi" } } }, "metadata": { "ucsc": { "group": "map", "html": "
\nThis track shows the gaps in the Jun. 2020 mouse genome assembly.\n
\n\nGenome assembly procedures are covered in the NCBI\nassembly documentation.
\nNCBI also provides\nspecific information about this assembly.\n
\nThe definition of the gaps in this assembly is from the\nAGP file delivered with the sequence. The NCBI document\nAGP Specification describes the format of the AGP file.\n
\n\nGaps are represented as black boxes in this track.\nIf the relative order and orientation of the contigs on either side\nof the gap is supported by read pair data,\nit is a bridged gap and a white line is drawn\nthrough the black box representing the gap.\n
\nThis assembly contains the following principal types of gaps:\n
\nThis track shows the sequences used in the Jun. 2020 mouse genome assembly.\n
\n\nGenome assembly procedures are covered in the NCBI\nassembly documentation.
\nNCBI also provides\nspecific information about this assembly.\n
\nThe definition of this assembly is from the\nAGP file delivered with the sequence. The NCBI document\nAGP Specification describes the format of the AGP file.\n
\n\nIn dense mode, this track depicts the contigs that make up the\ncurrently viewed scaffold.\nContig boundaries are distinguished by the use of alternating gold and brown\ncoloration. Where gaps\nexist between contigs, spaces are shown between the gold and brown\nblocks. The relative order and orientation of the contigs\nwithin a scaffold is always known; therefore, a line is drawn in the graphical\ndisplay to bridge the blocks.
\n\nComponent types found in this track (with counts of that type in parentheses):\n
This track shows various types of alternative splicing and other\nevents that result in more than a single transcript from the same\ngene. The label by an item describes the type of event. The events are:
\nThis track is based on an analysis by the txgAnalyse program of splicing graphs\nproduced by the txGraph program. Both of these programs were written by Jim\nKent at UCSC.
\n" } }, "description": "Alternative Splicing, Alternative Promoter and Similar Events in UCSC Genes", "category": ["Genes and Gene Predictions"] }, { "type": "FeatureTrack", "trackId": "mm39-microsat", "name": "Microsatellite", "assemblyNames": ["mm39"], "adapter": { "type": "BedTabixAdapter", "bedGzLocation": { "uri": "microsat.bed.gz" }, "index": { "indexType": "CSI", "location": { "uri": "microsat.bed.gz.csi" } } }, "metadata": { "ucsc": { "group": "varRep", "longLabel": "Microsatellites - Di-nucleotide and Tri-nucleotide Repeats", "shortLabel": "Microsatellite", "track": "microsat", "type": "bed 4", "visibility": "hide", "html": "\nThis track displays regions that are likely to be useful as microsatellite\nmarkers. These are sequences of at least 15 perfect di-nucleotide and \ntri-nucleotide repeats and tend to be highly polymorphic in the\npopulation.\n
\n\n\nThe data shown in this track are a subset of the Simple Repeats track, \nselecting only those \nrepeats of period 2 and 3, with 100% identity and no indels and with\nat least 15 copies of the repeat. The Simple Repeats track is\ncreated using the \nTandem Repeats Finder. For more information about this \nprogram, see Benson (1999).
\n\n\nTandem Repeats Finder was written by \nGary Benson.
\n\n\nBenson G.\n\nTandem repeats finder: a program to analyze DNA sequences.\nNucleic Acids Res. 1999 Jan 15;27(2):573-80.\nPMID: 9862982; PMC:
\nThis track shows joined fragments of interrupted repeats extracted\nfrom the output of the \nRepeatMasker program which screens DNA sequences\nfor interspersed repeats and low complexity DNA sequences using the\n\nRepbase Update library of repeats from the\nGenetic\nInformation Research Institute (GIRI). Repbase Update is described in\nJurka (2000) in the References section below.\n
\n\n\nThe detailed annotations from RepeatMasker are in the RepeatMasker track. This\ntrack shows fragments of original repeat insertions which have been interrupted\nby insertions of younger repeats or through local rearrangements. The fragments\nare joined using the ID column of RepeatMasker output.\n
\n\n\nIn pack or full mode, each interrupted repeat is displayed as boxes\n(fragments) joined by horizontal lines, labeled with the repeat name.\nIf all fragments are on the same strand, arrows are added to the\nhorizontal line to indicate the strand. In dense or squish mode, labels\nand arrows are omitted and in dense mode, all items are collapsed to\nfit on a single row.\n
\n\n\nItems are shaded according to the average identity score of their\nfragments. Usually, the shade of an item is similar to the shades of\nits fragments unless some fragments are much more diverged than\nothers. The score displayed above is the average identity score,\nclipped to a range of 50% - 100% and then mapped to the range\n0 - 1000 for shading in the browser.\n
\n\n\nUCSC has used the most current versions of the RepeatMasker software\nand repeat libraries available to generate these data. Note that these\nversions may be newer than those that are publicly available on the Internet.\n
\n\n\nData are generated using the RepeatMasker -s flag. Additional flags\nmay be used for certain organisms. See the\nFAQ for more information.\n
\n\n\nThanks to Arian Smit, Robert Hubley and GIRI for providing the tools and\nrepeat libraries used to generate this track.\n
\n\n\nSmit AFA, Hubley R, Green P.\nRepeatMasker Open-3.0.\n\nhttps://www.repeatmasker.org/. 1996-2010.\n
\n\n\nRepbase Update is described in:\n
\n\n\nJurka J.\n\nRepbase Update: a database and an electronic journal of repetitive elements.\nTrends Genet. 2000 Sep;16(9):418-420.\nPMID: 10973072\n
\n\n\nFor a discussion of repeats in mammalian genomes, see:\n
\n\n\nSmit AF.\n\nInterspersed repeats and other mementos of transposable elements in mammalian genomes.\nCurr Opin Genet Dev. 1999 Dec;9(6):657-63.\nPMID: 10607616\n
\n\n\nSmit AF.\n\nThe origin of interspersed repeats in the human genome.\nCurr Opin Genet Dev. 1996 Dec;6(6):743-8.\nPMID: 8994846\n
\n" } }, "description": "Fragments of Interrupted Repeats Joined by RepeatMasker ID", "category": ["Variation and Repeats"] }, { "type": "FeatureTrack", "trackId": "mm39-phastConsElements35way", "name": "Conserved Elements - 35 Vert. El", "assemblyNames": ["mm39"], "adapter": { "type": "BedTabixAdapter", "bedGzLocation": { "uri": "phastConsElements35way.bed.gz" }, "index": { "indexType": "CSI", "location": { "uri": "phastConsElements35way.bed.gz.csi" } } }, "metadata": { "ucsc": { "color": "110,10,40", "longLabel": "35 vertebrates Conserved Elements", "noInherit": "on", "parent": "cons35wayViewelements off", "priority": "23", "shortLabel": "35 Vert. El", "subGroups": "view=elements clade=all", "track": "phastConsElements35way", "type": "bed 5 .", "html": "" } }, "description": "35 vertebrates Conserved Elements", "category": ["Comparative Genomics"] }, { "type": "FeatureTrack", "trackId": "mm39-rmsk", "name": "RepeatMasker", "assemblyNames": ["mm39"], "adapter": { "type": "BedTabixAdapter", "bedGzLocation": { "uri": "rmsk.bed.gz" }, "index": { "indexType": "CSI", "location": { "uri": "rmsk.bed.gz.csi" } } }, "metadata": { "ucsc": { "canPack": "off", "group": "varRep", "longLabel": "Repeating Elements by RepeatMasker", "maxWindowToDraw": "10000000", "priority": "1", "shortLabel": "RepeatMasker", "spectrum": "on", "track": "rmsk", "type": "rmsk", "visibility": "dense", "html": "\nThis track was created by using Arian Smit's\nRepeatMasker\nprogram, which screens DNA sequences\nfor interspersed repeats and low complexity DNA sequences. The program\noutputs a detailed annotation of the repeats that are present in the\nquery sequence (represented by this track), as well as a modified version\nof the query sequence in which all the annotated repeats have been masked\n(generally available on the\nDownloads page). RepeatMasker uses the\nRepbase Update library of repeats from the\nGenetic \nInformation Research Institute (GIRI).\nRepbase Update is described in Jurka (2000) in the References section below.\nSome newer assemblies have been made with Dfam, not Repbase. You can\nfind the details for how we make our database data here in our "makeDb/doc/"\ndirectory.
\n\n\nWhen analyzing the data tables of this track, keep in mind that Repbase is not the same \nas the Repeatmasker sequence database and that the repeat names in the\nRepeatmasker output are not the same as the sequence names in the Repeatmasker\ndatabase. Concretely, you can find a name such as \"L1PA4\" in the Repeatmasker\noutput and this track, but there is not necessarily a single sequence \"L1PA4\"\nin the Repeatmasker database. This is because Repeatmasker creates annotations\nby joining matches to partial pieces of the database together so there is no\n1:1 relationship between its sequence database and the annotations. To learn\nmore, you can read the Repeatmasker paper, its source code or reach out to the\nRepeatmasker authors, your local expert on transposable elements or us.\n
\n\n\nIn full display mode, this track displays up to ten different classes of repeats:\n
\nThe level of color shading in the graphical display reflects the amount of\nbase mismatch, base deletion, and base insertion associated with a repeat\nelement. The higher the combined number of these, the lighter the shading.\n
\n\n\nA "?" at the end of the "Family" or "Class" (for example, DNA?) signifies that\nthe curator was unsure of the classification. At some point in the future,\neither the "?" will be removed or the classification will be changed.
\n\n\nData are generated using the RepeatMasker -s flag. Additional flags\nmay be used for certain organisms. Repeats are soft-masked. Alignments may\nextend through repeats, but are not permitted to initiate in them.\nSee the FAQ for more information.\n
\n\n\nThanks to Arian Smit, Robert Hubley and GIRI for providing the tools and\nrepeat libraries used to generate this track.\n
\n\n\nSmit AFA, Hubley R, Green P. RepeatMasker Open-3.0.\n\nhttps://www.repeatmasker.org/. 1996-2010.\n
\n\n\nRepbase Update is described in:\n
\n\n\nJurka J.\n\nRepbase Update: a database and an electronic journal of repetitive elements.\nTrends Genet. 2000 Sep;16(9):418-420.\nPMID: 10973072\n
\n\n\nFor a discussion of repeats in mammalian genomes, see:\n
\n\n\nSmit AF.\n\nInterspersed repeats and other mementos of transposable elements in mammalian genomes.\nCurr Opin Genet Dev. 1999 Dec;9(6):657-63.\nPMID: 10607616\n
\n\n\nSmit AF.\n\nThe origin of interspersed repeats in the human genome.\nCurr Opin Genet Dev. 1996 Dec;6(6):743-8.\nPMID: 8994846\n
\n" } }, "description": "Repeating Elements by RepeatMasker", "category": ["Variation and Repeats"] }, { "type": "FeatureTrack", "trackId": "mm39-simpleRepeat", "name": "Simple Repeats", "assemblyNames": ["mm39"], "adapter": { "type": "BedTabixAdapter", "bedGzLocation": { "uri": "simpleRepeat.bed.gz" }, "index": { "indexType": "CSI", "location": { "uri": "simpleRepeat.bed.gz.csi" } } }, "metadata": { "ucsc": { "group": "varRep", "longLabel": "Simple Tandem Repeats by TRF", "shortLabel": "Simple Repeats", "track": "simpleRepeat", "type": "bed 4 +", "visibility": "hide", "html": "\nThis track displays simple tandem repeats (possibly imperfect repeats) located\nby Tandem Repeats\nFinder (TRF) which is specialized for this purpose. These repeats can\noccur within coding regions of genes and may be quite\npolymorphic. Repeat expansions are sometimes associated with specific\ndiseases.
\n\n\nFor more information about the TRF program, see Benson (1999).\n
\n\n\nTRF was written by \nGary Benson.
\n\n\nBenson G.\n\nTandem repeats finder: a program to analyze DNA sequences.\nNucleic Acids Res. 1999 Jan 15;27(2):573-80.\nPMID: 9862982; PMC:
\nMost proteins are composed of one or more conserved functional regions called\ndomains. This track shows the high-quality, manually-curated\n\nPfam-A\ndomains found in transcripts located in the GENCODE Genes track by the software HMMER3.\n
\n\n\nThis track follows the display conventions for\ngene\ntracks.\n
\n\n\nThe sequences from the knownGenePep table (see \nGENCODE Genes description page)\nare submitted to the set of Pfam-A HMMs which annotate regions within the\npredicted peptide that are recognizable as Pfam protein domains. These regions\nare then mapped to the transcripts themselves using the\n\npslMap utility. A complete shell script log for every version of UCSC genes can be found in \nour GitHub repository under \n\nhg/makeDb/doc/ucscGenes, e.g. \n\nmm10.knownGenes17.csh is for the database mm10 and version 17 of UCSC known genes.\n
\n\n\nOf the several options for filtering out false positives, the "Trusted cutoff (TC)" \nthreshold method is used in this track to determine significance. For more information regarding \nthresholds and scores, see the HMMER \ndocumentation and\nresults interpretation pages.\n
\n\n\nNote: There is currently an undocumented but known HMMER problem which results in lessened \nsensitivity and possible missed searches for some zinc finger domains. Until a fix is released for \nHMMER /PFAM thresholds, please also consult the "UniProt Domains" subtrack of the UniProt\ntrack for more comprehensive zinc finger annotations.\n
\n\n\npslMap was written by Mark Diekhans at UCSC.\n
\n\n\nFinn RD, Mistry J, Tate J, Coggill P, Heger A, Pollington JE, Gavin OL, Gunasekaran P, Ceric G,\nForslund K et al.\nThe Pfam protein families database.\nNucleic Acids Res. 2010 Jan;38(Database issue):D211-22.\nPMID: 19920124; PMC:
\nThis track associates UCSC Genome Browser chromosome names to accession\nnames from the International Nucleotide Sequence Database Collaboration (INSDC).\n
\n\n\nThe data were downloaded from the NCBI assembly database.\n
\n\nThe data for this track was prepared by\nHiram Clawson.\n\n" } }, "description": "Accession at INSDC - International Nucleotide Sequence Database Collaboration", "category": ["Mapping and Sequencing"] }, { "type": "FeatureTrack", "trackId": "mm39-windowmaskerSdust", "name": "WM + SDust", "assemblyNames": ["mm39"], "adapter": { "type": "BedTabixAdapter", "bedGzLocation": { "uri": "windowmaskerSdust.bed.gz" }, "index": { "indexType": "CSI", "location": { "uri": "windowmaskerSdust.bed.gz.csi" } } }, "metadata": { "ucsc": { "group": "varRep", "longLabel": "Genomic Intervals Masked by WindowMasker + SDust", "shortLabel": "WM + SDust", "track": "windowmaskerSdust", "type": "bed 3", "visibility": "hide", "html": "
\nThis track depicts masked sequence as determined by\nWindowMasker. The\nWindowMasker tool is included in the NCBI C++ toolkit. The source code\nfor the entire toolkit is available from the NCBI\n\nFTP site.\n
\n\n\nTo create this track, WindowMasker was run with the following parameters:\n
\nwindowmasker -mk_counts true -input mm39.fa -output wm_counts\nwindowmasker -ustat wm_counts -sdust true -input mm39.fa -output repeats.bed\n\nThe repeats.bed (BED3) file was loaded into the "windowmaskerSdust" table for\nthis track.\n\n\n
\nMorgulis A, Gertz EM, Schäffer AA, Agarwala R.\nWindowMasker: window-based masker for sequenced genomes.\nBioinformatics. 2006 Jan 15;22(2):134-41.\nPMID: 16287941\n
\n" } }, "description": "Genomic Intervals Masked by WindowMasker + SDust", "category": ["Variation and Repeats"] }, { "type": "FeatureTrack", "trackId": "mm39-augustusGene", "name": "AUGUSTUS", "assemblyNames": ["mm39"], "adapter": { "type": "Gff3TabixAdapter", "gffGzLocation": { "uri": "augustusGene.gff.gz" }, "index": { "indexType": "CSI", "location": { "uri": "augustusGene.gff.gz.csi" } } }, "metadata": { "ucsc": { "baseColorDefault": "genomicCodons", "baseColorUseCds": "given", "color": "12,105,0", "group": "genes", "longLabel": "AUGUSTUS ab initio gene predictions v3.1", "shortLabel": "AUGUSTUS", "track": "augustusGene", "type": "genePred", "visibility": "hide", "html": "\n This track shows ab initio predictions from the program\n AUGUSTUS (version 3.1).\n The predictions are based on the genome sequence alone.\n
\n\n\nFor more information on the different gene tracks, see our Genes FAQ.
\n\n\nStatistical signal models were built for splice sites, branch-point\npatterns, translation start sites, and the poly-A signal.\nFurthermore, models were built for the sequence content of\nprotein-coding and non-coding regions as well as for the length distributions\nof different exon and intron types. Detailed descriptions of most of these different models\ncan be found in Mario Stanke's\ndissertation.\nThis track shows the most likely gene structure according to a\nSemi-Markov Conditional Random Field model.\nAlternative splicing transcripts were obtained with\na sampling algorithm (--alternatives-from-sampling=true --sample=100 --minexonintronprob=0.2\n--minmeanexonintronprob=0.5 --maxtracks=3 --temperature=2).\n
\n\n\nThe different models used by Augustus were trained on a number of different species-specific\ngene sets, which included 1000-2000 training gene structures. The --species option allows\none to choose the species used for training the models. Different training species were used\nfor the --species option when generating these predictions for different groups of\nassemblies.\n
| Assembly Group | \nTraining Species | \n
| Fish | \nzebrafish\n |
| Birds | \nchicken\n |
| Human and all other vertebrates | \nhuman\n |
| Nematodes | \ncaenorhabditis | \n
| Drosophila | \nfly | \n
| A. mellifera | \nhoneybee1 | \n
| A. gambiae | \nculex | \n
| S. cerevisiae | \nsaccharomyces | \n
\nThis table describes which training species was used for a particular group of assemblies.\nWhen available, the closest related training species was used.\n
\n\n\nStanke M, Diekhans M, Baertsch R, Haussler D.\n\nUsing native and syntenically mapped cDNA alignments to improve de novo gene finding.\nBioinformatics. 2008 Mar 1;24(5):637-44.\nPMID: 18218656\n
\n\n\nStanke M, Waack S.\n\nGene prediction with a hidden Markov model and a new intron submodel.\nBioinformatics. 2003 Oct;19 Suppl 2:ii215-25.\nPMID: 14534192\n
\n" } }, "description": "AUGUSTUS ab initio gene predictions v3.1", "category": ["Genes and Gene Predictions"] }, { "type": "FeatureTrack", "trackId": "mm39-genscan", "name": "Genscan Genes", "assemblyNames": ["mm39"], "adapter": { "type": "Gff3TabixAdapter", "gffGzLocation": { "uri": "genscan.gff.gz" }, "index": { "indexType": "CSI", "location": { "uri": "genscan.gff.gz.csi" } } }, "metadata": { "ucsc": { "color": "170,100,0", "group": "genes", "longLabel": "Genscan Gene Predictions", "shortLabel": "Genscan Genes", "track": "genscan", "type": "genePred genscanPep", "visibility": "hide", "html": "\nThis track shows predictions from the\nGenscan program\nwritten by Chris Burge.\nThe predictions are based on transcriptional, translational and donor/acceptor\nsplicing signals as well as the length and compositional distributions of exons,\nintrons and intergenic regions.\n
\n\n\nFor more information on the different gene tracks, see our Genes FAQ.
\n\n\nThis track follows the display conventions for\ngene prediction\ntracks.\n
\n\n\nThe track description page offers the following filter and configuration\noptions:\n
\nFor a description of the Genscan program and the model that underlies it,\nrefer to Burge and Karlin (1997) in the References section below.\nThe splice site models used are described in more detail in Burge (1998)\nbelow.\n
\n\n\nBurge C.\nModeling Dependencies in Pre-mRNA Splicing Signals.\nIn: Salzberg S, Searls D, Kasif S, editors.\nComputational Methods in Molecular Biology.\nAmsterdam: Elsevier Science; 1998. p. 127-163.\n
\n\n\nBurge C, Karlin S.\n\nPrediction of complete gene structures in human genomic DNA.\nJ. Mol. Biol. 1997 Apr 25;268(1):78-94.\nPMID: 9149143\n
\n" } }, "description": "Genscan Gene Predictions", "category": ["Genes and Gene Predictions"] }, { "type": "FeatureTrack", "trackId": "mm39-ncbiRefSeq", "name": "NCBI RefSeq - RefSeq All", "assemblyNames": ["mm39"], "adapter": { "type": "Gff3TabixAdapter", "gffGzLocation": { "uri": "ncbiRefSeq.gff.gz" }, "index": { "indexType": "CSI", "location": { "uri": "ncbiRefSeq.gff.gz.csi" } } }, "metadata": { "ucsc": { "baseColorDefault": "genomicCodons", "baseColorUseCds": "given", "color": "12,12,120", "idXref": "ncbiRefSeqLink mrnaAcc name", "longLabel": "NCBI RefSeq genes, curated and predicted (NM_*, XM_*, NR_*, XR_*, NP_*, YP_*)", "parent": "refSeqComposite off", "priority": "1", "shortLabel": "RefSeq All", "track": "ncbiRefSeq", "html": "" } }, "description": "NCBI RefSeq genes, curated and predicted (NM_*, XM_*, NR_*, XR_*, NP_*, YP_*)", "category": ["Genes and Gene Predictions"] }, { "type": "FeatureTrack", "trackId": "mm39-ncbiRefSeqCurated", "name": "NCBI RefSeq - RefSeq Curated", "assemblyNames": ["mm39"], "adapter": { "type": "Gff3TabixAdapter", "gffGzLocation": { "uri": "ncbiRefSeqCurated.gff.gz" }, "index": { "indexType": "CSI", "location": { "uri": "ncbiRefSeqCurated.gff.gz.csi" } } }, "metadata": { "ucsc": { "baseColorDefault": "genomicCodons", "baseColorUseCds": "given", "color": "12,12,120", "idXref": "ncbiRefSeqLink mrnaAcc name", "longLabel": "NCBI RefSeq genes, curated subset (NM_*, NR_*, NP_* or YP_*)", "parent": "refSeqComposite on", "priority": "2", "shortLabel": "RefSeq Curated", "track": "ncbiRefSeqCurated", "html": "" } }, "description": "NCBI RefSeq genes, curated subset (NM_*, NR_*, NP_* or YP_*)", "category": ["Genes and Gene Predictions"] }, { "type": "FeatureTrack", "trackId": "mm39-ncbiRefSeqPredicted", "name": "NCBI RefSeq - RefSeq Predicted", "assemblyNames": ["mm39"], "adapter": { "type": "Gff3TabixAdapter", "gffGzLocation": { "uri": "ncbiRefSeqPredicted.gff.gz" }, "index": { "indexType": "CSI", "location": { "uri": "ncbiRefSeqPredicted.gff.gz.csi" } } }, "metadata": { "ucsc": { "baseColorDefault": "genomicCodons", "baseColorUseCds": "given", "color": "12,12,120", "idXref": "ncbiRefSeqLink mrnaAcc name", "longLabel": "NCBI RefSeq genes, predicted subset (XM_* or XR_*)", "parent": "refSeqComposite off", "priority": "3", "shortLabel": "RefSeq Predicted", "track": "ncbiRefSeqPredicted", "html": "" } }, "description": "NCBI RefSeq genes, predicted subset (XM_* or XR_*)", "category": ["Genes and Gene Predictions"] }, { "type": "FeatureTrack", "trackId": "mm39-ncbiRefSeqSelect", "name": "NCBI RefSeq - RefSeq Select", "assemblyNames": ["mm39"], "adapter": { "type": "Gff3TabixAdapter", "gffGzLocation": { "uri": "ncbiRefSeqSelect.gff.gz" }, "index": { "indexType": "CSI", "location": { "uri": "ncbiRefSeqSelect.gff.gz.csi" } } }, "metadata": { "ucsc": { "baseColorDefault": "genomicCodons", "baseColorUseCds": "given", "color": "20,20,160", "idXref": "ncbiRefSeqLink mrnaAcc name", "longLabel": "NCBI RefSeq Select: One representative transcript per protein-coding gene", "parent": "refSeqComposite off", "priority": "8", "shortLabel": "RefSeq Select", "track": "ncbiRefSeqSelect", "trackHandler": "ncbiRefSeq", "type": "genePred", "html": "" } }, "description": "NCBI RefSeq Select: One representative transcript per protein-coding gene", "category": ["Genes and Gene Predictions"] }, { "type": "FeatureTrack", "trackId": "mm39-refGene", "name": "NCBI RefSeq - UCSC RefSeq", "assemblyNames": ["mm39"], "adapter": { "type": "Gff3TabixAdapter", "gffGzLocation": { "uri": "refGene.gff.gz" }, "index": { "indexType": "CSI", "location": { "uri": "refGene.gff.gz.csi" } } }, "metadata": { "ucsc": { "baseColorDefault": "genomicCodons", "baseColorUseCds": "given", "color": "12,12,120", "group": "genes", "idXref": "hgFixed.refLink mrnaAcc name", "longLabel": "UCSC annotations of RefSeq RNAs (NM_* and NR_*)", "parent": "refSeqComposite off", "priority": "7", "shortLabel": "UCSC RefSeq", "track": "refGene", "type": "genePred refPep refMrna", "visibility": "dense", "html": "\nThe RefSeq Genes track shows known mouse protein-coding and\nnon-protein-coding genes taken from the NCBI RNA reference sequences\ncollection (RefSeq). The data underlying this track are updated weekly.
\n\n\nPlease visit the Feedback for Gene and Reference Sequences (RefSeq) page to\nmake suggestions, submit additions and corrections, or ask for help concerning\nRefSeq records.\n
\n\n\nFor more information on the different gene tracks, see our Genes FAQ.
\n\n\nThis track follows the display conventions for\n\ngene prediction tracks.\nThe color shading indicates the level of review the RefSeq record has\nundergone: predicted (light), provisional (medium), reviewed (dark).\n
\n\n\nThe item labels and display colors of features within this track can be\nconfigured through the controls at the top of the track description page.\n
\nRefSeq RNAs were aligned against the mouse genome using BLAT. Those\nwith an alignment of less than 15% were discarded. When a single RNA\naligned in multiple places, the alignment having the highest base identity\nwas identified. Only alignments having a base identity level within 0.1% of\nthe best and at least 96% base identity with the genomic sequence were kept.\n
\n\n\nThis track was produced at UCSC from RNA sequence data generated by scientists\nworldwide and curated by the NCBI\nRefSeq project.\n
\n\n\nKent WJ.\n\nBLAT - the BLAST-like alignment tool.\nGenome Res. 2002 Apr;12(4):656-64.\nPMID: 11932250; PMC:
\nPruitt KD, Brown GR, Hiatt SM, Thibaud-Nissen F, Astashyn A, Ermolaeva O, Farrell CM, Hart J,\nLandrum MJ, McGarvey KM et al.\n\nRefSeq: an update on mammalian reference sequences.\nNucleic Acids Res. 2014 Jan;42(Database issue):D756-63.\nPMID: 24259432; PMC:
\nPruitt KD, Tatusova T, Maglott DR.\n\nNCBI Reference Sequence (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins.\nNucleic Acids Res. 2005 Jan 1;33(Database issue):D501-4.\nPMID: 15608248; PMC:
\nThe RefSeq mRNAs gene track for the mouse (Jun. 2020 (GRCm39/mm39))\ngenome assembly displays translated blat alignments of vertebrate and\ninvertebrate mRNA in\n GenBank.\n
\n\n\nTotal genome size: 2,654,624,157 (not counting gaps)
\nGene count: 22,442
\nBases in genes: 838,462,469 (txStart to txEnd)
\nGenes percent genome coverage: % 31.585
\nBases in exons: 53,564,706
\nExons percent genome coverage: % 2.018
\n
\nPlease note, the name searching system is not completely case insensitive.\nWhen in doubt, enter search names in all lower case to find gene names.\n
\n\n\nThe mRNAs were aligned against the mouse (Jun. 2020 (GRCm39/mm39)) genome using\ntranslated blat. When a single mRNA aligned in multiple places, the alignment\nhaving the highest base identity was found. Only those alignments having a base\nidentity level within 1% of the best and at least 25% base identity with the\ngenomic sequence were kept.\n
\n\n\nSpecifically, the translated blat command is:\n
\nblat -noHead -q=rnax -t=dnax -mask=lower target.fa query.fa target.query.psl\n\nwhere target.fa is one of the chromosome sequence of the genome assembly,\nand the query.fa is the mRNAs from RefSeq\n\nThe resulting PSL outputs are filtered:\n
\npslCDnaFilter -minId=0.35 -minCover=0.25 -globalNearBest=0.0100 -minQSize=20 \n -ignoreIntrons -repsAsMatch -ignoreNs -bestOverlap \n all.results.psl mm39.xenoRefGene.psl\n\nThe filtered mm39.xenoRefGene.psl is converted to\ngenePred data to display for this track.\n\n\n
\nThe mRNA track was produced at UCSC from mRNA sequence data\nsubmitted to the international public sequence databases by\nscientists worldwide.\n
\n\n\nBenson DA, Cavanaugh M, Clark K, Karsch-Mizrachi I, Lipman DJ, Ostell J, Sayers EW.\n\nGenBank.\nNucleic Acids Res. 2013 Jan;41(Database issue):D36-42.\nPMID: 23193287; PMC:
\nBenson DA, Karsch-Mizrachi I, Lipman DJ, Ostell J, Wheeler DL.\nGenBank: update.\nNucleic Acids Res. 2004 Jan 1;32(Database issue):D23-6.\nPMID: 14681350; PMC:
\nKent WJ.\nBLAT - the BLAST-like alignment tool.\nGenome Res. 2002 Apr;12(4):656-64.\nPMID: 11932250; PMC: