\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 A. gambiae anoGam3 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/anoGam3/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"], "formatDetails": { "feature": "jexl:{'Outside Link':feature.name?''+(''+feature.name)+'':undefined}" }, "displays": [ { "type": "LinearBasicDisplay", "displayId": "anoGam3-evaSnp4-LinearBasicDisplay", "mouseover": "jexl:`Ref/Alt allele(s): ${get(feature,'ref')}>${get(feature,'alt')}\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 A. gambiae anoGam3 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/anoGam3/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"], "formatDetails": { "feature": "jexl:{'Outside Link':feature.name?''+(''+feature.name)+'':undefined}" }, "displays": [ { "type": "LinearBasicDisplay", "displayId": "anoGam3-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 A. gambiae anoGam3 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\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 8 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., evaSnp8.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/anoGam3/bbi/evaSnp8.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 A. gambiae anoGam3 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\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 8 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., evaSnp8.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/anoGam3/bbi/evaSnp8.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 A. gambiae anoGam3 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\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 8 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., evaSnp8.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/anoGam3/bbi/evaSnp8.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\nThis track shows regions of the genome within 10,000 bp of transcribed regions and\nDNA sequences targetable by CRISPR RNA guides using the Cas9 enzyme\nfrom S. pyogenes (PAM: NGG).\nCRISPR target sites were annotated with predicted specificity\n(off-target effects) and predicted efficiency (on-target cleavage) by various\nalgorithms through the tool CRISPOR.\n
\n\n\nThe track "CRISPR Regions" shows the regions of the genome where target\nsites were analyzed, i.e. within 10,000 bp of transcribed regions as annotated by\nEnsembl transcript models.
\n\n\nThe track "CRISPR Targets" shows the target sites in these regions.\nThe target sequence of the guide is shown with a thick (exon) bar. The PAM\nmotif match (NGG) is shown with a thinner bar. Guides\nare colored to reflect both predicted specificity and efficiency. Specificity\nreflects the "uniqueness" of a 20mer sequence in the genome; the less unique a\nsequence is, the more likely it is to cleave other locations of the genome\n(off-target effects). Efficiency is the frequency of cleavage at the target\nsite (on-target efficiency).
\n\nShades of gray stand for sites that are hard to target specifically, as the\n20mer is not very unique in the genome:
\n| impossible to target: target site has at least one identical copy in the genome and was not scored | |
| hard to target: many similar sequences in the genome that alignment stopped, repeat? | |
| hard to target: target site was aligned but results in a low specificity score <= 50 (see below) |
Colors highlight targets that are specific in the genome (MIT specificity > 50) but have different predicted efficiencies:
\n| unable to calculate Doench/Fusi 2016 efficiency score | |
| low predicted cleavage: Doench/Fusi 2016 Efficiency percentile <= 30 | |
| medium predicted cleavage: Doench/Fusi 2016 Efficiency percentile > 30 and < 55 | |
| high predicted cleavage: Doench/Fusi 2016 Efficiency > 55 |
\nMouse-over a target site to show predicted specificity and efficiency scores:
\n
Click onto features to show all scores and predicted off-targets with up to\nfour mismatches. The Out-of-Frame score by Bae et al. 2014\nis correlated with\nthe probability that mutations induced by the guide RNA will disrupt the open\nreading frame. The authors recommend out-of-frame scores > 66 to create\nknock-outs with a single guide efficiently.
\n\n
Off-target sites are sorted by the CFD score (Doench et al. 2016). \nThe higher the CFD score, the more likely there is off-target cleavage at that site. \nOff-targets with a CFD score < 0.023 are not shown on this page, but are availble when \nfollowing the link to the external CRISPOR tool. \nWhen compared against experimentally validated off-targets by \nHaeussler et al. 2016, the large majority of predicted\noff-targets with CFD scores < 0.023 were false-positives. For storage and performance\nreasons, on the level of individual off-targets, only CFD scores are available.
\n\n\nLike most algorithms, the MIT specificity score is not always a perfect\npredictor of off-target effects. Despite low scores, many tested guides \ncaused few and/or weak off-target cleavage when tested with whole-genome assays\n(Figure 2 from Haeussler\net al. 2016), as shown below, and the published data contains few data points\nwith high specificity scores. Overall though, the assays showed that the higher\nthe specificity score, the lower the off-target effects.
\n\n![](\"https://genome.ucsc.edu/images/crisprFig_mitScore.png\")
\n\nSimilarly, efficiency scoring is not very accurate: guides with low\nscores can be efficient and vice versa. As a general rule, however, the higher\nthe score, the less likely that a guide is very inefficient. The\nfollowing histograms illustrate, for each type of score, how the share of\ninefficient guides drops with increasing efficiency scores:\n
\n\n![](\"https://genome.ucsc.edu/images/crisprFig_effScores.png\")
\n\nWhen reading this plot, keep in mind that both scores were evaluated on\ntheir own training data. Especially for the Moreno-Mateos score, the\nresults are too optimistic, due to overfitting. When evaluated on independent\ndatasets, the correlation of the prediction with other assays was around 25%\nlower, see Haeussler et al. 2016. At the time of\nwriting, there is no independent dataset available yet to determine the\nMoreno-Mateos accuracy for each score percentile range.
\n\n\nExons as predicted by Ensembl Gene models were used, extended by 10,000 basepairs\non each side, searched for the -NGG motif. Flanking 20mer guide sequences were\naligned to the genome with BWA and scored with MIT Specificity scores using the\ncommand-line version of crispor.org. Non-unique guide sequences were skipped.\nFlanking sequences were extracted from the genome and input for Crispor\nefficiency scoring, available from the
\nThe raw data can be explored interactively with the Table Browser.\nFor automated analysis, the genome annotation is stored in a bigBed file that\ncan be downloaded from\nour download server.\nThe files for this track are called crispr.bb and crisprDetails.tab. Individual\nregions or the whole genome annotation can be obtained using our tool bigBedToBed,\nwhich 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. The tool\ncan also be used to obtain only features within a given range, e.g. bigBedToBed\nhttp://hgdownload.soe.ucsc.edu/gbdb/anoGam3/crispr10K/crispr.bb -chrom=chr21\n-start=0 -end=1000000 stdout
\n\n\nTrack created by Maximilian Haeussler, with helpful input from Jean-Paul Concordet (MNHN Paris) and\nAlberto Stolfi (NYU). This track was built with financial support from
\nHaeussler M, Schönig K, Eckert H, Eschstruth A, Mianné J, Renaud JB, Schneider-Maunoury S,\nShkumatava A, Teboul L, Kent J et al.\nEvaluation of off-target and on-target scoring algorithms and integration into the\nguide RNA selection tool CRISPOR.\nGenome Biol. 2016 Jul 5;17(1):148.\nPMID: 27380939; PMC:
\nBae S, Kweon J, Kim HS, Kim JS.\n\nMicrohomology-based choice of Cas9 nuclease target sites.\nNat Methods. 2014 Jul;11(7):705-6.\nPMID: 24972169\n
\n\n\nDoench JG, Fusi N, Sullender M, Hegde M, Vaimberg EW, Donovan KF, Smith I, Tothova Z, Wilen C,\nOrchard R et al.\n\nOptimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9.\nNat Biotechnol. 2016 Feb;34(2):184-91.\nPMID: 26780180; PMC:
\nHsu PD, Scott DA, Weinstein JA, Ran FA, Konermann S, Agarwala V, Li Y, Fine EJ, Wu X, Shalem O\net al.\n\nDNA targeting specificity of RNA-guided Cas9 nucleases.\nNat Biotechnol. 2013 Sep;31(9):827-32.\nPMID: 23873081; PMC:
\nMoreno-Mateos MA, Vejnar CE, Beaudoin JD, Fernandez JP, Mis EK, Khokha MK, Giraldez AJ.\n\nCRISPRscan: designing highly efficient sgRNAs for CRISPR-Cas9 targeting in vivo.\nNat Methods. 2015 Oct;12(10):982-8.\nPMID: 26322839; PMC:
\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"] }, { "type": "FeatureTrack", "trackId": "anoGam3-cpgIslandExt", "name": "CpG Islands", "assemblyNames": ["anoGam3"], "adapter": { "type": "BedTabixAdapter", "bedGzLocation": { "uri": "cpgIslandExt.bed.gz" }, "index": { "indexType": "CSI", "location": { "uri": "cpgIslandExt.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. Miklem 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": "anoGam3-cpgIslandExtUnmasked", "name": "CpG Islands - Unmasked CpG", "assemblyNames": ["anoGam3"], "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. Miklem 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": "anoGam3-crispr10KRanges", "name": "CRISPR 10K - CRISPR Regions 10K", "assemblyNames": ["anoGam3"], "adapter": { "type": "BedTabixAdapter", "bedGzLocation": { "uri": "crispr10KRanges.bed.gz" }, "index": { "indexType": "CSI", "location": { "uri": "crispr10KRanges.bed.gz.csi" } } }, "metadata": { "ucsc": { "color": "110,110,110", "html": "\nThis track shows regions of the genome within 10,000 bp of transcribed regions and\nDNA sequences targetable by CRISPR RNA guides using the Cas9 enzyme\nfrom S. pyogenes (PAM: NGG).\nCRISPR target sites were annotated with predicted specificity\n(off-target effects) and predicted efficiency (on-target cleavage) by various\nalgorithms through the tool CRISPOR.\n
\n\n\nThe track "CRISPR Regions" shows the regions of the genome where target\nsites were analyzed, i.e. within 10,000 bp of transcribed regions as annotated by\nEnsembl transcript models.
\n\n\nThe track "CRISPR Targets" shows the target sites in these regions.\nThe target sequence of the guide is shown with a thick (exon) bar. The PAM\nmotif match (NGG) is shown with a thinner bar. Guides\nare colored to reflect both predicted specificity and efficiency. Specificity\nreflects the "uniqueness" of a 20mer sequence in the genome; the less unique a\nsequence is, the more likely it is to cleave other locations of the genome\n(off-target effects). Efficiency is the frequency of cleavage at the target\nsite (on-target efficiency).
\n\nShades of gray stand for sites that are hard to target specifically, as the\n20mer is not very unique in the genome:
\n| impossible to target: target site has at least one identical copy in the genome and was not scored | |
| hard to target: many similar sequences in the genome that alignment stopped, repeat? | |
| hard to target: target site was aligned but results in a low specificity score <= 50 (see below) |
Colors highlight targets that are specific in the genome (MIT specificity > 50) but have different predicted efficiencies:
\n| unable to calculate Doench/Fusi 2016 efficiency score | |
| low predicted cleavage: Doench/Fusi 2016 Efficiency percentile <= 30 | |
| medium predicted cleavage: Doench/Fusi 2016 Efficiency percentile > 30 and < 55 | |
| high predicted cleavage: Doench/Fusi 2016 Efficiency > 55 |
\nMouse-over a target site to show predicted specificity and efficiency scores:
\n
Click onto features to show all scores and predicted off-targets with up to\nfour mismatches. The Out-of-Frame score by Bae et al. 2014\nis correlated with\nthe probability that mutations induced by the guide RNA will disrupt the open\nreading frame. The authors recommend out-of-frame scores > 66 to create\nknock-outs with a single guide efficiently.
\n\n
Off-target sites are sorted by the CFD score (Doench et al. 2016). \nThe higher the CFD score, the more likely there is off-target cleavage at that site. \nOff-targets with a CFD score < 0.023 are not shown on this page, but are availble when \nfollowing the link to the external CRISPOR tool. \nWhen compared against experimentally validated off-targets by \nHaeussler et al. 2016, the large majority of predicted\noff-targets with CFD scores < 0.023 were false-positives. For storage and performance\nreasons, on the level of individual off-targets, only CFD scores are available.
\n\n\nLike most algorithms, the MIT specificity score is not always a perfect\npredictor of off-target effects. Despite low scores, many tested guides \ncaused few and/or weak off-target cleavage when tested with whole-genome assays\n(Figure 2 from Haeussler\net al. 2016), as shown below, and the published data contains few data points\nwith high specificity scores. Overall though, the assays showed that the higher\nthe specificity score, the lower the off-target effects.
\n\n\n\nSimilarly, efficiency scoring is not very accurate: guides with low\nscores can be efficient and vice versa. As a general rule, however, the higher\nthe score, the less likely that a guide is very inefficient. The\nfollowing histograms illustrate, for each type of score, how the share of\ninefficient guides drops with increasing efficiency scores:\n
\n\n\n\nWhen reading this plot, keep in mind that both scores were evaluated on\ntheir own training data. Especially for the Moreno-Mateos score, the\nresults are too optimistic, due to overfitting. When evaluated on independent\ndatasets, the correlation of the prediction with other assays was around 25%\nlower, see Haeussler et al. 2016. At the time of\nwriting, there is no independent dataset available yet to determine the\nMoreno-Mateos accuracy for each score percentile range.
\n\n\nExons as predicted by Ensembl Gene models were used, extended by 10,000 basepairs\non each side, searched for the -NGG motif. Flanking 20mer guide sequences were\naligned to the genome with BWA and scored with MIT Specificity scores using the\ncommand-line version of crispor.org. Non-unique guide sequences were skipped.\nFlanking sequences were extracted from the genome and input for Crispor\nefficiency scoring, available from the
\nThe raw data can be explored interactively with the Table Browser.\nFor automated analysis, the genome annotation is stored in a bigBed file that\ncan be downloaded from\nour download server.\nThe files for this track are called crispr.bb and crisprDetails.tab. Individual\nregions or the whole genome annotation can be obtained using our tool bigBedToBed,\nwhich 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. The tool\ncan also be used to obtain only features within a given range, e.g. bigBedToBed\nhttp://hgdownload.soe.ucsc.edu/gbdb/anoGam3/crispr10K/crispr.bb -chrom=chr21\n-start=0 -end=1000000 stdout
\n\n\nTrack created by Maximilian Haeussler, with helpful input from Jean-Paul Concordet (MNHN Paris) and\nAlberto Stolfi (NYU). This track was built with financial support from
\nHaeussler M, Schönig K, Eckert H, Eschstruth A, Mianné J, Renaud JB, Schneider-Maunoury S,\nShkumatava A, Teboul L, Kent J et al.\nEvaluation of off-target and on-target scoring algorithms and integration into the\nguide RNA selection tool CRISPOR.\nGenome Biol. 2016 Jul 5;17(1):148.\nPMID: 27380939; PMC:
\nBae S, Kweon J, Kim HS, Kim JS.\n\nMicrohomology-based choice of Cas9 nuclease target sites.\nNat Methods. 2014 Jul;11(7):705-6.\nPMID: 24972169\n
\n\n\nDoench JG, Fusi N, Sullender M, Hegde M, Vaimberg EW, Donovan KF, Smith I, Tothova Z, Wilen C,\nOrchard R et al.\n\nOptimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9.\nNat Biotechnol. 2016 Feb;34(2):184-91.\nPMID: 26780180; PMC:
\nHsu PD, Scott DA, Weinstein JA, Ran FA, Konermann S, Agarwala V, Li Y, Fine EJ, Wu X, Shalem O\net al.\n\nDNA targeting specificity of RNA-guided Cas9 nucleases.\nNat Biotechnol. 2013 Sep;31(9):827-32.\nPMID: 23873081; PMC:
\nMoreno-Mateos MA, Vejnar CE, Beaudoin JD, Fernandez JP, Mis EK, Khokha MK, Giraldez AJ.\n\nCRISPRscan: designing highly efficient sgRNAs for CRISPR-Cas9 targeting in vivo.\nNat Methods. 2015 Oct;12(10):982-8.\nPMID: 26322839; PMC:
\nThis track shows the gaps in the Oct. 2006 A. gambiae 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 Oct. 2006 A. gambiae 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
\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": "anoGam3-rmsk", "name": "RepeatMasker", "assemblyNames": ["anoGam3"], "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
\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\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\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 \nFAQ for \nmore information.
\n\n\nThanks to Arian Smit and GIRI\nfor providing the tools and repeat libraries used to generate this track.
\n\n\nRepbase Update is described in \nJurka, J. \nRepbase update: a database and an electronic journal of \nrepetitive elements.\nTrends Genet. 16(9), 418-420 (2000).\nPMID: 10973072\n
\n" } }, "description": "Repeating Elements by RepeatMasker", "category": ["Variation and Repeats"] }, { "type": "FeatureTrack", "trackId": "anoGam3-simpleRepeat", "name": "Simple Repeats", "assemblyNames": ["anoGam3"], "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:
\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"], "formatDetails": { "feature": "jexl:{'INSDC link':feature.name?''+(''+feature.name)+'':undefined}" } }, { "type": "FeatureTrack", "trackId": "anoGam3-ucscToRefSeq", "name": "RefSeq Acc", "assemblyNames": ["anoGam3"], "adapter": { "type": "BedTabixAdapter", "bedGzLocation": { "uri": "ucscToRefSeq.bed.gz" }, "index": { "indexType": "CSI", "location": { "uri": "ucscToRefSeq.bed.gz.csi" } } }, "metadata": { "ucsc": { "group": "map", "longLabel": "RefSeq Accession", "shortLabel": "RefSeq Acc", "track": "ucscToRefSeq", "type": "bed 4", "url": "https://www.ncbi.nlm.nih.gov/nuccore/$$", "urlLabel": "RefSeq accession:", "visibility": "hide", "html": "
\nThis track associates UCSC Genome Browser chromosome names to accession\nidentifiers from the NCBI Reference Sequence Database (RefSeq).\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" } }, "description": "RefSeq Accession", "category": ["Mapping and Sequencing"], "formatDetails": { "feature": "jexl:{'RefSeq accession':feature.name?''+(''+feature.name)+'':undefined}" } }, { "type": "FeatureTrack", "trackId": "anoGam3-windowmaskerSdust", "name": "WM + SDust", "assemblyNames": ["anoGam3"], "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 anoGam3.fa -output wm_counts\nwindowmasker -ustat wm_counts -sdust true -input anoGam3.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": "anoGam3-augustusGene", "name": "AUGUSTUS", "assemblyNames": ["anoGam3"], "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": "anoGam3-ensGene", "name": "Ensembl Genes", "assemblyNames": ["anoGam3"], "adapter": { "type": "Gff3TabixAdapter", "gffGzLocation": { "uri": "ensGene.gff.gz" }, "index": { "indexType": "CSI", "location": { "uri": "ensGene.gff.gz.csi" } } }, "metadata": { "ucsc": { "color": "150,0,0", "ensemblIdUrl": "http://metazoa.ensembl.org", "exonNumbers": "on", "group": "genes", "longLabel": "Ensembl Genes", "shortLabel": "Ensembl Genes", "track": "ensGene", "type": "genePred ensPep", "visibility": "pack", "html": "\nThese gene predictions were generated by Ensembl.\n
\n\n\nFor more information on the different gene tracks, see our Genes FAQ.
\n\n\nFor a description of the methods used in Ensembl gene predictions, please refer to\nHubbard et al. (2002), also listed in the References section below. \n
\n\n\nEnsembl Gene data can be explored interactively using the\nTable Browser or the\nData Integrator. \nFor local downloads, the genePred format files for anoGam3 are available in our\n\ndownloads directory as ensGene.txt.gz or in our\n\ngenes download directory in GTF format.
\nFor programmatic access, the data can be queried from the \nREST API or\ndirectly from our public MySQL\nservers. Instructions on this method are available on our\nMySQL help page and on\nour blog.
\nPrevious versions of this track can be found on our archive download server.\n
\n\n\nWe would like to thank Ensembl for providing these gene annotations. For more information, please see\nEnsembl's genome annotation page.\n
\n\n\nHubbard T, Barker D, Birney E, Cameron G, Chen Y, Clark L, Cox T, Cuff J,\nCurwen V, Down T et al.\nThe Ensembl genome database project.\nNucleic Acids Res. 2002 Jan 1;30(1):38-41.\nPMID: 11752248; PMC:
\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": "anoGam3-xenoRefGene", "name": "Other RefSeq", "assemblyNames": ["anoGam3"], "adapter": { "type": "Gff3TabixAdapter", "gffGzLocation": { "uri": "xenoRefGene.gff.gz" }, "index": { "indexType": "CSI", "location": { "uri": "xenoRefGene.gff.gz.csi" } } }, "metadata": { "ucsc": { "color": "12,12,120", "group": "genes", "longLabel": "Non-A. gambiae RefSeq Genes", "shortLabel": "Other RefSeq", "track": "xenoRefGene", "type": "genePred xenoRefPep xenoRefMrna", "visibility": "dense", "html": "\nThis track shows known protein-coding and non-protein-coding genes \nfor organisms other than A. gambiae, taken from the NCBI RNA reference \nsequences collection (RefSeq). The data underlying this track are \nupdated weekly.
\n\n\nThis track follows the display conventions for \ngene prediction \ntracks.\nThe color shading indicates the level of review the RefSeq record has \nundergone: predicted (light), provisional (medium), reviewed (dark).
\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
\nThe RNAs were aligned against the A. gambiae genome using blat; those\nwith an alignment of less than 15% were discarded. When a single RNA aligned \nin multiple places, the alignment having the highest base identity was \nidentified. Only alignments having a base identity level within 0.5% of \nthe best and at least 25% base identity with the genomic sequence were kept.\n
\n\n\nThis track was produced at UCSC from RNA sequence data\ngenerated by scientists worldwide and curated by the \nNCBI RefSeq project.
\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: