ANNOVAR main package
Please join the ANNOVAR mailing list at google groups here to receive announcements on software updates.
The latest version of ANNOVAR (2017Jun01) can be downloaded here (registration required).
ANNOVAR is written in Perl and can be run as a standalone application on diverse hardware systems where standard Perl modules are installed.
Additional databases
Many of the databases that ANNOVAR uses can be directly retrieved from UCSC Genome Browser Annotation Database by -downdb argument.
Several very commonly used annotation databases for human genomes are additionally provided below. In general, users can use -downdb -webfrom annovar in ANNOVAR directly to download these databases. To view of full list of databases (and their size and last changed date) prepared by ANNOVAR developers, use avdblist keyword in -downdb operation.
- For gene-based annotation
| Build | Table Name | Explanation | Date |
|---|---|---|---|
| hg18 | refGene | FASTA sequences for all annotated transcripts in RefSeq Gene | 20170601 |
| hg19 | refGene | same as above | 20170601 |
| hg38 | refGene | save as above | 20170601 |
| hg18 | knownGene | FASTA sequences for all annotated transcripts in UCSC Known Gene | 20170601 |
| hg19 | knownGene | same as above | 20170601 |
| hg38 | knownGene | same as above | 20170601 |
| hg18 | ensGene | FASTA sequences for all annotated transcripts in ENSEMBL Gene | 20170601 |
| hg19 | ensGene | same as above | 20170601 |
- For filter-based annotation
| Build | Table Name | Explanation | Date |
|---|---|---|---|
| hg18 | avsift | whole-exome SIFT scores for non-synonymous variants (obselete and should not be uesd any more) | 20120222 |
| hg19 | avsift | same as above | 20120222 |
| hg18 | ljb26_all | whole-exome SIFT, PolyPhen2 HDIV, PolyPhen2 HVAR, LRT, MutationTaster, MutationAssessor, FATHMM, MetaSVM, MetaLR, VEST, CADD, GERP++, PhyloP and SiPhy scores from dbNSFP version 2.6 | 20140925 |
| hg19 | ljb26_all | same as above | 20140925 |
| hg38 | ljb26_all | same as above | 20150520 |
| hg18 | dbnsfp30a | whole-exome SIFT, PolyPhen2 HDIV, PolyPhen2 HVAR, LRT, MutationTaster, MutationAssessor, FATHMM, MetaSVM, MetaLR, VEST, CADD, GERP++, DANN, fitCons, PhyloP and SiPhy scores from dbNSFP version 3.0a | 20151015 |
| hg19 | dbnsfp30a | same as above | 20151015 |
| hg38 | dbnsfp30a | same as above | 20151015 |
| hg19 | dbnsfp31a_interpro | protein domain for variants | 20151219 |
| hg38 | dbnsfp31a_interpro | same as above | 20151219 |
| hg18 | dbnsfp33a | whole-exome SIFT, PolyPhen2 HDIV, PolyPhen2 HVAR, LRT, MutationTaster, MutationAssessor, FATHMM, PROVEAN, MetaSVM, MetaLR, VEST, M-CAP, CADD, GERP++, DANN, fathmm-MKL, Eigen, GenoCanyon, fitCons, PhyloP and SiPhy scores from dbNSFP version 3.3a | 20170221 |
| hg19 | dbnsfp33a | same as above | 20170221 |
| hg38 | dbnsfp33a | same as above | 20170221 |
| hg19 | dbscsnv11 | dbscSNV version 1.1 for splice site prediction by AdaBoost and Random Forest | 20151218 |
| hg38 | dbscsnv11 | same as above | 20151218 |
| hg19 | intervar_20170202 | InterVar: clinical interpretation of missense variants | 20170202 |
| hg18 | cg46 | alternative allele frequency in 46 unrelated human subjects sequenced by Complete Genomics | 20120222 |
| hg19 | cg46 | same as above | index updated 2012Feb22 |
| hg18 | cg69 | allele frequency in 69 human subjects sequenced by Complete Genomics | 20120222 |
| hg19 | cg69 | same as above | 20120222 |
| hg19 | cosmic64 | COSMIC database version 64 | 20130520 |
| hg19 | cosmic65 | COSMIC database version 65 | 20130706 |
| hg19 | cosmic67 | COSMIC database version 67 | 20131117 |
| hg19 | cosmic67wgs | COSMIC database version 67 on WGS data | 20131117 |
| hg19 | cosmic68 | COSMIC database version 68 | 20140224 |
| hg19 | cosmic68wgs | COSMIC database version 68 on WGS data | 20140224 |
| hg19 | cosmic70 | same as above | 20140911 |
| hg18 | cosmic70 | same as above | 20150428 |
| hg38 | cosmic70 | same as above | 20150428 |
| hg39 | cosmic71, 72, ..., 80 | read here | |
| hg18 | esp6500siv2_ea | alternative allele frequency in European American subjects in the NHLBI-ESP project with 6500 exomes, including the indel calls and the chrY calls. This is lifted over from hg19 by myself | 20141222 |
| hg19 | esp6500siv2_ea | same as above | 20141222 |
| hg38 | esp6500siv2_ea | same as above, lifted over from hg19 by myself | 20141222 |
| hg18 | esp6500siv2_aa | alternative allele frequency in African American subjects in the NHLBI-ESP project with 6500 exomes, including the indel calls and the chrY calls. This is lifted over from hg19 by myself. | 20141222 |
| hg19 | esp6500siv2_aa | same as above | 20141222 |
| hg38 | esp6500siv2_aa | same as above, lifted over from hg19 by myself | 20141222 |
| hg18 | esp6500siv2_all | alternative allele frequency in All subjects in the NHLBI-ESP project with 6500 exomes, including the indel calls and the chrY calls. This is lifted over from hg19 by myself. | 20141222 |
| hg19 | esp6500siv2_all | same as above | 20141222 |
| hg38 | esp6500siv2_all | same as above, lifted over from hg19 by myself | 20141222 |
| hg19 | exac03 | ExAC 65000 exome allele frequency data for ALL, AFR (African), AMR (Admixed American), EAS (East Asian), FIN (Finnish), NFE (Non-finnish European), OTH (other), SAS (South Asian)). version 0.3. Left normalization done. | 20151129 |
| hg18 | exac03 | same as above | 20151129 |
| hg38 | exac03 | same as above | 20151129 |
| hg19 | exac03nontcga | ExAC on non-TCGA samples (updated header) | 20160423 |
| hg38 | exac03nontcga | same as above | 20160423 |
| hg19 | exac03nonpsych | ExAC on non-Psychiatric disease samples (updated header) | 20160423 |
| hg38 | exac03nonpsych | same as above | 20160423 |
| hg19 | gnomad_exome | gnomAD exome collection | 20170311 |
| hg38 | gnomad_exome | gnomAD exome collection | 20170311 |
| hg19 | gnomad_genome | gnomAD genome collection | 20170311 |
| hg38 | gnomad_genome | gnomAD genome collection | 20170311 |
| hg19 | kaviar_20150923 | 170 million Known VARiants from 13K genomes and 64K exomes in 34 projects | 20151203 |
| hg38 | kaviar_20150923 | same as above | 20151203 |
| hg19 | hrcr1 | 40 million variants from 32K samples in haplotype reference consortium | 20151203 |
| hg38 | hrcr1 | same as above | 20151203 |
| hg18 | 1000g (3 data sets) | alternative allele frequency data in 1000 Genomes Project | 20120222 |
| hg18 | 1000g2010 (3 data sets) | same as above | 20120222 |
| hg18 | 1000g2010jul (3 data sets) | same as above | 20120222 |
| hg18 | 1000g2012apr | I lifted over the latest 1000 Genomes Project data to hg18, to help researchers working with hg18 coordinates | 20120820 |
| hg19 | 1000g2010nov | same as above | 20120222 |
| hg19 | 1000g2011may | same as above | 20120222 |
| hg19 | 1000g2012feb | same as above | 20130308 |
| hg18 | 1000g2012apr (5 data sets) | This is done by liftOver of the hg19 data below. It contains alternative allele frequency data in 1000 Genomes Project for ALL, AMR (admixed american), EUR (european), ASN (asian), AFR (african) populations | 20130508 |
| hg19 | 1000g2012apr (5 data sets) | alternative allele frequency data in 1000 Genomes Project for ALL, AMR (admixed american), EUR (european), ASN (asian), AFR (african) populations | 20120525 |
| hg19 | 1000g2014aug (6 data sets) | alternative allele frequency data in 1000 Genomes Project for autosomes (ALL, AFR (African), AMR (Admixed American), EAS (East Asian), EUR (European), SAS (South Asian)). Based on 201408 collection v4 (based on 201305 alignment) | 20140915 |
| hg19 | 1000g2014sep (6 data sets) | alternative allele frequency data in 1000 Genomes Project for autosomes (ALL, AFR (African), AMR (Admixed American), EAS (East Asian), EUR (European), SAS (South Asian)). Based on 201409 collection v5 (based on 201305 alignment) | 20140925 |
| hg19 | 1000g2014oct (6 data sets) | alternative allele frequency data in 1000 Genomes Project for autosomes (ALL, AFR (African), AMR (Admixed American), EAS (East Asian), EUR (European), SAS (South Asian)). Based on 201409 collection v5 (based on 201305 alignment) but including chrX and chrY data finally! | 20141216 |
| hg18 | 1000g2014oct (6 data sets) | same as above | 20150428 |
| hg38 | 1000g2014oct (6 data sets) | same as above | 20150424 |
| hg19 | 1000g2015aug (6 data sets) | The 1000G team fixed a bug in chrX frequency calculation. Based on 201508 collection v5b (based on 201305 alignment) | 20150824 |
| hg38 | 1000g2015aug (6 data sets) | same as above | 20150824 |
| hg19 | gme | Great Middle East allele frequency including NWA (northwest Africa), NEA (northeast Africa), AP (Arabian peninsula), Israel, SD (Syrian desert), TP (Turkish peninsula) and CA (Central Asia) | 20161024 |
| hg38 | gme | same as above | 20161024 |
| hg19 | mcap | M-CAP scores for non-synonymous variants | 20161104 |
| hg38 | mcap | same as above | 20161104 |
| hg19 | revel | REVEL scores for non-synonymous variants | 20161205 |
| hg38 | revel | same as above | 20161205 |
| hg18 | snp128 | dbSNP with ANNOVAR index files | 20120222 |
| hg18 | snp129 | same as above | 20120222 |
| hg19 | snp129 | liftover from hg18_snp129.txt | 20120809 |
| hg18 | snp130 | same as above | 20120222 |
| hg19 | snp130 | same as above | 20120222 |
| hg18 | snp131 | same as above | 20120222 |
| hg19 | snp131 | same as above | 20120222 |
| hg18 | snp132 | same as above | 20120222 |
| hg19 | snp132 | same as above | 20120222 |
| hg18 | snp135 | I lifted over SNP135 to hg18 | 20120820 |
| hg19 | snp135 | same as above | 20120222 |
| hg19 | snp137 | same as above | 20130109 |
| hg18 | snp138 | I lifted over SNP138 to hg18 | 20140910 |
| hg19 | snp138 | same as above | file and index updated 20140910 |
| hg19 | avsnp138 | dbSNP138 with allelic splitting and left-normalization | 20141223 |
| hg19 | avsnp142 | dbSNP142 with allelic splitting and left-normalization | 20141228 |
| hg19 | avsnp144 | dbSNP144 with allelic splitting and left-normalization (careful with bugs!) | 20151102 |
| hg38 | avsnp144 | same as above | 20151102 |
| hg19 | avsnp147 | dbSNP147 with allelic splitting and left-normalization | 20160606 |
| hg38 | avsnp142 | dbSNP142 with allelic splitting and left-normalization | 20160106 |
| hg38 | avsnp144 | dbSNP144 with allelic splitting and left-normalization | 20151102 |
| hg38 | avsnp147 | dbSNP147 with allelic splitting and left-normalization | 20160606 |
| hg18 | snp128NonFlagged | dbSNP with ANNOVAR index files, after removing those flagged SNPs (SNPs < 1% minor allele frequency (MAF) (or unknown), mapping only once to reference assembly, flagged in dbSnp as "clinically associated") | 20120524 |
| hg18 | snp129NonFlagged | same as above | 20120524 |
| hg18 | snp130NonFlagged | same as above | 20120524 |
| hg19 | snp130NonFlagged | same as above | 20120524 |
| hg18 | snp131NonFlagged | same as above | 20120524 |
| hg19 | snp131NonFlagged | same as above | 20120524 |
| hg18 | snp132NonFlagged | same as above | 20120524 |
| hg19 | snp132NonFlagged | same as above | 20120524 |
| hg19 | snp135NonFlagged | same as above | 20120524 |
| hg19 | snp137NonFlagged | same as above | 20130109 |
| hg19 | snp138NonFlagged | same as above | 20140222 |
| hg19 | nci60 | NCI-60 human tumor cell line panel exome sequencing allele frequency data | 20130724 |
| hg18 | nci60 | same as above | 20150428 |
| hg38 | nci60 | same as above | 20150428 |
| hg19 | icgc21 | International Cancer Genome Consortium version 21 | 20160622 |
| hg19 | clinvar_20131105 | CLINVAR database with Variant Clinical Significance (unknown, untested, non-pathogenic, probable-non-pathogenic, probable-pathogenic, pathogenic, drug-response, histocompatibility, other) and Variant disease name | 20140430 |
| hg19 | clinvar_20140211 | same as above | 20140430 |
| hg19 | clinvar_20140303 | same as above | 20140430 |
| hg19 | clinvar_20140702 | same as above | 20140712 |
| hg38 | clinvar_20140702 | same as above | 20140712 |
| hg19 | clinvar_20140902 | same as above | 20140911 |
| hg38 | clinvar_20140902 | same as above | 20140911 |
| hg19 | clinvar_20140929 | same as above | 20141002 |
| hg19 | clinvar_20150330 | same as above but with variant normalization | 20150413 |
| hg38 | clinvar_20150330 | same as above but with variant normalization | 20150413 |
| hg19 | clinvar_20150629 | same as above but with variant normalization | 20150724 |
| hg38 | clinvar_20150629 | same as above but with variant normalization | 20150724 |
| hg19 | clinvar_20151201 | Clinvar version 20151201 with separate columns (CLINSIG CLNDBN CLNACC CLNDSDB CLNDSDBID) | 20160303 |
| hg38 | clinvar_20151201 | same as avove | 20160303 |
| hg19 | clinvar_20160302 | Clinvar version 20160302 with separate columns (CLINSIG CLNDBN CLNACC CLNDSDB CLNDSDBID) | 20160303 |
| hg38 | clinvar_20160302 | same as above | 20160303 |
| hg19 | clinvar_20161128 | Clinvar version 20161128 with separate columns (CLINSIG CLNDBN CLNACC CLNDSDB CLNDSDBID) | 20161205 |
| hg38 | clinvar_20161128 | same as above (updated 20170215 to add missing header line) | 20170215 |
| hg19 | clinvar_20170130 | Clinvar version 20170130 with separate columns (CLINSIG CLNDBN CLNACC CLNDSDB CLNDSDBID) | 20170215 |
| hg38 | clinvar_20170130 | same as above | 20170125 |
| hg19 | popfreq_max_20150413 | A database containing the maximum allele frequency from 1000G, ESP6500, ExAC and CG46 | 20150413 |
| hg19 | popfreq_all_20150413 | A database containing all allele frequency from 1000G, ESP6500, ExAC and CG46 | 20150413 |
| hg19 | mitimpact2 | pathogenicity predictions of human mitochondrial missense variants (see here | 20150520 |
| hg19 | mitimpact24 | same as above with version 2.4 | 20160123 |
| hg18 | gerp++elem | conserved genomic regions by GERP++ | 20140223 |
| hg19 | gerp++elem | same as above | 20140223 |
| mm9 | gerp++elem | same as above | 20140223 |
| hg18 | gerp++gt2 | whole-genome GERP++ scores greater than 2 (RS score threshold of 2 provides high sensitivity while still strongly enriching for truly constrained sites. ) | 20120621 |
| hg19 | gerp++gt2 | same as above | 20120621 |
| hg19 | caddgt20 | with score>20 | 20160607 |
| hg19 | caddgt10 | CADD with score>10 | 20160607 |
| hg19 | cadd | CADD | 20140223 |
| hg19 | cadd13 | CADD version 1.3 | 20170123 |
| hg19 | cadd13gt10 | CADD version 1.3 score>10 | 20170123 |
| hg19 | cadd13gt20 | CADD version 1.3 score>20 | 20170123 |
| hg19 | caddindel | removed | 20150505 |
| hg19 | fathmm | whole-genome FATHMM_coding and FATHMM_noncoding scores (noncoding and coding scores in the 2015 version was reversed) | 20160315 |
| hg19 | gwava | whole genome GWAVA_region_score GWAVA_tss_score GWAVA_unmatched_score, see ref | 20150623 |
| hg19 | eigen | whole-genome Eigen scores, see ref | 20160330 |
User-contributed datasets
Several generous ANNOVAR users provide additional annotation datasets that may help other users. These datasets are described below:
- MitImpact2: pathogenicity predictions of human mitochondrial missense variants. This is prepared as filter-based annotation format and users can directly download from ANNOVAR (see table above).
- LoFtool score: gene loss-of-function score percentiles. The smaller the percentile, the most intolerant is the gene to functional variation. The file can be downloaded here. Manuscript in preparation (please contact Dr. Joao Fadista - joao.fadista@med.lu.se). The authors would like to thank the Exome Aggregation Consortium and the groups that provided exome variant data for comparison. A full list of contributing groups can be found at http://exac.broadinstitute.org/about.
- RVIS-ESV score: RVIS score measures genetic intolerance of genes to functional mutations, as described in Petrovski et al. Original RVIS was constructed based on patterns of standing variation in 6503 samples. The authors have recently constructed scores based on the ~61,000 samples from ExAC. There is high correlation, but more resolution for many genes. The ExAC cohort implementation is what we consider RVIS (v2). It can be downloaded here.
- GDI score: the gene damage index (GDI) is describing the accumulated mutational damage for each human gene in the general population, and shows that highly mutated/damaged genes are unlikely to be disease-causing and yet they generate a big proportion of false positive variants harbored in such genes. Therefore removing high GDI genes is a very effective way to remove confidently false positives from WES/WGS data. More details were given in this paper. The data set includes general damage prediction (low/medium/high) for different disease type (all, Mendelian, cancer, and PID) and can be downloaded from here.
- TMC-SNPDB: SNP database from whole exome data of 62 normal samples derived from cancer patients of Indian origin, representing 114, 309 unique germline variants. Read the manuscript here. It is useful for exome sequencing studies on Indian populations and can be downloaded from here.
Third-party datasets
Several third-party researchers have provided additional annotation datasets that can be used by ANNOVAR directly. However, users need to agree to specific license terms set forth by the third parties:
- SPIDEX: SPIDEX 1.0 - Deep Genomics : (Xiong et al, Science 2015) Machine-learning prediction on how genetic variants affect RNA splicing. This dataset can be downloaded here.