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Genomic variant annotation and prioritization with ANNOVAR and wANNOVAR

Abstract

Recent developments in sequencing techniques have enabled rapid and high-throughput generation of sequence data, democratizing the ability to compile information on large amounts of genetic variations in individual laboratories. However, there is a growing gap between the generation of raw sequencing data and the extraction of meaningful biological information. Here, we describe a protocol to use the ANNOVAR (ANNOtate VARiation) software to facilitate fast and easy variant annotations, including gene-based, region-based and filter-based annotations on a variant call format (VCF) file generated from human genomes. We further describe a protocol for gene-based annotation of a newly sequenced nonhuman species. Finally, we describe how to use a user-friendly and easily accessible web server called wANNOVAR to prioritize candidate genes for a Mendelian disease. The variant annotation protocols take 5–30 min of computer time, depending on the size of the variant file, and 5–10 min of hands-on time. In summary, through the command-line tool and the web server, these protocols provide a convenient means to analyze genetic variants generated in humans and other species.

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Figure 1: The three different types of annotations supported by ANNOVAR are gene-based, region-based and filter-based annotations.
Figure 2: Screenshot of wANNOVAR, including the general steps to upload and prioritize variants.
Figure 3: Screenshot of the wANNOVAR result page.

References

  1. 1

    Li, H. & Homer, N. A survey of sequence alignment algorithms for next-generation sequencing. Brief. Bioinform. 11, 473–483 (2010).

    CAS  Article  PubMed  Google Scholar 

  2. 2

    Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    CAS  Article  PubMed  Google Scholar 

  3. 3

    Langmead, B., Trapnell, C., Pop, M. & Salzberg, S.L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).

    Article  PubMed  Google Scholar 

  4. 4

    Nagarajan, N. & Pop, M. Sequence assembly demystified. Nat. Rev. Genet. 14, 157–167 (2013).

    CAS  Article  PubMed  Google Scholar 

  5. 5

    Li, H. Exploring single-sample SNP and INDEL calling with whole-genome de novo assembly. Bioinformatics 28, 1838–1844 (2012).

    CAS  Article  PubMed  Google Scholar 

  6. 6

    Simpson, J.T. et al. ABySS: a parallel assembler for short read sequence data. Genome Res. 19, 1117–1123 (2009).

    CAS  Article  PubMed  Google Scholar 

  7. 7

    Xie, Y. et al. SOAPdenovo-Trans: de novo transcriptome assembly with short RNA-seq reads. Bioinformatics 30, 1660–1666 (2014).

    CAS  Article  PubMed  Google Scholar 

  8. 8

    Andrews, S. FastQC: a quality control tool for high throughput sequence data. http://www.bioinformatics.babraham.ac.uk/projects/fastqc (2010).

  9. 9

    Nielsen, R., Paul, J.S., Albrechtsen, A. & Song, Y.S. Genotype and SNP calling from next-generation sequencing data. Nat. Rev. Genet. 12, 443–451 (2011).

    CAS  Article  PubMed  Google Scholar 

  10. 10

    McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).

    CAS  Article  PubMed  Google Scholar 

  11. 11

    Zhao, M., Wang, Q., Wang, Q., Jia, P. & Zhao, Z. Computational tools for copy number variation (CNV) detection using next-generation sequencing data: features and perspectives. BMC Bioinformatics 14, S1 (2013).

    Article  PubMed  Google Scholar 

  12. 12

    Abyzov, A., Urban, A.E., Snyder, M. & Gerstein, M. CNVnator: an approach to discover, genotype, and characterize typical and atypical CNVs from family and population genome sequencing. Genome Res. 21, 974–984 (2011).

    CAS  Article  PubMed  Google Scholar 

  13. 13

    Zhu, M. et al. Using ERDS to infer copy-number variants in high-coverage genomes. Am. J. Hum. Genet. 91, 408–421 (2012).

    CAS  Article  PubMed  Google Scholar 

  14. 14

    Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010).

    Article  PubMed  Google Scholar 

  15. 15

    McLaren, W. et al. Deriving the consequences of genomic variants with the Ensembl API and SNP Effect Predictor. Bioinformatics 26, 2069–2070 (2010).

    CAS  Article  PubMed  Google Scholar 

  16. 16

    De Baets, G. et al. SNPeffect 4.0: on-line prediction of molecular and structural effects of protein-coding variants. Nucleic Acids Res. 40 (Database issue): D935–D939 (2012).

    CAS  Article  PubMed  Google Scholar 

  17. 17

    Hu, H. et al. VAAST 2.0: improved variant classification and disease-gene identification using a conservation-controlled amino acid substitution matrix. Genet. Epidemiol. 37, 622–634 (2013).

    Article  PubMed  Google Scholar 

  18. 18

    Makarov, V. et al. AnnTools: a comprehensive and versatile annotation toolkit for genomic variants. Bioinformatics 28, 724–725 (2012).

    CAS  Article  PubMed  Google Scholar 

  19. 19

    Michaelson, J.J. et al. Whole-genome sequencing in autism identifies hot spots for de novo germline mutation. Cell 151, 1431–1442 (2012).

    CAS  Article  PubMed  Google Scholar 

  20. 20

    Girard, S.L. et al. Increased exonic de novo mutation rate in individuals with schizophrenia. Nat. Genet. 43, 860–863 (2011).

    CAS  Article  PubMed  Google Scholar 

  21. 21

    Weedon, M.N. et al. Exome sequencing identifies a DYNC1H1 mutation in a large pedigree with dominant axonal Charcot-Marie-Tooth disease. Am. J. Hum. Genet. 89, 308–312 (2011).

    CAS  Article  PubMed  Google Scholar 

  22. 22

    Lai, C.-C. et al. Whole-exome sequencing to identify a novel LMNA gene mutation associated with inherited cardiac conduction disease. PLoS ONE 8, e83322 (2013).

    Article  PubMed  Google Scholar 

  23. 23

    Brownstein, C.A. et al. An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in the CLARITY Challenge. Genome Biol. 15, R53 (2014).

    Article  PubMed  Google Scholar 

  24. 24

    Liu, J. et al. Regenerative phenotype in mice with a point mutation in transforming growth factor β type I receptor (TGFBR1). Proc. Natl. Acad. Sci. USA 108, 14560–14565 (2011).

    CAS  Article  PubMed  Google Scholar 

  25. 25

    Nam, K. et al. Strong selective sweeps associated with ampliconic regions in great ape X chromosomes. arXiv:1402.5790 (2014).

  26. 26

    Chang, X. & Wang, K. wANNOVAR: annotating genetic variants for personal genomes via the web. J. Med. Genet. 49, 433–436 (2012).

    Article  PubMed  Google Scholar 

  27. 27

    Yang, H., Robinson, P.N. & Wang, K. Phenolyzer: phenotype-based prioritization of candidate genes for human diseases. Nat. Methods 10.1038/nmeth.3484 (20 July 2015).

  28. 28

    Siepel, A. et al. Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes. Genome Res. 15, 1034–1050 (2005).

    CAS  Article  PubMed  Google Scholar 

  29. 29

    Lewis, B.P., Shih, I.-h., Jones-Rhoades, M.W., Bartel, D.P. & Burge, C.B. Prediction of mammalian microRNA targets. Cell 115, 787–798 (2003).

    CAS  Article  PubMed  Google Scholar 

  30. 30

    Birney, E. et al. Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. Nature 447, 799–816 (2007).

    CAS  Article  PubMed  Google Scholar 

  31. 31

    Consortium, G.P. An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56–65 (2012).

    Article  Google Scholar 

  32. 32

    Fu, W. et al. Analysis of 6,515 exomes reveals the recent origin of most human protein-coding variants. Nature 493, 216–220 (2013).

    CAS  Article  PubMed  Google Scholar 

  33. 33

    Ng, P.C. & Henikoff, S. SIFT: predicting amino acid changes that affect protein function. Nucleic Acids Res. 31, 3812–3814 (2003).

    CAS  Article  PubMed  Google Scholar 

  34. 34

    Adzhubei, I.A. et al. A method and server for predicting damaging missense mutations. Nat. Methods 7, 248–249 (2010).

    CAS  Article  PubMed  Google Scholar 

  35. 35

    Sherry, S.T. et al. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 29, 308–311 (2001).

    CAS  Article  PubMed  Google Scholar 

  36. 36

    Lyon, G.J. & Wang, K. Identifying disease mutations in genomic medicine settings: current challenges and how to accelerate progress. Genome Med. 4, 58 (2012).

    Article  PubMed  Google Scholar 

  37. 37

    Hu, H. et al. A unified test of linkage analysis and rare-variant association for analysis of pedigree sequence data. Nat. Biotechnol. 32, 663–669 (2014).

    CAS  Article  PubMed  Google Scholar 

  38. 38

    Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly 6, 80–92 (2012).

    CAS  Article  PubMed  Google Scholar 

  39. 39

    Paila, U., Chapman, B.A., Kirchner, R. & Quinlan, A.R. GEMINI: integrative exploration of genetic variation and genome annotations. PLoS Comput. Biol. 9, e1003153 (2013).

    CAS  Article  PubMed  Google Scholar 

  40. 40

    Habegger, L. et al. VAT: a computational framework to functionally annotate variants in personal genomes within a cloud-computing environment. Bioinformatics 28, 2267–2269 (2012).

    CAS  Article  PubMed  Google Scholar 

  41. 41

    Ng, S.B. et al. Exome sequencing identifies the cause of a Mendelian disorder. Nature Genet. 42, 30–35 (2010).

    CAS  Article  PubMed  Google Scholar 

  42. 42

    Vuong, H. et al. AVIA v2.0: annotation, visualization and impact analysis of genomic variants and genes. Bioinformatics 31, 2748–2750 (2015).

    CAS  Article  PubMed  Google Scholar 

  43. 43

    Medina, I. et al. VARIANT: command line, web service and web interface for fast and accurate functional characterization of variants found by next-generation sequencing. Nucleic Acids Res. 40, W54–W58 (2012).

    CAS  Article  PubMed  Google Scholar 

  44. 44

    McCarthy, D.J. et al. Choice of transcripts and software has a large effect on variant annotation. Genome Med. 6, 26 (2014).

    Article  PubMed  Google Scholar 

  45. 45

    Dong, C. et al. Comparison and integration of deleteriousness prediction methods for nonsynonymous SNVs in whole-exome sequencing studies. Hum. Mol. Genet. 24, 2125–2137 (2015).

    CAS  Article  PubMed  Google Scholar 

  46. 46

    Kircher, M. et al. A general framework for estimating the relative pathogenicity of human genetic variants. Nat. Genet. 46, 310–315 (2014).

    CAS  Article  PubMed  Google Scholar 

  47. 47

    Pollard, K.S., Hubisz, M.J., Rosenbloom, K.R. & Siepel, A. Detection of nonneutral substitution rates on mammalian phylogenies. Genome Res. 20, 110–121 (2010).

    CAS  Article  PubMed  Google Scholar 

  48. 48

    Eilbeck, K. et al. The Sequence Ontology: a tool for the unification of genome annotations. Genome Biol. 6, R44 (2005).

    Article  PubMed  Google Scholar 

  49. 49

    Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  PubMed  Google Scholar 

  50. 50

    Consortium, G.P. A map of human genome variation from population-scale sequencing. Nature 467, 1061–1073 (2010).

    Article  Google Scholar 

  51. 51

    Liu, X., Jian, X. & Boerwinkle, E. dbNSFP v2.0: a database of human non-synonymous SNVs and their functional predictions and annotations. Hum. Mutat. 34, E2393–E2402 (2013).

    CAS  Article  PubMed  Google Scholar 

  52. 52

    Landrum, M.J. et al. ClinVar: public archive of relationships among sequence variation and human phenotype. Nucleic Acids Res. 42 (Database issue): D980–D985 (2014).

    CAS  Article  PubMed  Google Scholar 

  53. 53

    Day, I.N. dbSNP in the detail and copy number complexities. Hum. Mutat. 31, 2–4 (2010).

    CAS  Article  PubMed  Google Scholar 

  54. 54

    Karolchik, D. et al. The UCSC genome browser database: 2014 update. Nucleic Acids Res. 42, D764–D770 (2014).

    CAS  Article  PubMed  Google Scholar 

  55. 55

    Pruitt, K.D., Tatusova, T. & Maglott, D.R. NCBI reference sequences (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res. 35, D61–D65 (2007).

    CAS  Article  PubMed  Google Scholar 

  56. 56

    Hsu, F. et al. The UCSC known genes. Bioinformatics 22, 1036–1046 (2006).

    CAS  Article  PubMed  Google Scholar 

  57. 57

    Hubbard, T. et al. The Ensembl genome database project. Nucleic Acids Res. 30, 38–41 (2002).

    CAS  Article  PubMed  Google Scholar 

  58. 58

    Derrien, T. et al. The GENCODE v7 catalog of human long noncoding RNAs: analysis of their gene structure, evolution, and expression. Genome Res. 22, 1775–1789 (2012).

    CAS  Article  PubMed  Google Scholar 

  59. 59

    Ng, P.C. SIFT: predicting amino acid changes that affect protein function. Nucleic Acids Res. 31, 3812–3814 (2003).

    CAS  Article  PubMed  Google Scholar 

  60. 60

    Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).

    CAS  Article  PubMed  Google Scholar 

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Acknowledgements

Development of the ANNOVAR/wANNOVAR tool is supported by US National Institutes of Health (NIH) grant R01 HG006465. We thank X. Chang for the initial development of the wANNOVAR server. We thank all ANNOVAR and wANNOVAR users for their helpful suggestions, comments and bug reports to improve the software tools and web servers.

Author information

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Authors

Contributions

H.Y. and K.W. drafted and revised this manuscript.

Corresponding author

Correspondence to Kai Wang.

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Competing interests

K.W. is a shareholder and board member of Tute Genomics.

Integrated supplementary information

Supplementary Figure 1 The expected results for discovering candidate genes of the ‘hemolytic anemia’ example in the Phenolyzer website.

Each ball represents one of the top 50 ranked genes. The larger the ball, the higher the ranking. The blue balls represent disease genes reported before and the yellow ones represent predicted disease genes. For detailed explanations on each color and shape, and on how the algorithm works to find disease genes, please visit http://phenolyzer.usc.edu/FAQ.php

Supplementary Figure 2 Explanation of each column in the wANNOVAR web view.

This is a sample output with default parameters. The first 5 columns represent the original information on the input variants. The following 5 columns give gene-based annotations on each variant. The following columns give region-based and filter-based annotations on each variant.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1 and 2, Supplementary Tables 1–3 (PDF 841 kb)

Supplementary Data

ANNOVAR and VEP comparison results (XLSX 453 kb)

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Yang, H., Wang, K. Genomic variant annotation and prioritization with ANNOVAR and wANNOVAR. Nat Protoc 10, 1556–1566 (2015). https://doi.org/10.1038/nprot.2015.105

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