VARPIN - Variant Prioritization and Interpretation for Genetic Analysis

References

We sincerely thank the following databases, which are used as reference data for our software in order to improve the efficiency of the calculation.
Name Reference Website
ANNOVAR Wang K, Li M, Hakonarson H. ANNOVAR: Functional annotation of genetic variants from next-generation sequencing data Nucleic Acids Research, 38:e164, 2010 http://annovar.openbioinformatics.org/
refGene O'Leary NA, Wright MW, Brister JR, et al. Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Res. 2016;44(D1):D733–D745. doi:10.1093/nar/gkv1189 https://www.ncbi.nlm.nih.gov/refseq/
regSNP-intron Lin, H., Hargreaves, K.A., Li, R. et al. RegSNPs-intron: a computational framework for predicting pathogenic impact of intronic single nucleotide variants. Genome Biol 20, 254 (2019) doi:10.1186/s13059-019-1847-4 http://clark.compbio.iupui.edu/regsnp_intron_web/
regSNP-splicing Zhang X, Li M, Lin H, et al. regSNPs-splicing: a tool for prioritizing synonymous single-nucleotide substitution. Hum Genet. 2017;136(9):1279–1289. doi:10.1007/s00439-017-1783-x http://regsnps-splicing.ccbb.iupui.edu/
dbscSNV Xueqiu Jian, Eric Boerwinkle, Xiaoming Liu, In silico prediction of splice-altering single nucleotide variants in the human genome, Nucleic Acids Research, Volume 42, Issue 22, 16 December 2014, Pages 13534–13544, https://doi.org/10.1093/nar/gku1206 http://www.liulab.science/dbscsnv.html
InterVar Quan Li and Kai Wang. InterVar: Clinical interpretation of genetic variants by ACMG-AMP 2015 guideline(The American Journal of Human Genetics 100, 1-14, February 2, 2017,http://dx.doi.org/10.1016/j.ajhg.2017.01.004) http://wintervar.wglab.org/
Clinvar NCBI Handbook Melissa Landrum, PhD, Jennifer Lee, PhD, George Riley, PhD, Wonhee Jang, PhD, Wendy Rubinstein, MD, PhD, Deanna Church, PhD, and Donna Maglott, PhD. ClinVar. [Bookshelf ID: NBK174587] https://www.ncbi.nlm.nih.gov/clinvar/intro/
Cosmic Tate JG, Bamford S, Jubb HC, et al. COSMIC: the Catalogue Of Somatic Mutations In Cancer. Nucleic Acids Research. 2019 Jan;47(D1):D941-D947. DOI: 10.1093/nar/gky1015. https://cancer.sanger.ac.uk/cosmic
NCI-60 Abaan OD, Polley EC, Davis SR, et al. The exomes of the NCI-60 panel: a genomic resource for cancer biology and systems pharmacology. Cancer Res. 2013;73(14):4372–4382. doi:10.1158/0008-5472.CAN-12-3342 https://dtp.cancer.gov/discovery_development/nci-60/
1000 Genomes Auton, A., Abecasis, G., Altshuler, D. et al. A global reference for human genetic variation. Nature 526, 68–74 (2015) doi:10.1038/nature15393 https://www.internationalgenome.org/about
dbSNP Kitts A, Phan L, Ward M, et al. The Database of Short Genetic Variation (dbSNP) 2013 Jun 30 [Updated 2014 Apr 3]. In: The NCBI Handbook [Internet]. 2nd edition. Bethesda (MD): National Center for Biotechnology Information (US); 2013-. https://www.ncbi.nlm.nih.gov/snp/
ExAC Karczewski KJ, Weisburd B, Thomas B, et al. The ExAC browser: displaying reference data information from over 60 000 exomes. Nucleic Acids Res. 2017;45(D1):D840–D845. doi:10.1093/nar/gkw971 http://exac.broadinstitute.org/
esp6500 https://evs.gs.washington.edu/EVS/
gnomad https://gnomad.broadinstitute.org/
dbNSFP Liu X, Jian X, and Boerwinkle E. 2011. dbNSFP: a lightweight database of human non-synonymous SNPs and their functional predictions. Human Mutation. 32:894-899. https://sites.google.com/site/jpopgen/dbNSFP
REVEL Ioannidis NM, Rothstein JH, Pejaver V, et al. REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants. Am J Hum Genet. 2016;99(4):877–885. doi:10.1016/j.ajhg.2016.08.016 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5065685/
SIFT Ngak-Leng Sim, Prateek Kumar, Jing Hu, Steven Henikoff, Georg Schneider, Pauline C. Ng, SIFT web server: predicting effects of amino acid substitutions on proteins, Nucleic Acids Research, Volume 40, Issue W1, 1 July 2012, Pages W452–W457, https://doi.org/10.1093/nar/gks539 https://sift.bii.a-star.edu.sg/www/publications.html
PolyPhen-2 Adzhubei IA, Schmidt S, Peshkin L, et al. A method and server for predicting damaging missense mutations. Nat Methods. 2010;7(4):248–249. doi:10.1038/nmeth0410-248 http://genetics.bwh.harvard.edu/pph2/dokuwiki/about
MutationTaster Schwarz, J., Cooper, D., Schuelke, M. et al. MutationTaster2: mutation prediction for the deep-sequencing age. Nat Methods 11, 361–362 (2014) doi:10.1038/nmeth.2890 http://www.mutationtaster.org/
MutationAssessor Boris Reva, Yevgeniy Antipin, Chris Sander, Predicting the functional impact of protein mutations: application to cancer genomics, Nucleic Acids Research, Volume 39, Issue 17, 1 September 2011, Page e118, https://doi.org/10.1093/nar/gkr407 http://mutationassessor.org/r3/
VEST3 Douville C, Masica DL, Stenson PD, et al. Assessing the Pathogenicity of Insertion and Deletion Variants with the Variant Effect Scoring Tool (VEST-Indel). Hum Mutat. 2016;37(1):28–35. doi:10.1002/humu.22911 https://karchinlab.org/apps/appVest.html
LRT Chun S, Fay JC. Identification of deleterious mutations within three human genomes. Genome Res. 2009;19(9):1553–1561. doi:10.1101/gr.092619.109 http://www.genetics.wustl.edu/jflab/lrt_query.html
Meta-SVM Dong C, Wei P, Jian X, et al. Comparison and integration of deleteriousness prediction methods for nonsynonymous SNVs in whole exome sequencing studies. Hum Mol Genet. 2015;24(8):2125–2137. doi:10.1093/hmg/ddu733 https://sites.google.com/site/jpopgen/dbNSFP
MetaLR Dong C, Wei P, Jian X, et al. Comparison and integration of deleteriousness prediction methods for nonsynonymous SNVs in whole exome sequencing studies. Hum Mol Genet. 2015;24(8):2125–2137. doi:10.1093/hmg/ddu733 https://sites.google.com/site/jpopgen/dbNSFP
CADD Rentzsch P, Witten D, Cooper GM, Shendure J, Kircher M. CADD: predicting the deleteriousness of variants throughout the human genome. Nucleic Acids Res. 2019;47(D1):D886–D894. doi:10.1093/nar/gky1016 https://cadd.gs.washington.edu/
DANN Quang D, Chen Y, Xie X. DANN: a deep learning approach for annotating the pathogenicity of genetic variants. Bioinformatics. 2015;31(5):761–763. doi:10.1093/bioinformatics/btu703 https://cbcl.ics.uci.edu/public_data/DANN/
fathmm-MKL Shihab HA, Rogers MF, Gough J, et al. An integrative approach to predicting the functional effects of non-coding and coding sequence variation. Bioinformatics. 2015;31(10):1536–1543. doi:10.1093/bioinformatics/btv009 https://github.com/HAShihab/fathmm-MKL
GERP++ Cooper GM, Stone EA, Asimenos G, et al. Distribution and intensity of constraint in mammalian genomic sequence. Genome Res. 2005;15(7):901–913. doi:10.1101/gr.3577405 http://mendel.stanford.edu/SidowLab/downloads/gerp/
FATHMM Shihab HA, Gough J, Mort M, Cooper DN, Day IN, Gaunt TR. Ranking non-synonymous single nucleotide polymorphisms based on disease concepts. Hum Genomics. 2014;8(1):11. Published 2014 Jun 30. doi:10.1186/1479-7364-8-11 http://fathmm.biocompute.org.uk/
PROVEAN Choi Y, Sims GE, Murphy S, Miller JR, Chan AP (2012) Predicting the Functional Effect of Amino Acid Substitutions and Indels. PLOS ONE 7(10): e46688. https://doi.org/10.1371/journal.pone.0046688 http://provean.jcvi.org/index.php
PhastCons Siepel A, Bejerano G, Pedersen JS, et al. Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes. Genome Res. 2005;15(8):1034–1050. doi:10.1101/gr.3715005 http://compgen.cshl.edu/phast/phastCons-HOWTO.html
siphy Manuel Garber, Mitchell Guttman, Michele Clamp, Michael C. Zody, Nir Friedman, Xiaohui Xie, Identifying novel constrained elements by exploiting biased substitution patterns, Bioinformatics, Volume 25, Issue 12, 15 June 2009, Pages i54–i62, https://doi.org/10.1093/bioinformatics/btp190 http://portals.broadinstitute.org/genome_bio/siphy/index.html