Predicting regulatory variants with composite statistic

Bioinformatics

Volumn 32, Issue 18, 2016, Pages 2729-2736

  Li, Mulin Jun ^a,b   Pan, Zhicheng ^b   Liu, Zipeng ^b   Wu, Jiexing ^a   Wang, Panwen ^b   Zhu, Yun ^b   Xu, Feng ^b   Xia, Zhengyuan ^b   Sham, Pak Chung ^b   Kocher, Jean Pierre A ^d   Li, Miaoxin ^b   Liu, Jun S ^a,c   Wang, Junwen ^b,d,e  

^a HARVARD UNIVERSITY   (United States)

^b UNIVERSITY OF HONG KONG   (Hong Kong)

^c TSINGHUA UNIVERSITY   (China)

^d MAYO CLINIC   (United States)

^e ARIZONA STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

UNTRANSLATED RNA;

ALGORITHM; EVOLUTION; GENETIC VARIATION; HUMAN; MOLECULAR GENETICS; SOFTWARE; THEORETICAL MODEL;

ALGORITHMS; BIOLOGICAL EVOLUTION; GENETIC VARIATION; HUMANS; MODELS, THEORETICAL; MOLECULAR SEQUENCE ANNOTATION; RNA, UNTRANSLATED; SOFTWARE;

EID: 84992187658     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btw288     Document Type: Article

References (41)

1. Brown, C.D. et al. (2013) Integrative modeling of eQTLs and cis-regulatory elements suggests mechanisms underlying cell type specificity of eQTLs. PLoS Genet., 9, e1003649.
2. Cassa, C.A. et al. (2013) Large numbers of genetic variants considered to be pathogenic are common in asymptomatic individuals. Human Mut., 34, 1216-1220.
3. Clark, P.M. et al. (2015) The dichotomy between disease phenotype databases and the implications for understanding complex diseases involving the major histocompatibility complex. Int. J. Immunogenet., 42, 413-422.
4. Cunningham, F. et al. (2015) Ensembl 2015. Nucleic Acids Res., 43, D662-D669.
5. Danecek, P. et al. (2011) The variant call format and VCFtools. Bioinformatics, 27, 2156-2158.
6. Davydov, E.V. et al. (2010) Identifying a high fraction of the human genome to be under selective constraint using GERP++. PLoS Comput. Biol., 6, e1001025.
7. Degner, J.F. et al. (2012) DNase I sensitivity QTLs are a major determinant of human expression variation. Nature, 482, 390-394.
8. Dong, C. et al. (2015) Comparison and integration of deleteriousness prediction methods for nonsynonymous SNVs in whole exome sequencing studies. Hum. Mol. Genet., 24, 2125-2137.
9. Farh, K.K. et al. (2015) Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature, 518, 337-343.
10. Forbes, S.A. et al. (2015) COSMIC: exploring the world's knowledge of somatic mutations in human cancer. Nucleic Acids Res., 43, D805-D811.
11. Fu, Y. et al. (2014) Fun Seq2: a framework for prioritizing noncoding regulatory variants in cancer. Genome Biol., 15, 480.
12. Genomes Project, C. et al. (2012) An integrated map of genetic variation from 1,092 human genomes. Nature, 491, 56-65.
13. Gonzalez-Perez, A. and Lopez-Bigas, N. (2011) Improving the assessment of the outcome of nonsynonymous SNVs with a consensus deleteriousness score, Condel. Am. J. Hum. Genet., 88, 440-449.
14. Griffith, O.L. et al. (2008) OReg Anno: an open-access community-driven resource for regulatory annotation. Nucleic Acids Res., 36, D107-D113.
15. Grossman, S.R. et al. (2010) A composite of multiple signals distinguishes causal variants in regions of positive selection. Science, 327, 883-886.
16. Ionita-Laza, I. et al. (2016) A spectral approach integrating functional genomic annotations for coding and noncoding variants. Nat. Genet., 48, 214-220.
17. Kellis, M. et al. (2014) Defining functional DNA elements in the human genome. Proc. Natl. Acad. Sci. USA, 111, 6131-6138.
18. Khurana, E. et al. (2013) Integrative annotation of variants from 1092 humans: application to cancer genomics. Science, 342, 1235587.
19. Kircher, M. and Shendure, J. (2015) Running spell-check to identify regulatory variants. Nat. Genet., 47, 853-855.
20. Kircher, M. et al. (2014) A general framework for estimating the relative pathogenicity of human genetic variants. Nat. Genet., 46, 310-315.
21. Landrum, M.J. et al. (2014) Clin Var: public archive of relationships among sequence variation and human phenotype. Nucleic Acids Res., 42, D980-D985.
22. Lee, D. et al. (2015) A method to predict the impact of regulatory variants from DNA sequence. Nat. Genet., 47, 955-961.
23. Li, H. (2011) Tabix: fast retrieval of sequence features from generic TABdelimited files. Bioinformatics, 27, 718-719.
24. Li, M.J. et al. (2015) wKGGSeq: A comprehensive strategy-based and diseasetargeted online framework to facilitate exome sequencing studies of inherited disorders. Hum. Mut., 36, 496-503.
25. Li, M.J. et al. (2016) GWASdb v2: an update database for human genetic variants identified by genome-wide association studies. Nucleic Acids Res., 44, D869-D876.
26. Li, M.J. and Wang, J. (2015) Current trend of annotating single nucleotide variation in humans - a case study on SNVrap. Methods, 79-80, 32-40.
27. Li, M.J. et al. (2013a) GWAS3D: Detecting human regulatory variants by integrative analysis of genome-wide associations, chromosome interactions and histone modifications. Nucleic Acids Res., 41, W150-W158.
28. Li, M.X. et al. (2013b) Predicting mendelian disease-causing non-synonymous single nucleotide variants in exome sequencing studies. PLoS Genet., 9, e1003143.
29. Lopes, M.C. et al. (2012) A combined functional annotation score for nonsynonymous variants. Hum. Hered., 73, 47-51.
30. Maurano, M.T. et al. (2015) Large-scale identification of sequence variants influencing human transcription factor occupancy in vivo. Nat. Genet., 47, 1393-1401.
31. Melton, C. et al. (2015) Recurrent somatic mutations in regulatory regions of human cancer genomes. Nat. Genet., 47, 710-716.
32. Patwardhan, R.P. et al. (2012) Massively parallel functional dissection of mammalian enhancers in vivo. Nat. Biotechnol., 30, 265-270.
33. Quang, D. et al. (2015) DANN: a deep learning approach for annotating the pathogenicity of genetic variants. Bioinformatics, 31, 761-763.
34. Ritchie, G.R. et al. (2014) Functional annotation of noncoding sequence variants. Nat. Methods, 11, 294-296.
35. Ryan, N.M. et al. (2014) SuRFing the genomics wave: an R package for prioritising SNPs by functionality. Genome Med., 6, 79.
36. Shihab, H.A. et al. (2015) An integrative approach to predicting the functional effects of non-coding and coding sequence variation. Bioinformatics, 31, 1536-1543.
37. Siepel, A. et al. (2005) Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes. Genome Res., 15, 1034-1050.
38. Stenson, P.D. et al. (2014) The Human Gene Mutation Database: building a comprehensive mutation repository for clinical and molecular genetics, diagnostic testing and personalized genomic medicine. Hum. Genet., 133, 1-9.
39. Stergachis, A.B. et al. (2013) Exonic transcription factor binding directs codon choice and affects protein evolution. Science, 342, 1367-1372.
40. Vockley, C.M. et al. (2015) Massively parallel quantification of the regulatory effects of noncoding genetic variation in a human cohort. Genome Res., 25, 1206-1214.
41. Wang, K. et al. (2010) ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res., 38, e164.