An integrative approach to predicting the functional effects of non-coding and coding sequence variation

Bioinformatics

Volumn 31, Issue 10, 2015, Pages 1536-1543

  Shihab, Hashem A ^a   Rogers, Mark F ^a   Gough, Julian ^a   Mort, Matthew ^b   Cooper, David N ^b   Day, Ian N M ^a   Gaunt, Tom R ^a   Campbell, Colin ^a  

^a UNIVERSITY OF BRISTOL   (United Kingdom)

^b CARDIFF UNIVERSITY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

UNTRANSLATED REGION;

ALGORITHM; GENETIC ASSOCIATION; GENETIC VARIABILITY; GENETICS; GENOMICS; HUMAN; HUMAN GENOME; MOLECULAR GENETICS; OPEN READING FRAME; PHENOTYPE; PROCEDURES; UNTRANSLATED REGION;

ALGORITHMS; GENETIC VARIATION; GENOME, HUMAN; GENOME-WIDE ASSOCIATION STUDY; GENOMICS; HUMANS; MOLECULAR SEQUENCE ANNOTATION; OPEN READING FRAMES; PHENOTYPE; UNTRANSLATED REGIONS;

EID: 84929628542     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btv009     Document Type: Article

References (19)

1. Campbell C. and Ying Y. (2011). Learning with Support Vector Machines. Morgan and Claypool, San Rafael, California, USA
2. Gönen M. and Alpaydin E. (2011). Multiple kernel learning algorithms. J. Mach. Learn. Res., 12, 2211-2268
3. Hindorff L.A., et al. (2009). Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc. Natl Acad. Sci., USA, 106, 9362-9367
4. Kircher M., et al. (2014). A general framework for estimating the relative pathogenicity of human genetic variants. Nat. Genet., 46, 310-315
5. Landrum M.J., et al. (2013). Clinvar: public archive of relationships among sequence variation and human phenotype. Nucleic Acids Res., gkt1113
6. Li H. (2011). Tabix: fast retrieval of sequence features from generic tab-delimited files. Bioinformatics, 27, 718-719
7. Platt J. (1999). Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In: Advances in Large Margin Classifiers. MIT Press, Cambridge, Massachusetts, USA, pp. 61-74
8. Qu H. and Fang X. (2013). A brief review on the human encyclopedia of DNA elements (encode) project. Genom. Proteom. Bioinform., 11, 135-141
9. Ritchie G.R.S., et al. (2014). Functional annotation of noncoding sequence variants. Nat. Methods, 11, 294-296
10. Shawe-Taylor J. and Cristianini N. (2004). Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge, United Kingdom
11. Shihab H.A., et al. (2013a). Predicting the functional consequences of cancerassociated amino acid substitutions. Bioinformatics, btt182
12. Shihab H.A., et al. (2013b). Predicting the functional, molecular, and phenotypic consequences of amino acid substitutions using hidden markov models. Hum. Mutat., 34, 57-65
13. Shihab H.A., et al. (2014). Ranking non-synonymous single nucleotide polymorphisms based on disease concepts. Hum. Genomics, 8, 11
14. 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
15. The 1000 Genomes Project. (2010). A map of human genome variation from population-scale sequencing. Nature, 467, 1061-1073
16. The 1000 Genomes Project Consortium. (2012). An integrated map of genetic variation from 1, 092 human genomes. Nature, 491, 56-65
17. The ENCODE Project Consortium. (2012). An integrated encyclopedia of DNA elements in the human genome. Nature, 489, 57-74
18. Thusberg J., et al. (2011). Performance of mutation pathogenicity prediction methods on missense variants. Hum. Mutat., 32, 358-368
19. Ying Y., et al. (2009). Enhanced protein fold recognition through a novel data integration approach. BMC Bioinformatics, 10, 267