Journal
BIOINFORMATICS
Volume 24, Issue 16, Pages 1787-1792Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btn311
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Funding
- NHLBI NIH HHS [U01 HL064777, U01 HL064777-06] Funding Source: Medline
- NIA NIH HHS [U19AG023122-01, U19 AG023122] Funding Source: Medline
- NIDA NIH HHS [U01 DA024417-01, U01 DA024417] Funding Source: Medline
- NIMH NIH HHS [P50 MH081755-01, R01 MH078151, N01 MH022005, P50 MH081755, 1 R01 MH078151-01A1] Funding Source: Medline
- PHS HHS [5 R01 HLMH065571-02] Funding Source: Medline
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Motivation: Limited availability of data has hindered the development of algorithms that can identify functionally meaningful regulatory single nucleotide polymorphisms (rSNPs). Given the large number of common polymorphisms known to reside in the human genome, the identification of functional rSNPs via laboratory assays will be costly and time-consuming. Therefore appropriate bioinformatics strategies for predicting functional rSNPs are necessary. Recent data from the Encyclopedia of DNA Elements (ENCODE) Project has significantly expanded the amount of available functional information relevant to non-coding regions of the genome, and, importantly, led to the conclusion that many functional elements in the human genome are not conserved. Results: In this article we describe how ENCODE data can be leveraged to probabilistically determine the functional and phenotypic significance of non-coding SNPs (ncSNPs). The method achieves excellent sensitivity (80) and speci.city (99) based on a set of known phenotypically relevant and non-functional SNPs. In addition, we show that our method is not overtrained through the use of cross-validation analyses.
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