Dataset Information

Predicting functional regulatory polymorphisms.

ABSTRACT:

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 ( approximately 80%) and speci.city ( approximately 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.

Availability

The software platforms used in our analyses are freely available (http://www.cs.waikato.ac.nz/ml/weka/). In addition, we provide the training dataset (Supplementary Table 3), and our predictions (Supplementary Table 6), in the Supplementary Material.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Torkamani A

PROVIDER: S-EPMC2732211 | biostudies-literature | 2008 Aug

REPOSITORIES: biostudies-literature

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Publications

Predicting functional regulatory polymorphisms.

Torkamani Ali A Schork Nicholas J NJ

Bioinformatics (Oxford, England) 20080618 16

<h4>Motivation</h4>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 ...[more]

PMID: 18562267

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Dataset Information

Predicting functional regulatory polymorphisms.

Motivation

Results

Availability

Supplementary information

Publications

Predicting functional regulatory polymorphisms.

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