Abstract
BackgroundPredicting the functional impact of amino acid substitutions (AAS) caused by nonsynonymous single nucleotide polymorphisms (nsSNPs) is becoming increasingly important as more and more novel variants are being discovered. Bioinformatics analysis is essential to predict potentially causal or contributing AAS to human diseases for further analysis, as for each genome, thousands of rare or private AAS exist and only a very small number of which are related to an underlying disease. Existing algorithms in this field still have high false prediction rate and novel development is needed to take full advantage of vast amount of genomic data.ResultsHere we report a novel algorithm that features two innovative changes: 1. making better use of sequence conservation information by grouping the homologous protein sequences into six blocks according to evolutionary distances to human and evaluating sequence conservation in each block independently, and 2. including as many such homologous sequences as possible in analyses. Random forests are used to evaluate sequence conservation in each block and to predict potential impact of an AAS on protein function. Testing of this algorithm on a comprehensive dataset showed significant improvement on prediction accuracy upon currently widely-used programs. The algorithm and a web-based application tool implementing it, EFIN (Evaluation of Functional Impact of Nonsynonymous SNPs) were made freely available (http://paed.hku.hk/efin/) to the public.ConclusionsGrouping homologous sequences into different blocks according to the evolutionary distance of the species to human and evaluating sequence conservation in each group independently significantly improved prediction accuracy. This approach may help us better understand the roles of genetic variants in human disease and health.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-455) contains supplementary material, which is available to authorized users.
Highlights
Predicting the functional impact of amino acid substitutions (AAS) caused by nonsynonymous single nucleotide polymorphisms is becoming increasingly important as more and more novel variants are being discovered
We developed a novel algorithm and a web-based application executing it, EFIN (Evaluation of Functional Impact of Nonsynonymous SNPs), to try to make more thorough and more accurate use of conservation information in predicting functional impact of an AAS
To validate the use of the block-wise structure introduced in this study in evaluation of conservation, we compared normalized alignment score (NAS) in two situations between two adjacent sequences in a multiple sequence alignment (MSA): when two adjacent sequences belong to the same block or when they belong to two different blocks
Summary
Predicting the functional impact of amino acid substitutions (AAS) caused by nonsynonymous single nucleotide polymorphisms (nsSNPs) is becoming increasingly important as more and more novel variants are being discovered. Bioinformatics analysis is essential to predict potentially causal or contributing AAS to human diseases for further analysis, as for each genome, thousands of rare or private AAS exist and only a very small number of which are related to an underlying disease. Existing algorithms in this field still have high false prediction rate and novel development is needed to take full advantage of vast amount of genomic data. A large number of somatic mutations may exist in a given tumour. Analysing ever increasing sequence data in silico first and identifying a small number
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