EFIN: predicting the functional impact of nonsynonymous single nucleotide polymorphisms in human genome

• Published in: BMC genomics Vol. 15; no. 1; p. 455
• Main Authors: Zeng, Shuai, Yang, Jing, Chung, Brian Hon-Yin, Lau, Yu Lung, Yang, Wanling
• Format: Journal Article
• Language: English
• Published: London BioMed Central 10.06.2014; BioMed Central Ltd; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Amino Acid Sequence; Amino Acid Substitution; Amino acids; Analysis; Animal Genetics and Genomics; Bacteria; Bioinformatics; Biomedical and Life Sciences; Comparative and evolutionary genomics; Computational Biology - methods; Conservation; Conserved Sequence; Evolution, Molecular; Fungi; Genetic aspects; Genetic variance; Genetic Variation; Genome, Human; Genomes; Humans; Internet; Life Sciences; Methodology; Methodology Article; Methods; Microarrays; Microbial Genetics and Genomics; Monkeys & apes; Mutation; Mutation Rate; Plant Genetics and Genomics; Polymorphism, Single Nucleotide; Proteins; Proteomics; Single nucleotide polymorphisms; Taxonomy; Functional impact; nsSNP; Evolutionary distance; Coding mutation; Amino acid conservation
• ISSN: 1471-2164; 1471-2164
• DOI: 10.1186/1471-2164-15-455

Background 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. Results Here 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. Conclusions Grouping 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.