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2014 ; 15
(1
): 455
Nephropedia Template TP
gab.com Text
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English Wikipedia
EFIN: predicting the functional impact of nonsynonymous single nucleotide
polymorphisms in human genome
#MMPMID24916671
Zeng S
; Yang J
; Chung BH
; Lau YL
; Yang W
BMC Genomics
2014[Jun]; 15
(1
): 455
PMID24916671
show ga
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.