A support vector machine for identification of single-nucleotide polymorphisms from next-generation sequencing data
Author(s) -
Brendan O’Fallon,
Whitney WooderchakDonahue,
David K. Crockett
Publication year - 2013
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btt172
Subject(s) - computer science , executable , support vector machine , source code , identification (biology) , data mining , single nucleotide polymorphism , artificial intelligence , biology , genetics , programming language , genotype , botany , gene
Accurate determination of single-nucleotide polymorphisms (SNPs) from next-generation sequencing data is a significant challenge facing bioinformatics researchers. Most current methods use mechanistic models that assume nucleotides aligning to a given reference position are sampled from a binomial distribution. While such methods are sensitive, they are often unable to discriminate errors resulting from misaligned reads, sequencing errors or platform artifacts from true variants.
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