Improving SNP discovery by base alignment quality

I propose a new application of profile Hidden Markov Models in the area of SNP discovery from resequencing data, to greatly reduce false SNP calls caused by misalignments around insertions and deletions (indels). The central concept is per-Base Alignment Quality, which accurately measures the probab...

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Bibliographic Details
Published inBioinformatics Vol. 27; no. 8; pp. 1157 - 1158
Main Author Li, Heng
Format Journal Article
LanguageEnglish
Published Oxford Oxford University Press 15.04.2011
Subjects
Algorithms
Applications Note
Base Sequence
Biological and medical sciences
Fundamental and applied biological sciences. Psychology
General aspects
Genomics
INDEL Mutation
Markov Chains
Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects)
Polymorphism, Single Nucleotide
Sequence Alignment - methods
Sequence Alignment - standards
Quality
Single nucleotide polymorphism
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Summary:I propose a new application of profile Hidden Markov Models in the area of SNP discovery from resequencing data, to greatly reduce false SNP calls caused by misalignments around insertions and deletions (indels). The central concept is per-Base Alignment Quality, which accurately measures the probability of a read base being wrongly aligned. The effectiveness of BAQ has been positively confirmed on large datasets by the 1000 Genomes Project analysis subgroup. Availability:  http://samtools.sourceforge.net Contact:  hengli@broadinstitute.org
Bibliography:ObjectType-Article-1
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Associate Editor: John Quackenbush
ISSN:1367-4803
1367-4811
1367-4811
1460-2059
DOI:10.1093/bioinformatics/btr076