ABRA: Improved coding indel detection via assembly-based realignment

Lisle E. Mose*, Matthew D. Wilkerson, D. Neil Hayes, Charles M. Perou, Joel S. Parker

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

121 Scopus citations

Abstract

Motivation: Variant detection from next-generation sequencing (NGS) data is an increasingly vital aspect of disease diagnosis, treatment and research. Commonly used NGS-variant analysis tools generally rely on accurately mapped short reads to identify somatic variants and germ-line genotypes. Existing NGS read mappers have difficulty accurately mapping short reads containing complex variation (i.e. more than a single base change), thus making identification of such variants difficult or impossible. Insertions and deletions (indels) in particular have been an area of great difficulty. Indels are frequent and can have substantial impact on function, which makes their detection all the more imperative. Results: We present ABRA, an assembly-based realigner, which uses an efficient and flexible localized de novo assembly followed by global realignment to more accurately remap reads. This results in enhanced performance for indel detection as well as improved accuracy in variant allele frequency estimation.

Original languageEnglish
Pages (from-to)2813-2815
Number of pages3
JournalBioinformatics
Volume30
Issue number19
DOIs
StatePublished - 2 Apr 2014
Externally publishedYes

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