ABRA: improved coding indel detection via assembly-based realignment.

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

doi:10.1093/bioinformatics/btu376

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.Availability and implementation: ABRA is implemented in a combination of Java and C/C++ and is freely available for download at https://github.com/mozack/abra.Contact: lmose@unc.edu; parkerjs@email.unc.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Highlights

A number of realignment or assembly methods have been proposed to overcome the alignment errors and reference bias that limit indel detection
ABRA enables an increase in the number of Mendelian consistent loci (MCL) detected and a decrease in Mendelian conflict rate (MCR) with either Freebayes or UnifiedGenotyper (Fig. 1)
The Freebayes/ABRA combination yields a decrease in MCR compared with HaplotypeCaller and remains competitive in number of MCL detected

Summary

INTRODUCTION

A number of realignment or assembly methods have been proposed to overcome the alignment errors and reference bias that limit indel detection. Short read micro aligner locally realigns reads to regionally assembled variant graphs (Homer and Nelson, 2010). Dindel realigns reads to candidate haplotypes and uses a Bayesian method to call indels up to 50 bp in length (Albers et al, 2011). Localized assembly and calling on regions containing reads where only one half of a paired read is mapped (Li et al, 2012). Clipping REveals STructure (CREST) uses soft clipped reads and localized assembly to identify somatic structural variants (Wang et al, 2010). Our newly developed tool called ABRA accepts a Sequence Alignment/Map (SAM/BAM) file as input and produces a realigned BAM file as output, allowing flexibility in selection of variant calling algorithms and other downstream analysis. ABRA can be used to enhance both germ-line and somatic variant detection and works with paired-end as well as single-end data

METHODS

HapMap trio

TCGA tumor and normal data

CONCLUSION