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Bioinformatics. 2017 Jul 15;33(14):i161-i169. doi: 10.1093/bioinformatics/btx254.

Discovery and genotyping of novel sequence insertions in many sequenced individuals.

Author information

1
Department of Computer Engineering, Bogaziçi University, Istanbul, Turkey.
2
School of Computing Science, Simon Fraser University, Burnaby, Canada.
3
Department of Computer Engineering, Bilkent University, Ankara, Turkey.
4
Vancouver Prostate Centre, Vancouver, Canada.
5
Department of Urologic Sciences, University of British Columbia, Vancouver, Canada.

Abstract

Motivation:

Despite recent advances in algorithms design to characterize structural variation using high-throughput short read sequencing (HTS) data, characterization of novel sequence insertions longer than the average read length remains a challenging task. This is mainly due to both computational difficulties and the complexities imposed by genomic repeats in generating reliable assemblies to accurately detect both the sequence content and the exact location of such insertions. Additionally, de novo genome assembly algorithms typically require a very high depth of coverage, which may be a limiting factor for most genome studies. Therefore, characterization of novel sequence insertions is not a routine part of most sequencing projects.

Result:

Here, we present Pamir, a new algorithm to efficiently and accurately discover and genotype novel sequence insertions using either single or multiple genome sequencing datasets. Pamir is able to detect breakpoint locations of the insertions and calculate their zygosity (i.e. heterozygous versus homozygous) by analyzing multiple sequence signatures, matching one-end-anchored sequences to small-scale de novo assemblies of unmapped reads, and conducting strand-aware local assembly. We test the efficacy of Pamir on both simulated and real data, and demonstrate its potential use in accurate and routine identification of novel sequence insertions in genome projects.

Availability and implementation:

Pamir is available at https://github.com/vpc-ccg/pamir .

Contact:

fhach@{sfu.ca, prostatecentre.com } or calkan@cs.bilkent.edu.tr.

Supplementary information:

Supplementary data are available at Bioinformatics online.

PMID:
28881988
PMCID:
PMC5870608
DOI:
10.1093/bioinformatics/btx254
[Indexed for MEDLINE]
Free PMC Article

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