ScanIndel: A hybrid framework for indel detection via gapped alignment, split reads and de novo assembly

Research output: Contribution to journalArticlepeer-review

28 Scopus citations

Abstract

Comprehensive identification of insertions/deletions (indels) across the full size spectrum from second generation sequencing is challenging due to the relatively short read length inherent in the technology. Different indel calling methods exist but are limited in detection to specific sizes with varying accuracy and resolution. We present ScanIndel, an integrated framework for detecting indels with multiple heuristics including gapped alignment, split reads and de novo assembly. Using simulation data, we demonstrate ScanIndel's superior sensitivity and specificity relative to several state-of-the-art indel callers across various coverage levels and indel sizes. ScanIndel yields higher predictive accuracy with lower computational cost compared with existing tools for both targeted resequencing data from tumor specimens and high coverage whole-genome sequencing data from the human NIST standard NA12878. Thus, we anticipate ScanIndel will improve indel analysis in both clinical and research settings. ScanIndel is implemented in Python, and is freely available for academic use at https://github.com/cauyrd/ScanIndel.

Original languageEnglish (US)
Article number127
JournalGenome medicine
Volume7
Issue number1
DOIs
StatePublished - Dec 7 2015

Bibliographical note

Publisher Copyright:
© 2015 Yang et al.

Fingerprint Dive into the research topics of 'ScanIndel: A hybrid framework for indel detection via gapped alignment, split reads and de novo assembly'. Together they form a unique fingerprint.

Cite this