FaSD-somatic: A fast and accurate somatic SNV detection algorithm for cancer genome sequencing data

Weixin Wang, Panwen Wang, Feng Xu, Ruibang Luo, Maria Pik Wong, Tak Wah Lam, Junwen Wang

Research output: Contribution to journalArticle

10 Scopus citations

Abstract

Summary: Recent advances in high-throughput sequencing technologies have enabled us to sequence large number of cancer samples to reveal novel insights into oncogenetic mechanisms. However, the presence of intratumoral heterogeneity, normal cell contamination and insufficient sequencing depth, together pose a challenge for detecting somatic mutations. Here we propose a fast and an accurate somatic single-nucleotide variations (SNVs) detection program, FaSD-somatic. The performance of FaSD-somatic is extensively assessed on various types of cancer against several state-of-the-Art somatic SNV detection programs. Benchmarked by somatic SNVs from either existing databases or de novo higher-depth sequencing data, FaSD-somatic has the best overall performance. Furthermore, FaSD-somatic is efficient, it finishes somatic SNV calling within 14 h on 50X whole genome sequencing data in paired samples.

Original languageEnglish (US)
Pages (from-to)2498-2500
Number of pages3
JournalBioinformatics
Volume30
Issue number17
DOIs
StatePublished - Sep 1 2014

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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