PSSV: A novel pattern-based probabilistic approach for somatic structural variation identification

Xi Chen, Xu Shi, Leena Hilakivi-Clarke, Ayesha N. Shajahan-Haq, Robert Clarke, Jianhua Xuan

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Motivation: Whole genome DNA-sequencing (WGS) of paired tumor and normal samples has enabled the identification of somatic DNA changes in an unprecedented detail. Large-scale identification of somatic structural variations (SVs) for a specific cancer type will deepen our understanding of driver mechanisms in cancer progression. However, the limited number of WGS samples, insufficient read coverage, and the impurity of tumor samples that contain normal and neoplastic cells, limit reliable and accurate detection of somatic SVs. Results: We present a novel pattern-based probabilistic approach, PSSV, to identify somatic structural variations from WGS data. PSSV features a mixture model with hidden states representing different mutation patterns; PSSV can thus differentiate heterozygous and homozygous SVs in each sample, enabling the identification of those somatic SVs with heterozygous mutations in normal samples and homozygous mutations in tumor samples. Simulation studies demonstrate that PSSV outperforms existing tools. PSSV has been successfully applied to breast cancer data to identify somatic SVs of key factors associated with breast cancer development.

Original languageEnglish (US)
Pages (from-to)177-183
Number of pages7
JournalBioinformatics
Volume33
Issue number2
DOIs
StatePublished - Jan 15 2017

Bibliographical note

Funding Information:
This work was supported in part by the National Institutes of Health (CA149653 to J.X., CA149147 & CA184902 to R.C. and CA164384 to L.H.-C.).

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