PSSV: a novel pattern-based probabilistic approach for somatic structural variation identification | Zendy

Xi Chen | Zendy; Xu Shi | Zendy; Leena HilakiviClarke | Zendy; Ayesha N. ShajahanHaq | Zendy; Robert Clarke | Zendy; Jianhua Xuan | Zendy

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

Author(s) -

Xi Chen,

Xu Shi,

Leena HilakiviClarke,

Ayesha N. ShajahanHaq,

Robert Clarke,

Jianhua Xuan

Publication year - 2016

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btw605

Subject(s) - somatic cell , computational biology , germline mutation , identification (biology) , biology , breast cancer , genetics , mutation , cancer , probabilistic logic , dna sequencing , computer science , dna , gene , artificial intelligence , botany

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.

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