ArticleBioinformatics (Oxford, England)2017
PSSV: a novel pattern-based probabilistic approach for somatic structural variation identification.
Article in Bioinformatics (Oxford, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Current status of structural variation studies in plants.Plant biotechnology journal · 2021Review
- Multicenter validation of cancer gene panel-based next-generation sequencing for translational research and molecular diagnostics.Virchows Archiv : an international journal of pathology · 2018Article
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6 authors.
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Abstract
motivationWhole 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.
resultsWe 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. AVAILABILITY AND IMPLEMENTATION: An R package of PSSV is available at http://www.cbil.ece.vt.edu/software.htm CONTACT: xuan@vt.eduSupplementary information: Supplementary data are available at Bioinformatics online.
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