title: PSSV: a novel pattern-based probabilistic approach for somatic structural variation identification.
abstract: 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.
  
  
  
  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.
  
  
  
  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.
authors.affiliation: Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA.
authors.forename: Xi
authors.initials: X
authors.lastname: Chen
authors.affiliation: Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA.
authors.forename: Xu
authors.initials: X
authors.lastname: Shi
authors.affiliation: Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC 20057, USA.
authors.forename: Leena
authors.initials: L
authors.lastname: Hilakivi-Clarke
authors.affiliation: Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC 20057, USA.
authors.forename: Ayesha N
authors.initials: AN
authors.lastname: Shajahan-Haq
authors.affiliation: Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC 20057, USA.
authors.forename: Robert
authors.initials: R
authors.lastname: Clarke
authors.affiliation: Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA.
authors.forename: Jianhua
authors.initials: J
authors.lastname: Xuan
mesh_terms: D001943:Breast Neoplasms / Q000235:genetics*; D004252:DNA Mutational Analysis / Q000379:methods*; D004273:DNA, Neoplasm; D005260:Female; D015972:Gene Expression Regulation, Neoplastic; D056914:Genomic Structural Variation; D006801:Humans; D009154:Mutation; D012333:RNA, Messenger; D012984:Software
chemical_list: D004273:DNA, Neoplasm; D012333:RNA, Messenger