title: SoloDel: a probabilistic model for detecting low-frequent somatic deletions from unmatched sequencing data.
abstract: BACKGROUND
  Finding somatic mutations from massively parallel sequencing data is becoming a standard process in genome-based biomedical studies. There are a number of robust methods developed for detecting somatic single nucleotide variations However, detection of somatic copy number alteration has been substantially less explored and remains vulnerable to frequently raised sampling issues: low frequency in cell population and absence of the matched control samples.
  
  
  RESULTS
  We developed a novel computational method SoloDel that accurately classifies low-frequent somatic deletions from germline ones with or without matched control samples. We first constructed a probabilistic, somatic mutation progression model that describes the occurrence and propagation of the event in the cellular lineage of the sample. We then built a Gaussian mixture model to represent the mixed population of somatic and germline deletions. Parameters of the mixture model could be estimated using the expectation-maximization algorithm with the observed distribution of read-depth ratios at the points of discordant-read based initial deletion calls. Combined with conventional structural variation caller, SoloDel greatly increased the accuracy in classifying somatic mutations. Even without control, SoloDel maintained a comparable performance in a wide range of mutated subpopulation size (10-70%). SoloDel could also successfully recall experimentally validated somatic deletions from previously reported neuropsychiatric whole-genome sequencing data.
  
  
  METHODS
  Java-based implementation of the method is available at http://sourceforge.net/projects/solodel/
  
  
  BACKGROUND
  swkim@yuhs.ac or dhlee@biosoft.kaist.ac.kr
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Severance Biomedical Science Institute, Yonsei University College of Medicine, Seoul 120-752, Korea, Department of Bio and Brain Engineering, KAIST, Yuseong-Gu, Daejeon 305-701, Korea.
authors.forename: Junho
authors.initials: J
authors.lastname: Kim
authors.affiliation: Stanley Brain Research Laboratory, Stanley Medical Research Institute, Rockville, MD 20850, USA and.
authors.forename: Sanghyeon
authors.initials: S
authors.lastname: Kim
authors.affiliation: School of Information and Communications, Gwangju Institute of Science and Technology, Gwangju 500-712, Korea.
authors.forename: Hojung
authors.initials: H
authors.lastname: Nam
authors.affiliation: Severance Biomedical Science Institute, Yonsei University College of Medicine, Seoul 120-752, Korea.
authors.forename: Sangwoo
authors.initials: S
authors.lastname: Kim
authors.affiliation: Department of Bio and Brain Engineering, KAIST, Yuseong-Gu, Daejeon 305-701, Korea.
authors.forename: Doheon
authors.initials: D
authors.lastname: Lee
mesh_terms: D003198:Computer Simulation; D030541:Databases, Genetic; D006801:Humans; D001523:Mental Disorders / Q000235:genetics; D015233:Models, Statistical; D015203:Reproducibility of Results; D017422:Sequence Analysis, DNA / Q000379:methods*; D017384:Sequence Deletion / Q000235:genetics*; D012984:Software