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Paper   IPM / Biological Sciences / 14591
School of Biological Sciences
  Title:   PSE-HMM: genome-wide CNV detection from NGS data using an HMM with Position-Specific Emission probabilities
1.  Seyed Amir Malekpour
2.  Hamid Pezeshk
3.  Mehdi Sadeghi
  Status:   Published
  Journal: BMC Bioinformatics
  No.:  1
  Vol.:  https://doi.org/10.1186/s12859-016-1296-y
  Year:  2016
  Pages:   1-30
  Supported by:  IPM
Background Copy Number Variation (CNV) is envisaged to be a major source of large structural variations in the human genome. In recent years, many studies apply Next Generation Sequencing (NGS) data for the CNV detection. However, still there is a necessity to invent more accurate computational tools.
Results In this study, mate pair NGS data are used for the CNV detection in a Hidden Markov Model (HMM). The proposed HMM has position specific emission probabilities, i.e. a Gaussian mixture distribution. Each component in the Gaussian mixture distribution captures a different type of aberration that is observed in the mate pairs, after being mapped to the reference genome. These aberrations may include any increase (decrease) in the insertion size or change in the direction of mate pairs that are mapped to the reference genome. This HMM with Position-Specific Emission probabilities (PSE-HMM) is utilized for the genome-wide detection of deletions and tandem duplications. The performance of PSE-HMM is evaluated on a simulated dataset and also on a real data of a Yoruban HapMap individual, NA18507.
Conclusions PSE-HMM is effective in taking observation dependencies into account and reaches a high accuracy in detecting genome-wide CNVs. MATLAB programs are available at http://bs.ipm.ir/softwares/PSE-HMM/.

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