Copy number variation detection using next generation sequencing read counts

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• 作者：Heng Wang (10) (8) (9)
  Dan Nettleton (11)
  Kai Ying (12)
  
  10. Department of Animal Science ; Michigan State University ; East Lansing ; USA
  8. Lyman Briggs College ; Michigan State University ; East Lansing ; USA
  9. Department of Statistics and Probability ; Michigan State University ; East Lansing ; USA
  11. Department of Statistics ; Iowa State University ; Ames ; USA
  12. Genome Technology Branch ; The National Human Genome Research Institute ; NIH ; Bethesda ; USA
  
• 关键词：Count data ; Gamma ; Poisson mixture ; Hidden Markov model ; Plant genomics ; Poisson mixture model
• 刊名：BMC Bioinformatics
• 出版年：2014
• 出版时间：December 2014
• 年：2014
• 卷：15
• 期：1
• 全文大小：823 KB
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• 刊物主题：Bioinformatics; Microarrays; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Combinatorial Libraries; Algorithms;
• 出版者：BioMed Central
• ISSN：1471-2105

文摘

Background A copy number variation (CNV) is a difference between genotypes in the number of copies of a genomic region. Next generation sequencing (NGS) technologies provide sensitive and accurate tools for detecting genomic variations that include CNVs. However, statistical approaches for CNV identification using NGS are limited. We propose a new methodology for detecting CNVs using NGS data. This method (henceforth denoted by m-HMM) is based on a hidden Markov model with emission probabilities that are governed by mixture distributions. We use the Expectation-Maximization (EM) algorithm to estimate the parameters in the model. Results A simulation study demonstrates that our proposed m-HMM approach has greater power for detecting copy number gains and losses relative to existing methods. Furthermore, application of our m-HMM to DNA sequencing data from the two maize inbred lines B73 and Mo17 to identify CNVs that may play a role in creating phenotypic differences between these inbred lines provides results concordant with previous array-based efforts to identify CNVs. Conclusions The new m-HMM method is a powerful and practical approach for identifying CNVs from NGS data.