Reference-free SNP calling: improved accuracy by preventing incorrect calls from repetitive genomic regions

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• 作者：Jinzhuang Dou (1) (2)
  Xiqiang Zhao (2)
  Xiaoteng Fu (1)
  Wenqian Jiao (1)
  Nannan Wang (2)
  Lingling Zhang (1)
  Xiaoli Hu (1)
  Shi Wang (1)
  Zhenmin Bao (1)
  
• 关键词：Next ; generation sequencing ; single nucleotide polymorphism ; genotyping ; maximum likelihood ; mixed Poisson/normal model
• 刊名：Biology Direct
• 出版年：2012
• 出版时间：December 2012
• 年：2012
• 卷：7
• 期：1
• 全文大小：536KB
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• 作者单位：Jinzhuang Dou (1) (2)
  Xiqiang Zhao (2)
  Xiaoteng Fu (1)
  Wenqian Jiao (1)
  Nannan Wang (2)
  Lingling Zhang (1)
  Xiaoli Hu (1)
  Shi Wang (1)
  Zhenmin Bao (1)
  
  1. Key Laboratory of Marine Genetics and Breeding, College of Marine Life Sciences, Ocean University of China, 5 Yushan Road, Qingdao, 266003, China
  2. College of Mathematical sciences, Ocean University of China, 238 Songling Road, Qingdao, 266003, China
  

文摘

Background Single nucleotide polymorphisms (SNPs) are the most abundant type of genetic variation in eukaryotic genomes and have recently become the marker of choice in a wide variety of ecological and evolutionary studies. The advent of next-generation sequencing (NGS) technologies has made it possible to efficiently genotype a large number of SNPs in the non-model organisms with no or limited genomic resources. Most NGS-based genotyping methods require a reference genome to perform accurate SNP calling. Little effort, however, has yet been devoted to developing or improving algorithms for accurate SNP calling in the absence of a reference genome. Results Here we describe an improved maximum likelihood (ML) algorithm called iML, which can achieve high genotyping accuracy for SNP calling in the non-model organisms without a reference genome. The iML algorithm incorporates the mixed Poisson/normal model to detect composite read clusters and can efficiently prevent incorrect SNP calls resulting from repetitive genomic regions. Through analysis of simulation and real sequencing datasets, we demonstrate that in comparison with ML or a threshold approach, iML can remarkably improve the accuracy of de novo SNP genotyping and is especially powerful for the reference-free genotyping in diploid genomes with high repeat contents. Conclusions The iML algorithm can efficiently prevent incorrect SNP calls resulting from repetitive genomic regions, and thus outperforms the original ML algorithm by achieving much higher genotyping accuracy. Our algorithm is therefore very useful for accurate de novo SNP genotyping in the non-model organisms without a reference genome. Reviewers This article was reviewed by Dr. Richard Durbin, Dr. Liliana Florea (nominated by Dr. Steven Salzberg) and Dr. Arcady Mushegian.