The distribution and mutagenesis of short coding INDELs from 1,128 whole exomes

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• 作者：Danny Challis (1) (2) (3)
  Lilian Antunes (1) (2) (4)
  Erik Garrison (5)
  Eric Banks (6)
  Uday S Evani (1) (2) (7)
  Donna Muzny (1) (2)
  Ryan Poplin (6)
  Richard A Gibbs (1) (2)
  Gabor Marth (5) (8)
  Fuli Yu (1) (2) (9)
  
  1. Human Genome Sequencing Center ; Baylor College of Medicine ; Houston ; TX ; 77030 ; USA
  2. Department of Molecular and Human Genetics ; Baylor College of Medicine ; Houston ; TX ; 77030 ; USA
  3. Present address ; Monsanto Company ; Ankeny ; IA ; 50021 ; USA
  4. Present address ; Washington University School of Medicine ; Saint Louis ; MO ; 63110 ; USA
  5. Department of Biology ; Boston College ; Wellcome Trust Sanger Institute ; Chestnut Hill ; MA ; 02467 ; USA
  6. Program in Medical and Population Genetics ; Broad Institute of Harvard and MIT ; Cambridge ; MA ; 02142 ; USA
  7. Present address ; New York Genome Center ; New York ; NY ; 10013 ; USA
  8. Present address ; Department of Human Genetics and Utah Center for Genetic Discovery ; University of Utah School of Medicine ; Salt Lake City ; UT ; 84112 ; USA
  9. Institute of Neurology ; Tianjin Medical University General Hospital ; Tianjin ; 300052 ; China
  
• 关键词：INDEL ; 1000 Genomes Project ; Distribution ; Mutagenesis
• 刊名：BMC Genomics
• 出版年：2015
• 出版时间：December 2015
• 年：2015
• 卷：16
• 期：1
• 全文大小：1,175 KB
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• 刊物主题：Life Sciences, general; Microarrays; Proteomics; Animal Genetics and Genomics; Microbial Genetics and Genomics; Plant Genetics & Genomics;
• 出版者：BioMed Central
• ISSN：1471-2164

文摘

Background Identifying insertion/deletion polymorphisms (INDELs) with high confidence has been intrinsically challenging in short-read sequencing data. Here we report our approach for improving INDEL calling accuracy by using a machine learning algorithm to combine call sets generated with three independent methods, and by leveraging the strengths of each individual pipeline. Utilizing this approach, we generated a consensus exome INDEL call set from a large dataset generated by the 1000 Genomes Project (1000G), maximizing both the sensitivity and the specificity of the calls. Results This consensus exome INDEL call set features 7,210 INDELs, from 1,128 individuals across 13 populations included in the 1000 Genomes Phase 1 dataset, with a false discovery rate (FDR) of about 7.0%. Conclusions In our study we further characterize the patterns and distributions of these exonic INDELs with respect to density, allele length, and site frequency spectrum, as well as the potential mutagenic mechanisms of coding INDELs in humans.