title: Identification of indels in next-generation sequencing data.
abstract: BACKGROUND
  The discovery and mapping of genomic variants is an essential step in most analysis done using sequencing reads. There are a number of mature software packages and associated pipelines that can identify single nucleotide polymorphisms (SNPs) with a high degree of concordance. However, the same cannot be said for tools that are used to identify the other types of variants. Indels represent the second most frequent class of variants in the human genome, after single nucleotide polymorphisms. The reliable detection of indels is still a challenging problem, especially for variants that are longer than a few bases.
  
  
  RESULTS
  We have developed a set of algorithms and heuristics collectively called indelMINER to identify indels from whole genome resequencing datasets using paired-end reads. indelMINER uses a split-read approach to identify the precise breakpoints for indels of size less than a user specified threshold, and supplements that with a paired-end approach to identify larger variants that are frequently missed with the split-read approach. We use simulated and real datasets to show that an implementation of the algorithm performs favorably when compared to several existing tools.
  
  
  CONCLUSIONS
  indelMINER can be used effectively to identify indels in whole-genome resequencing projects. The output is provided in the VCF format along with additional information about the variant, including information about its presence or absence in another sample. The source code and documentation for indelMINER can be freely downloaded from www.bx.psu.edu/miller_lab/indelMINER.tar.gz .
authors: Center for Comparative Genomics and Bioinformatics, Pennsylvania State University, 506, Wartik Laboratory, University Park, PA, 16802, USA. ratan@bx.psu.edu.|Department of Public Health Sciences and Center for Public Health Genomics, University of Virginia, Charlottesville, VA, 22908, USA. ratan@bx.psu.edu.
authors: Aakrosh
authors: A
authors: Ratan
authors: Department of Medicine, Hematology and Oncology, and the University of Virginia Cancer Center, University of Virginia, Charlottesville, VA, 22908, USA. tlo6s@eservices.virginia.edu.
authors: Thomas L
authors: TL
authors: Olson
authors: Department of Medicine, Hematology and Oncology, and the University of Virginia Cancer Center, University of Virginia, Charlottesville, VA, 22908, USA. tl7cs@hscmail.mcc.virginia.edu.
authors: Thomas P
authors: TP
authors: Loughran
authors: Center for Comparative Genomics and Bioinformatics, Pennsylvania State University, 506, Wartik Laboratory, University Park, PA, 16802, USA. webb@bx.psu.edu.
authors: Webb
authors: W
authors: Miller
mesh_terms: D000465:Algorithms; D014408:Biomarkers, Tumor / Q000235:genetics*; D016022:Case-Control Studies; D015894:Genome, Human; D023281:Genomics / Q000379:methods; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D054643:INDEL Mutation / Q000235:genetics*; D009369:Neoplasms / Q000235:genetics*; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D017422:Sequence Analysis, DNA / Q000379:methods*
chemical_list: D014408:Biomarkers, Tumor