Identification of indels in next-generation sequencing data

BMC Bioinformatics. 2015 Feb 13;16(1):42. doi: 10.1186/s12859-015-0483-6.


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 .

Publication types

  • Comparative Study
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Biomarkers, Tumor / genetics*
  • Case-Control Studies
  • Genome, Human*
  • Genomics / methods
  • High-Throughput Nucleotide Sequencing / methods*
  • Humans
  • INDEL Mutation / genetics*
  • Neoplasms / genetics*
  • Polymorphism, Single Nucleotide / genetics
  • Sequence Analysis, DNA / methods*


  • Biomarkers, Tumor