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Updated: Apr 14, 2026

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Published on: March 31, 2022
Identification of indels in next-generation sequencing data.
Aakrosh Ratan1,2, Thomas L Olson3, Thomas P Loughran4
1Center for Comparative Genomics and Bioinformatics, Pennsylvania State University, 506, Wartik Laboratory, University Park, PA, 16802, USA. ratan@bx.psu.edu.
Identifying insertion-deletion variants (indels) is challenging. We developed indelMINER, a new tool that effectively detects indels from whole-genome sequencing data, outperforming existing methods.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Genomic variant discovery is crucial for sequencing-based analyses.
- Single nucleotide polymorphisms (SNPs) are well-characterized, but indel detection remains a challenge.
- Indels are the second most common variant type, particularly longer ones.
Purpose of the Study:
- To develop and evaluate a novel tool for accurate indel detection.
- To address limitations in current indel identification software.
Main Methods:
- Developed indelMINER, a set of algorithms and heuristics for indel identification.
- Employed a split-read approach for precise breakpoint identification of smaller indels.
- Utilized a paired-end read approach to detect larger indels often missed by split-read methods.
Main Results:
- indelMINER demonstrates favorable performance compared to existing tools on simulated and real datasets.
- The tool effectively identifies indels from whole-genome resequencing data.
- Achieved high concordance in indel detection.
Conclusions:
- indelMINER is an effective tool for indel identification in whole-genome resequencing.
- Outputs variant data in VCF format with additional sample comparison information.
- Source code and documentation are publicly available for download.
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