Comparison of insertion/deletion calling algorithms on human next-generation sequencing data
Related Experiment Video
Updated: Apr 20, 2026

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing
Published on: August 24, 2017
Dalia H Ghoneim1, Jason R Myers, Emily Tuttle
1Center for Neural Development and Disease, University of Rochester Medical Center, 601 Elmwood Avenue, Rochester, NY, USA. Dalia_Ghoneim@urmc.rochester.edu.
Background:
Insertions/deletions (indels) are the second most common type of genomic variant and the most common type of structural variant. Identification of indels in next generation sequencing data is a challenge, and algorithms commonly used for indel detection have not been compared on a research cohort of human subject genomic data. Guidelines for the optimal detection of biologically significant indels are limited. We analyzed three sets of human next generation sequencing data (48 samples of a 200 gene target exon sequencing, 45 samples of whole exome sequencing, and 2 samples of whole genome sequencing) using three algorithms for indel detection (Pindel, Genome Analysis Tool Kit's UnifiedGenotyper and HaplotypeCaller).
Results:
We observed variation in indel calls across the three algorithms. The intersection of the three tools comprised only 5.70% of targeted exon, 19.52% of whole exome, and 14.25% of whole genome indel calls. The majority of the discordant indels were of lower read depth and likely to be false positives. When software parameters were kept consistent across the three targets, HaplotypeCaller produced the most reliable results. Pindel results did not validate well without adjustments to parameters to account for varied read depth and number of samples per run. Adjustments to Pindel's M (minimum support for event) parameter improved both concordance and validation rates. Pindel was able to identify large deletions that surpassed the length capabilities of the GATK algorithms.
Conclusions:
Despite the observed variability in indel identification, we discerned strengths among the individual algorithms on specific data sets. This allowed us to suggest best practices for indel calling. Pindel's low validation rate of indel calls made in targeted exon sequencing suggests that HaplotypeCaller is better suited for short indels and multi-sample runs in targets with very high read depth. Pindel allows for optimization of minimum support for events and is best used for detection of larger indels at lower read depths.
Related Concept Videos
Next-generation Sequencing
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features....
Evolutionary Relationships through Genome Comparisons
Sanger Sequencing
Maxam-Gilbert Sequencing
Challenges of the Maxam-Gilbert Method
The...
Genome Annotation and Assembly
Comparing Copy Number Variations and SNPs
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...

