Open AccessTool evaluation for the detection of variably sized indels from next generation whole genome and targeted sequencing data
Author(s) -
Ning Wang,
Vladislav Lysenkov,
Katri Orte,
Veli Kairisto,
Juhani Aakko,
Sofia Khan,
Laura L. Elo
Publication year - 2022
Publication title -
plos computational biology/plos computational biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.628
H-Index - 182
eISSN - 1553-7358
pISSN - 1553-734X
DOI - 10.1371/journal.pcbi.1009269
Subject(s) - indel , indel mutation , dna sequencing , computational biology , genome , 1000 genomes project , human genome , biology , genomics , computer science , genetics , gene , single nucleotide polymorphism , genotype
Insertions and deletions (indels) in human genomes are associated with a wide range of phenotypes, including various clinical disorders. High-throughput, next generation sequencing (NGS) technologies enable the detection of short genetic variants, such as single nucleotide variants (SNVs) and indels. However, the variant calling accuracy for indels remains considerably lower than for SNVs. Here we present a comparative study of the performance of variant calling tools for indel calling, evaluated with a wide repertoire of NGS datasets. While there is no single optimal tool to suit all circumstances, our results demonstrate that the choice of variant calling tool greatly impacts the precision and recall of indel calling. Furthermore, to reliably detect indels, it is essential to choose NGS technologies that offer a long read length and high coverage coupled with specific variant calling tools.