Comparative evaluation of SNVs, indels, and structural variations detected with short- and long-read sequencing data

• Published in: Human genome variation Vol. 11; no. 1; pp. 18 - 10
• Main Authors: Kosugi, Shunichi, Terao, Chikashi
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 17.04.2024; Springer Nature B.V; Nature Publishing Group
• Subjects: 631/114/2785; 631/1647/2217; Algorithms; Biomedical and Life Sciences; Biomedicine; Gene Expression; Gene Function; Gene Therapy; Human Genetics; Molecular Medicine
• ISSN: 2054-345X; 2054-345X
• DOI: 10.1038/s41439-024-00276-x

Short- and long-read sequencing technologies are routinely used to detect DNA variants, including SNVs, indels, and structural variations (SVs). However, the differences in the quality and quantity of variants detected between short- and long-read data are not fully understood. In this study, we comprehensively evaluated the variant calling performance of short- and long-read-based SNV, indel, and SV detection algorithms (6 for SNVs, 12 for indels, and 13 for SVs) using a novel evaluation framework incorporating manual visual inspection. The results showed that indel-insertion calls greater than 10 bp were poorly detected by short-read-based detection algorithms compared to long-read-based algorithms; however, the recall and precision of SNV and indel-deletion detection were similar between short- and long-read data. The recall of SV detection with short-read-based algorithms was significantly lower in repetitive regions, especially for small- to intermediate-sized SVs, than that detected with long-read-based algorithms. In contrast, the recall and precision of SV detection in nonrepetitive regions were similar between short- and long-read data. These findings suggest the need for refined strategies, such as incorporating multiple variant detection algorithms, to generate a more complete set of variants using short-read data.