Comprehensive evaluation and guidance of structural variation detection tools in chicken whole genome sequence data

Abstract

Abstract Background Structural variations (SVs) are widespread across genome and have a great impact on evolution, disease, and phenotypic diversity. Despite the development of numerous bioinformatic tools, commonly referred to as SV callers, tailored for detecting SVs using whole genome sequence (WGS) data and employing diverse algorithms, their performance necessitates rigorous evaluation with real data and validated SVs. Moreover, a considerable proportion of these tools have been primarily designed and optimized using human genome data. Consequently, their applicability and performance in Avian species, characterized by smaller genomes and distinct genomic architectures, remain inadequately assessed. Results We performed a comprehensive assessment of the performance of ten widely used SV callers using population-level real genomic data with the validated five common types of SVs. The performance of SV callers varies with the types and sizes of SVs. As compared with other tools, GRIDSS, Lumpy, Wham, and Manta present better detection accuracy. Pindel can detect more small SVs than others. CNVnator and CNVkit can detect more medium and large copy number variations. Given the poor consistency among different SV callers, the combination calling strategy is not recommended. All tools show poor ability in the detection of insertions (especially with size > 150 bp). At least 50× read depth is required to detect more than 80% of the SVs for most tools. Conclusions This study highlights the importance and necessity of using real sequencing data, rather than simulated data only, with validated SVs for SV caller evaluation. Some practical guidance and suggestions are provided for SV detection in future researches.

Keywords

• Structural variation
• Copy number variation
• Population genomic sequence
• WGS
• Read depth
• SV caller