Leaf: an ultrafast filter for population-scale long-read SV detection

Abstract

Abstract Advances in sequencing technology have facilitated population-scale long-read structural variant (SV) detection. Arguably, one of the main challenges in population-scale analysis is developing effective computational pipelines. Here, we present a new filter-based pipeline for population-scale long-read SV detection. It better captures SV signals at an early stage than conventional assembly-based or alignment-based pipelines. Assessments in this work suggest that the filter-based pipeline helps better resolve intra-read rearrangements. Moreover, it is also more computationally efficient than conventional pipelines and thus may facilitate population-scale long-read applications.

Keywords

• Filter-based pipelines
• Intra-read SV detection
• Population-scale long-read applications
• Generative model
• Extended SAM/BAM