Advances in sequencing technology have facilitated population-scale long-read analysis, in which one of the main challenges is arguably developing high-performance computational pipelines. Sequence alignment and assembly are two main long-read analysis methods. Alignment-based pipelines are commonly more efficient and require less read coverage than assembly-based ones, and thus are more applicable to population-scale analysis. However, alignment-based pipelines are less effective in reconstructing highly diverse structures in ultra-long reads such as intra-read SVs. Here, we propose a new filter-based pipeline that is designed to capture rearrangement signals at an earlier stage than conventional pipelines to improve long-read analysis performance. To this end, we investigated the feasibility and essential methods of the design and assessed the performance of the pipeline. Correspondingly, this work comprises three parts starting with data structure optimizations then module development and finally large-scale assessments. Assessments based on high-quality datasets suggest that filter-based pipelines are comparable to or outperform conventional pipelines in terms of detecting complex intra-read rearrangements and computational efficiency. Therefore, the newly proposed pipeline may further benefit population-scale long-read analysis.
