Repository: Freie Universität Berlin, Math Department

Linear: a framework to enable existing software to resolve structural variants in long reads with flexible and efficient alignment-free statistical models

Pan, Chenxu and Rahn, René and Heller, David and Reinert, Knut (2023) Linear: a framework to enable existing software to resolve structural variants in long reads with flexible and efficient alignment-free statistical models. Briefings in Bioinformatics, 24 (2). ISSN 1467-5463; Online ISSN 1477-4054

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Official URL: https://doi.org/10.1093/bib/bbad071

Abstract

Alignment is the cornerstone of many long-read pipelines and plays an essential role in resolving structural variants (SVs). However, forced alignments of SVs embedded in long reads, inflexibility of integrating novel SVs models and computational inefficiency remain problems. Here, we investigate the feasibility of resolving long-read SVs with alignment-free algorithms. We ask: (1) Is it possible to resolve long-read SVs with alignment-free approaches? and (2) Does it provide an advantage over existing approaches? To this end, we implemented the framework named Linear, which can flexibly integrate alignment-free algorithms such as the generative model for long-read SV detection. Furthermore, Linear addresses the problem of compatibility of alignment-free approaches with existing software. It takes as input long reads and outputs standardized results existing software can directly process. We conducted large-scale assessments in this work and the results show that the sensitivity, and flexibility of Linear outperform alignment-based pipelines. Moreover, the computational efficiency is orders of magnitude faster.

Item Type:Article
Uncontrolled Keywords:alignment-free approach, graph generative model, structural variants resolution, long-read analysis
Subjects:Mathematical and Computer Sciences > Computer Science
Divisions:Department of Mathematics and Computer Science > Institute of Computer Science > Algorithmic Bioinformatics Group
ID Code:2947
Deposited By: Anja Kasseckert
Deposited On:19 Apr 2023 13:18
Last Modified:19 Apr 2023 13:18

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