Tradeoffs in alignment and assembly-based methods for structural variant detection with long-read sequencing data

Yichen Henry Liu, Can Luo, Staunton G. Golding, Jacob B. Ioffe and Xin Maizie Zhou ()
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Yichen Henry Liu: Vanderbilt University
Can Luo: Vanderbilt University
Staunton G. Golding: Vanderbilt University
Jacob B. Ioffe: Vanderbilt University
Xin Maizie Zhou: Vanderbilt University

Nature Communications, 2024, vol. 15, issue 1, 1-22

Abstract: Abstract Long-read sequencing offers long contiguous DNA fragments, facilitating diploid genome assembly and structural variant (SV) detection. Efficient and robust algorithms for SV identification are crucial with increasing data availability. Alignment-based methods, favored for their computational efficiency and lower coverage requirements, are prominent. Alternative approaches, relying solely on available reads for de novo genome assembly and employing assembly-based tools for SV detection via comparison to a reference genome, demand significantly more computational resources. However, the lack of comprehensive benchmarking constrains our comprehension and hampers further algorithm development. Here we systematically compare 14 read alignment-based SV calling methods (including 4 deep learning-based methods and 1 hybrid method), and 4 assembly-based SV calling methods, alongside 4 upstream aligners and 7 assemblers. Assembly-based tools excel in detecting large SVs, especially insertions, and exhibit robustness to evaluation parameter changes and coverage fluctuations. Conversely, alignment-based tools demonstrate superior genotyping accuracy at low sequencing coverage (5-10×) and excel in detecting complex SVs, like translocations, inversions, and duplications. Our evaluation provides performance insights, highlighting the absence of a universally superior tool. We furnish guidelines across 31 criteria combinations, aiding users in selecting the most suitable tools for diverse scenarios and offering directions for further method development.

Date: 2024
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DOI: 10.1038/s41467-024-46614-z

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