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BenchPA: A Benchmarking Framework for Pairwise Sequence Alignment Methods

Zhuang Liu,Jinshuo Lv,Shanru Zhang,Xiao Zhu,Wen Liu,Wei Quan

Published 2025 in IEEE International Conference on Bioinformatics and Biomedicine

ABSTRACT

Pairwise sequence alignment of long reads is a critical step in many bioinformatics analyses. Although many pairwise sequence alignment algorithms have emerged for long-read sequencing data, the absence of dedicated evaluation platforms has impeded rigorous validation of their computational effectiveness and alignment accuracy. Here, we present an integrated benchmarking framework for pairwise sequence alignment methods. This framework introduces two novel evaluation metrics to assess alignment accuracy effectively, and employs error model-based simulated data incorporating platform specific error profiles during experimental validation to faithfully replicate real-world pairwise sequence alignment scenarios. To demonstrate the capabilities of our evaluation framework, we assess state-of-the-art pairwise sequence alignment tools across diverse genomic datasets. The assessment includes both real datasets (Illumina, PacBio CLR, PacBio HiFi, ONT Ultra-long) and simulated datasets spanning sequence lengths from 1 kbp to 1 Mbp with error rates from 1% to 20%. The analysis results and visualization reports generated by our automated Snakemake pipeline are designed to guide tool selection and inform future method development. Source code is available from https://github.com/liuz138/Align_benchmark under the MIT license.

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