MINI REVIEW article
Front. Genet.
Sec. Computational Genomics
Volume 16 - 2025 | doi: 10.3389/fgene.2025.1722317
This article is part of the Research TopicAdvances in circRNA Research: Disease Associations and Diagnostic InnovationsView all 5 articles
Advances in post-processing methods for multiple sequence alignment
Provisionally accepted
- 1University of Electronic Science and Technology of China, Chengdu, China
- 2Harbin Institute of Technology, Harbin, China
- 3Shenzhen Institute of Information Technology, Shenzhen, China
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The reliability of multiple sequence alignment results (MSA) directly determines the credibility of the conclusions drawn from biological research. However, MSA is inherently an NP-hard problem, making it theoretically impossible to guarantee a globally optimal solution. Consequently, in addition to developing more efficient alignment algorithms, improving the quality of initial alignments through post-processing optimization has become an important strategy for enhancing overall alignment accuracy. Although post-processing methods have shown potential in improving alignment accuracy, there is currently a lack of systematic reviews and summaries. This review provides a systematic overview of the development and key research ideas of multiple sequence alignment post-processing methods over the past three decades, and outlines potential directions for future research.
Keywords: Post-processing, meta-alignment, realigner, realignment, multiple sequence alignment
Received: 10 Oct 2025; Accepted: 20 Oct 2025.
Copyright: © 2025 Zhai, Zhang and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Zhen Li, lizhen5000@sziit.edu.cn
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