Evidence map›Paper›PMID 40712047›Full record

ArticleBioinformatics (Oxford, England)2025

ReAlign-P: a vertical iterative realignment method for protein multiple sequence alignment.

Yixiao Zhai, Pinglu Zhang, Quan Zou, Ximei Luo

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Yixiao ZhaiInstitute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 610054, China.ORCID 0000-0002-5068-5672
Pinglu ZhangInstitute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 610054, China.ORCID 0009-0002-1788-3084
Quan ZouInstitute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 610054, China.ORCID 0000-0001-6406-1142
Ximei LuoInstitute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 610054, China.ORCID 0000-0003-2956-6799

Funding

Municipal Government of Quzhou 2024D001National Natural Science Foundation of China 62425107National Natural Science Foundation of China 62450002National Science and Technology Major Project 2022ZD0117700
6 · The paper itself

Abstract

motivationReliable protein multiple sequence alignment (MSA) is essential for downstream biomedical research and directly impacts the accuracy of analytical results. However, protein sequences often exhibit low similarity and complex alignment patterns, and existing general alignment tools frequently fall short in terms of accuracy. Many current realignment methods are outdated, suffering from issues such as code obsolescence and inadequate precision. As a result, there is a pressing need for realignment methods that can better address these challenges.

resultsThis study introduces ReAlign-P, a realignment tool designed specifically for protein MSA. ReAlign-P first divides the initial alignment into three regions and applies a novel vertical iterative realignment strategy to optimize the more conserved middle region. This method is by default compatible with MUSCLE5 for realignment, leading to a significant improvement in accuracy. We evaluated initial alignments generated using 10 different MSA parameter configurations across four protein benchmark datasets. The results demonstrate that ReAlign-P consistently outperforms or matches the quality of the initial alignments in all cases. In contrast, RASCAL-the only other currently functional protein realignment tool-sometimes even reduces alignment quality. ReAlign-P not only delivers more substantial improvements but also exhibits greater stability, effectively addressing the gap in available protein realignment tools. AVAILABILITY AND IMPLEMENTATION: The source code and test data for ReAlign-P are available on GitHub (https://github.com/malabz/ReAlign-P).

Indexed as

ProteinsSequence AlignmentSequence Analysis, ProteinSoftwareAlgorithmsAmino Acid SequenceDatabases, ProteinProteins

Identifiers

PMID40712047
PMCPMC12342757

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.