MuSAlS: A Fast Multiple Sequence Alignment Approach Using Hierarchical Clustering

2026-01-21

AI Summary

This work proposes a novel approach that integrates hierarchical clustering with exact dynamic programming to address the poor computational efficiency and limited scalability of multiple sequence alignment (MSA) on large-scale sequence data. By constructing a guide tree based on Levenshtein distance and employing a bottom-up strategy to invoke the Needleman–Wunsch algorithm, the method achieves high alignment accuracy while substantially improving runtime performance. Implemented efficiently in Rust, the proposed framework matches the accuracy of state-of-the-art tools yet significantly reduces computation time. This study presents the first effective fusion of exact dynamic programming with scalable hierarchical clustering, making it well-suited for large-scale genomic and metagenomic analyses.

Technology Category

Machine Learning: Scalability of ML SystemsData Mining & Knowledge Management: Scalability, Parallel & Distributed SystemsSearch and Optimization: Metareasoning and Metaheuristics

Application Category

Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphsSearch and Retrieval-Augmented AI: Efficiency and scalability of Web search enginesSemantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

Motivation: The multiple sequence alignment (MSA) problem has been extensively studied, with numerous approaches developed over recent years. With the rapid growth of sequence data, there is an increasing need for fast and accurate MSA tools that scale effectively to large datasets. Building on our previous work on CLAM, we are able to use exact dynamic programming (Needleman-Wunsch) while scaling to large datasets. We introduce MuSAlS (Multiple Sequence Alignment at Scale), a fast and scalable de novo MSA aligner. MuSAlS uses hierarchical clustering to construct a guide tree based on the Levenshtein distance metric, enabling efficient and accurate alignment through a bottom-up approach. Results: MuSAlS achieves competitive accuracy compared to state-of-the-art methods while significantly improving runtime performance. This makes it a valuable tool for researchers analyzing large-scale genomic and metagenomic datasets, addressing the growing demand for scalable bioinformatics solutions. Availability and Implementation: MuSAlS is implemented in the Rust programming language, and available at https://github.com/URI-ABD/clam

Problem

• Multiple Sequence Alignment
• Scalability
• Large-scale genomic data
• Computational efficiency

Innovation

• Multiple Sequence Alignment
• Hierarchical Clustering
• Levenshtein Distance
• Scalable Bioinformatics
• Exact Dynamic Programming