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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.15458 |
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| _version_ | 1866914272161824768 |
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| author | Light, Emily G. Prior, Morgan Daniels, Noah M. Ishaq, Najib |
| author_facet | Light, Emily G. Prior, Morgan Daniels, Noah M. Ishaq, Najib |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_15458 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MuSAlS: A Fast Multiple Sequence Alignment Approach Using Hierarchical Clustering Light, Emily G. Prior, Morgan Daniels, Noah M. Ishaq, Najib Computational Engineering, Finance, and Science 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 |
| title | MuSAlS: A Fast Multiple Sequence Alignment Approach Using Hierarchical Clustering |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2601.15458 |