Aligning 415 519 proteins in less than two hours on PC
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Abstract (arXiv)
Rapid development of modern sequencing platforms enabled an unprecedented growth of protein families databases. The abundance of sets composed of hundreds of thousands sequences is a great challenge for multiple sequence alignment algorithms. In the article we introduce FAMSA, a new progressive algorithm designed for fast and accurate alignment of thousands of protein sequences. Its features include the utilisation of longest common subsequence measure for determining pairwise similarities, a novel method of gap costs evaluation, and a new iterative refinement scheme. Importantly, its implementation is highly optimised and parallelised to make the most of modern computer platforms. Thanks to the above, quality indicators, namely sum-of-pairs and total-column scores, show FAMSA to be superior to competing algorithms like Clustal Omega or MAFFT for datasets exceeding a few thousand of sequences. The quality does not compromise time and memory requirements which are an order of magnitude lower than that of existing solutions. For example, a family of 415 519 sequences was analysed in less than two hours and required only 8GB of RAM. FAMSA is freely available at http://sun.aei.polsl.pl/REFRESH/famsa.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 22 Mar 2016.
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Citing papers per year: 2022: 1; 2023: 1; 2024: 0; 2025: 1; 2026: 1.
4 papers.
- 2022Exploring evolution-aware & -free protein language models as protein function predictorscited by 20
- 2023Retrieved Sequence Augmentation for Protein Representation Learningcited by 7
- 2026Towards A Generative Protein Evolution Machine with DPLM-Evocited by 3
- 2025Generative Artificial Intelligence in Bioinformatics: A Systematic Review of Models, Applications, and Methodological Advancescited by 0
- 2026Towards A Generative Protein Evolution Machine with DPLM-Evo30 Apr 2026
- 2025Generative Artificial Intelligence in Bioinformatics: A Systematic Review of Models, Applications, and Methodological Advances5 Nov 2025
- 2023Retrieved Sequence Augmentation for Protein Representation Learning24 Feb 2023
- 2022Exploring evolution-aware & -free protein language models as protein function predictors14 Jun 2022