# Aligning 415 519 proteins in less than two hours on PC

arXiv: https://arxiv.org/abs/1603.06958 · HTML: https://mlsanity.com/paper/1603.06958/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Mar 2016.

## Built on by (most cited first, 4 of 4)

- [Exploring evolution-aware & -free protein language models as protein function predictors](https://mlsanity.com/paper/2206.06583/) (2022)
- [Retrieved Sequence Augmentation for Protein Representation Learning](https://mlsanity.com/paper/2302.12563/) (2023)
- [Towards A Generative Protein Evolution Machine with DPLM-Evo](https://mlsanity.com/paper/2605.00182/) (2026)
- [Generative Artificial Intelligence in Bioinformatics: A Systematic Review of Models, Applications, and Methodological Advances](https://mlsanity.com/paper/2511.03354/) (2025)

## 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.