Multiple sequence alignment as a sequence-to-sequence learning problem

Edo Dotan; Yonatan Belinkov; Oren Avram; Elya Wygoda; Noa Ecker; Michael Alburquerque; Omri Keren; Gil Loewenthal

ICLR 2023poster7 citations

Multiple sequence alignment as a sequence-to-sequence learning problem

Edo Dotan, Yonatan Belinkov, Oren Avram, Elya Wygoda, Noa Ecker, Michael Alburquerque, Omri Keren, Gil Loewenthal

Abstract

The sequence alignment problem is one of the most fundamental problems in bioinformatics and a plethora of methods were devised to tackle it. Here we introduce BetaAlign, a methodology for aligning sequences using an NLP approach. BetaAlign accounts for the possible variability of the evolutionary process among different datasets by using an ensemble of transformers, each trained on millions of samples generated from a different evolutionary model. Our approach leads to alignment accuracy that is similar and often better than commonly used methods, such as MAFFT, DIALIGN, ClustalW, T-Coffee, PRANK, and MUSCLE.

sequence alignmentmolecular evolutionnatural language processingbioinformatics

BibTeX

@inproceedings{
dotan2023multiple,
title={Multiple sequence alignment as a sequence-to-sequence learning problem},
author={Edo Dotan and Yonatan Belinkov and Oren Avram and Elya Wygoda and Noa Ecker and Michael Alburquerque and Omri Keren and Gil Loewenthal and Tal Pupko},
booktitle={The Eleventh International Conference on Learning Representations },
year={2023},
url={https://openreview.net/forum?id=8efJYMBrNb}
}