Multiple sequence alignment as a sequence-to-sequence learning problemDownload PDF

Published: 01 Feb 2023, Last Modified: 02 Mar 2023ICLR 2023 posterReaders: Everyone
Keywords: sequence alignment, molecular evolution, natural language processing, bioinformatics
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.
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