AlignNLP: Harnessing Natural Language Processing Methods for Sequence Alignment
Автор: TAUVOD
Загружено: 2022-03-06
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AlignNLP: Harnessing Natural Language Processing Methods for Sequence Alignment
The sequence alignment problem is one of the most fundamental issues in bioinformatics and indeed a plethora of methods has been devised to tackle it. In practice, these methods are applied with the same parameters (i.e., default configuration) on every given input, implicitly assuming a single evolutionary model. This is clearly an oversimplification of biological reality.
Here we introduce AlignNLP, a state-of-the-art methodology for aligning sequences using a deep neural network. AlignNLP accounts for the possible variability of the evolutionary process among different lineages by using an ensemble of transformers. Each transformer is trained on millions of samples from different evolutionary models, leading to a superb alignment accuracy, overperforming commonly used methods, such as MAFFT and Prank.
Edo Dotan, Graduate Student, Tel Aviv University
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