End-to-end learning of multiple sequence alignments with differentiable Smith-Waterman.

ABSTRACT:

Motivation

Multiple sequence alignments (MSAs) of homologous sequences contain information on structural and functional constraints and their evolutionary histories. Despite their importance for many downstream tasks, such as structure prediction, MSA generation is often treated as a separate pre-processing step, without any guidance from the application it will be used for.

Results

Here, we implement a smooth and differentiable version of the Smith-Waterman pairwise alignment algorithm that enables jointly learning an MSA and a downstream machine learning system in an end-to-end fashion. To demonstrate its utility, we introduce SMURF (Smooth Markov Unaligned Random Field), a new method that jointly learns an alignment and the parameters of a Markov Random Field for unsuperv

SUBMITTER: Petti S 

PROVIDER: S-EPMC9805565 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature