LearnMSA: learning and aligning large protein families.

ABSTRACT:

Background

The alignment of large numbers of protein sequences is a challenging task and its importance grows rapidly along with the size of biological datasets. State-of-the-art algorithms have a tendency to produce less accurate alignments with an increasing number of sequences. This is a fundamental problem since many downstream tasks rely on accurate alignments.

Results

We present learnMSA, a novel statistical learning approach of profile hidden Markov models (pHMMs) based on batch gradient descent. Fundamentally different from popular aligners, we fit a custom recurrent neural network architecture for (p)HMMs to potentially millions of sequences with respect to a maximum a posteriori objective and decode an alignment. We rely on automatic differentiation of the log-like

SUBMITTER: Becker F 

PROVIDER: S-EPMC9673500 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature