DCAlign v1.0: Aligning biological sequences using co-evolution models and informed priors

TL;DR

DCAlign v1.0 introduces a faster biological sequence alignment method that incorporates an empirical prior to efficiently handle conservation and co-evolution signals, reducing computational time significantly.

Contribution

The paper presents an improved version of DCAlign that incorporates an empirical prior, enabling faster alignment of sequences while maintaining accuracy in co-evolution signal detection.

Findings

Significant reduction in computational time for sequence alignment.

Effective handling of conservation and co-evolution signals.

Maintains alignment accuracy with the new prior approach.

Abstract

DCAlign is a new alignment method able to cope with the conservation and the co-evolution signals that characterize the columns of multiple sequence alignments of homologous sequences. However, the pre-processing steps required to align a candidate sequence are computationally demanding. We show in v1.0 how to dramatically reduce the overall computing time by including an empirical prior over an informative set of variables mirroring the presence of insertions and deletions.