ConsAlign: simultaneous RNA structural aligner based on rich transfer learning and thermodynamic ensemble model of alignment scoring.

ABSTRACT:

Motivation

To capture structural homology in RNAs, alignment and folding (AF) of RNA homologs has been a fundamental framework around RNA science. Learning sufficient scoring parameters for simultaneous AF (SAF) is an undeveloped subject because evaluating them is computationally expensive.

Results

We developed ConsTrain-a gradient-based machine learning method for rich SAF scoring. We also implemented ConsAlign-a SAF tool composed of ConsTrain's learned scoring parameters. To aim for better AF quality, ConsAlign employs (1) transfer learning from well-defined scoring models and (2) the ensemble model between the ConsTrain model and a well-established thermodynamic scoring model. Keeping comparable running time, ConsAlign demonstrated competitive AF prediction quality among

SUBMITTER: Tagashira M 

PROVIDER: S-EPMC10172041 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature