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 current AF tools.