Max-Margin Models for RNA Secondary Structure Prediction
نویسنده
چکیده
RNA was first explored and understood as a messenger molecule, relaying DNA encodings of amino acids during protein synthesis. Beyond messenger RNA, however, a class of RNA known as non-coding RNA plays fundamental roles in transcriptional and translational gene regulation. As the biological mantra goes, form fits function, and these regulatory roles depend on the 3-D structure of the RNA molecule. The 3-D structure is largely induced by the secondary structure, or the base-pairs of the RNA sequence—unlike DNA, whose complementary strands are fully paired, RNA is single-stranded and displays complex patterns of basepairs. As empirical methods for finding RNA structure, such as crystallography, are time-consuming and involve expensive equipment and expertise, computational methods for predicting RNA secondary structure are of great value to the study of non-coding RNA.
منابع مشابه
MMKnots: A max-margin model for RNA secondary structure prediction including pseudoknots
Motivation: The ideal algorithm for the prediction of pseudoknotted RNA secondary structures will provide fast and accurate predictions for pseudoknots of arbitrary complexity. However, existing algorithms are typically lacking on one of these three axes. Energy-based methods suffer from the intractability of pseudoknotted structure prediction under realistic energy models, while statistical ap...
متن کاملComputational approaches for RNA energy parameter estimation.
Methods for efficient and accurate prediction of RNA structure are increasingly valuable, given the current rapid advances in understanding the diverse functions of RNA molecules in the cell. To enhance the accuracy of secondary structure predictions, we developed and refined optimization techniques for the estimation of energy parameters. We build on two previous approaches to RNA free-energy ...
متن کاملA max-margin model for predicting residue–base contacts in protein–RNA interactions
Motivation: Protein–RNA interactions (PRIs) are essential for many biological processes, so understanding aspects of the sequence and structure in PRIs is important for understanding those processes. Due to the expensive and time-consuming processes required for experimental determination of complex protein–RNA structures, various computational methods have been developed to predict PRIs. Howev...
متن کاملA max-margin model for efficient simultaneous alignment and folding of RNA sequences
MOTIVATION The need for accurate and efficient tools for computational RNA structure analysis has become increasingly apparent over the last several years: RNA folding algorithms underlie numerous applications in bioinformatics, ranging from microarray probe selection to de novo non-coding RNA gene prediction. In this work, we present RAF (RNA Alignment and Folding), an efficient algorithm for ...
متن کاملA max-margin training of RNA secondary structure prediction integrated with the thermodynamic model
Motivation: A popular approach for predicting RNA secondary structure is the thermodynamic nearest neighbor model that finds a thermodynamically most stable secondary structure with the minimum free energy (MFE). For further improvement, an alternative approach that is based on machine learning techniques has been developed. The machine learning based approach can employ a fine-grained model th...
متن کامل