McQFold

McQFold predicts RNA secondary structures, including pseudoknots, by sampling the posterior distribution of structures using a probabilistic model and Markov-chain Monte Carlo.

Key Features:

• Probabilistic Modeling with SCFG: Employs a stochastic context-free grammar (SCFG) as a prior distribution for RNA secondary structure.
• Handling Pseudoknots: Incorporates pseudoknot configurations into secondary structure predictions despite the NP-complete search space for optimal structures.
• Bayesian Sampling Approach: Uses Bayesian sampling to estimate probable structures and quantify uncertainty in predictions.
• MCMC Methodology: Uses Markov-chain Monte Carlo (MCMC) to sample RNA structures from the posterior distribution given a nucleotide sequence.

Scientific Applications:

• tmRNA analysis: Applied to transfer-messenger RNA (tmRNA) to predict structures that include pseudoknots.
• Benchmarking on datasets: Validated on both real-world biological datasets and simulated datasets.
• Uncertainty quantification: Provides probabilistic assessments of structure predictions to inform studies of RNA folding dynamics.

Methodology:

Integrates a probabilistic model with a stochastic context-free grammar (SCFG) prior and employs Markov-chain Monte Carlo (MCMC) to sample from the posterior distribution of RNA secondary structures.