Elena Rivas

The paper develops a hybrid computational and high-throughput experimental method to identify and analyze full riboswitch sequences, generating covariation models that support previously published structural rearrangement mechanisms.

RNAcentral integrates literature and gene-level data to enhance non-coding RNA sequence analysis and functional understanding.

This paper introduces CaCoFold-R3D, a method that jointly predicts RNA 3D motifs and secondary structure using evolutionary information, demonstrating its effectiveness for RNA structure prediction.

This paper shows that a simple probabilistic model with few parameters can learn RNA base-pairing rules from RNA sequences alone, without structural or alignment data.

RNAhub generates RNA multiple sequence alignments and assesses conserved RNA structure using covariation analysis.

The paper identifies differences between protein and RNA structure and proposes RNA-specific methods to improve deep learning models for RNA 3D structure prediction.

Rfam 15.0 expands coverage to 26,106 genomes, improves RNA family annotations with 3D structures, and synchronizes with miRBase, enhancing RNA research and machine learning applications.

The paper introduces RNA3DB, a dataset of structured RNAs designed for training and benchmarking deep learning models, with non-redundant sequence and structure splits to improve generalization.

The paper introduces a helix-level covariation measure to improve detection of conserved RNA structures and identifies an artifact in previous methods.

The paper compares RNA design fitness functions and finds that maximizing probability outperforms minimizing ensemble defect.

The paper identifies 17 structurally conserved non-coding RNAs in fungal genomes using a bioinformatics method combining homology search and covariation analysis.

The paper introduces CaCoFold, a method that predicts conserved RNA structures by integrating positive and negative evolutionary information, improving accuracy over existing methods.

The paper challenges the validity of covariation analysis in lncRNA structures by showing that the RAFS statistic can falsely identify covarying base pairs due to primary sequence conservation.

The paper assesses the power of sequence covariation to detect conserved RNA structure and finds that some previously unconfirmed RNAs may have sufficient covariation signal.