Eugene F. Baulin

SQUARNA improves RNA secondary structure prediction by combining stem maximization with free energy minimization, outperforming existing methods including deep learning models.

This paper evaluates blind predictions of dynamic water and ion ensembles around RNA using cryo-EM densities from CASP16, finding that molecular dynamics approaches perform best.

The paper systematically surveys GNRA tetraloop-like motifs in nucleic acid 3D structures, identifying twelve recurrent backbone topology variants and their interactions.

ARTEM identifies RNA and DNA tertiary motifs by searching structural databases, revealing new kink-turn variants and their functional implications.

ClaPNAC classifies nucleic acid-protein contacts in 3D structures using geometric features, distinguishing stacking, pseudo pairs, and phosphate/ribose interactions.

The paper introduces MIXALIME, a statistical framework for detecting allele-specific events in sequencing data, and demonstrates its application in constructing an atlas of allele-specific chromatin accessibility.

ARTEMIS improves RNA 3D structure superposition and alignment, revealing conserved helical motifs across non-coding RNAs with sequence permutations.

SQUARNA is an RNA secondary structure prediction method that uses a greedy stem formation model, performing competitively on single sequences and outperforming existing tools on sequence alignment inputs.

The paper surveys long-range tertiary interactions and motifs in non-coding RNA structures, identifies three common interaction types, and introduces a new dataset and algorithm for their analysis.

The paper identifies allele-specific transcription factor binding events across the human genome using ChIP-Seq data, revealing regulatory variants linked to disease and molecular mechanisms.