David A. Hendrix

The paper applies single-cell proteomics to study proteome changes during NGF-induced PC12 differentiation, revealing subpopulations not detected in bulk data.

This study identifies the PDGF/VEGF signaling pathway as a key driver of immunometabolism in Drosophila during parasitoid wasp infection, revealing a trade-off between immune function and development.

The paper introduces local stability compensation (LSC) as a principle governing RNA folding, showing it is prevalent in RNA structures and validated through chemical mapping experiments.

The paper identifies RNA hairpins that retain their structure after dinucleotide shuffling and finds they have specific sequence features and biological relevance.

The paper improves protein-coding potential prediction using a Fourier-transform inspired architecture and a machine translation task, showing better classification performance than traditional methods.

bpRNA-align improves RNA secondary structure alignment and clustering by using a customized gap penalty and substitution matrix, showing better performance on a benchmark dataset.

The paper identifies age-dependent changes in diurnal transcriptome rhythms in Drosophila using a novel Fourier-based method, revealing shifts in core clock and pathway rhythmicity.

The paper presents a chromosome-level assembly of the Cascade hop genome, identifies sex chromosomes, and reveals molecular evolution patterns in the Cannabaceae family.

The paper identifies position-dependent codon usage bias in the human transcriptome and shows its relationship with translational efficiency and functional enrichment.

The paper presents a phased diploid assembly of the Cascade hop genome, revealing patterns of selection and haplotype variation, and identifies a homolog of CBDAS.

The paper trains a deep recurrent neural network to predict RNA protein-coding potential, finding it outperforms existing methods and reveals context-sensitive codons predictive of coding potential.

The paper introduces bpRNA, a tool for annotating RNA secondary structures, and presents a large database of annotated structures for improving structure prediction and analysis.

LinearFold predicts RNA secondary structures in linear time using dynamic programming with beam search pruning, achieving higher accuracy on long sequences and long-range base pairs.