Transfer RNAs (tRNAs) and their modifications are central to bacterial translation and physiology, yet their roles in stress adaptation remain underexplored. While extensively studied in eukaryotes, a...

Structural RNAs exhibit a vast array of recurrent short three-dimensional (3D) elements found in loop regions involving non-Watson–Crick interactions that help arrange canonical double helices into tertiary structures. Here we present CaCoFold-R3D, a probabilistic grammar that predicts these RNA 3D motifs (also termed modules) jointly with RNA secondary structure over a sequence or alignment. CaCoFold-R3D uses evolutionary information present in an RNA alignment to reliably identify canonical helices (including pseudoknots) by covariation. Here we further introduce the R3D grammars, which also exploit helix covariation that constrains the positioning of the mostly noncovarying RNA 3D motifs. Our method runs predictions over an almost-exhaustive list of over 50 known RNA motifs (‘everything’). Motifs can appear in any nonhelical loop region (including three-way, four-way and higher junctions) (‘everywhere’). All structural motifs as well as the canonical helices are arranged into one single structure predicted by one single joint probabilistic grammar (‘all-at-once’). Our results demonstrate that CaCoFold-R3D is a valid alternative for predicting the all-residue interactions present in a RNA 3D structure. CaCoFold-R3D is fast and easily customizable for novel motif discovery and shows promising value both as a strong input for deep learning approaches to all-atom structure prediction as well as toward guiding RNA design as drug targets for therapeutic small molecules.

Microbiome sequencing measures relative rather than absolute abundances, providing no direct information about total microbial load. Normalization methods attempt to compensate, but rely on strong, of...

Laboratory models provide tractable, reproducible systems that have long served as foundational tools in microbiology. However, the extent to which these models accurately mimic the biological environ...

Prokaryotic genes are arranged in operons, with functionally related genes often located adjacent to one another (1). There are several ways in which the operonic organization of genes facilitates the...

Establishing antisense oligomers as versatile, non-genetic tools to silence phage mRNAs opens applications in basic research and biotechnology, as shown by identifying essential factors for propagatio...

The in-person component will be held at the New York Genome Center, 101 6th Ave, New York, NY 10013. All times below are Eastern Time.

LexicMap uses a fixed set of probes to efficiently query gene sequences for fast and low-memory alignment.

Symbiotic bacteria in the human intestinal microbiota provide many pivotal functions to human health and occupy distinct biogeographic niches within the gut. Yet the molecular basis underlying niche-s...

Abstract. Two-dimensional drawing of nucleic acid structures, particularly RNA structures, is fundamental to the communication of nucleic acids research. H

Background Variation within the prokaryotic pangenome is not random, and natural selection that favours particular combinations of genes appears to dominate over random drift. What is less clear is wh...

Deep learning models have achieved state-of-the-art performance at predicting diverse genomic modalities, yet their promise for biological discovery lies in how they are used after demonstrating their...