rRNARNAksi

rRNARNAksi is a computational framework designed to identify and characterize functional RNAs within ribosomal RNA (rRNA) sequences. Developed in 2022 by a consortium of computational biologists and microbiologists, its primary goal is to annotate non‑canonical RNA motifs that influence ribosome assembly and function. The tool integrates multiple data types, including secondary structure models, evolutionary conservation scores, and experimental cross‑linking data, to produce high‑confidence predictions of ribosomal RNA-associated regulatory elements.

The core algorithm uses a hybrid approach: a de‑novo motif discovery stage employs stochastic context‑free grammars to scan rRNA sequences for unusual structural signatures, followed by a refinement stage that applies a support vector machine trained on experimentally validated rRNA motifs. In contrast to traditional rRNA annotation pipelines that focus mainly on conserved housekeeping sequences, rRNARNAksi specifically targets low‑abundance or lineage‑specific motifs that may confer adaptive advantages in extremophiles or pathogenic bacteria. The output includes a ranked list of candidate motifs, their positional context within the ribosome, and predicted functional annotations.

Applications of rRNARNAksi span evolutionary studies, where it helps reconstruct the phylogenetic history of ribosomal RNA features, and drug discovery, by uncovering novel regulatory elements that could serve as antimicrobial targets. It has been applied to over 1,500 bacterial and archaeal genomes, revealing several previously uncharacterized small RNA elements located near ribosomal protein operons. Limitations arise from the reliance on high‑quality multiple sequence alignments, which can be challenging for highly divergent rRNA genes. Future updates aim to incorporate machine‑learning models that can learn directly from raw sequence data and to expand the database of experimentally validated motifs through community submissions. The project is maintained as an open‑source repository, encouraging collaboration across computational and experimental disciplines.