STRUCT: a statistical approach to identify RNA secondary structures from raw sequencing data, bypassing multiple sequence alignment 1. STRUCT introduces an innovative MSA-free, assembly-free statistical method to detect RNA secondary structures directly from raw sequencing data, offering unprecedented scalability and accuracy. 2. Unlike traditional methods, STRUCT bypasses the limitations of multiple sequence alignment (MSA), reducing statistical biases and confounding factors associated with evolutionary relationships. 3. STRUCT employs statistical testing for base-pair covariation (BPC) and stem-variation exclusion (SVE), enabling precise identification of conserved RNA structures across diverse datasets. 4. The tool rediscovered key HIV-1 structural RNA elements like the ribosomal frameshift signal, psi packaging signal, and TAR element from longitudinal datasets, demonstrating its accuracy in de novo structure identification. 5. STRUCT’s application to mosquito metatranscriptomics revealed conserved rRNA elements and novel viral RNA structures, including sequences from viruses not present in existing reference databases. 6. By analyzing raw RNA-seq data without requiring a reference genome, STRUCT enables high-throughput discovery of RNA structural motifs crucial for understanding functional RNA elements and viral genome structures. 7. STRUCT sets a new standard in RNA structure prediction, offering a computationally efficient, metadata-independent approach suitable for diverse biological and clinical research applications. @arjunrustagi 💻Code: github.com/juliefrwang/splas… 📜Paper: biorxiv.org/content/10.1101/… #RNAstructure #Bioinformatics #MachineLearning #StructuralBiology #RNAseq #ComputationalBiology #HIVResearch #Metatranscriptomics