Metatranscriptomic (MetaT) sequencing provides critical insights into the gene expression and functional activity of microbial communities. However, its utility is limited by the overwhelming abundance of ribosomal RNA (rRNA), which typically represents [≥]90% of total RNA [1-2]. A major obstacle to efficient MetaT analysis is the removal of highly abundant rRNA transcripts present in complex microbial communities, which may contain thousands of species. Although commercial rRNA depletion kits can effectively reduce rRNA content, they are typically optimized for specific host microbiomes and often underperform in others. For example, probes designed for the human gut microbiome frequently show reduced efficiency when applied to non-human samples such as mouse cecal donor samples – a common model in microbiome research. Regardless of the depletion strategy used, designing rRNA removal probes solely based on a microbiome’s taxonomic composition often requires an extensive number of probes, making the approach expensive, difficult to manufacture, and sometimes technically impractical [3]. Here, we present RiboZAP, a species-agnostic computational pipeline for designing custom RNase H depletion probes directly from MetaT sequencing data and without prior knowledge of sample composition. Our results show that the probes generated with RiboZAP are efficient for removal of rRNA content, increasing messenger RNA (mRNA), and improving transcriptome coverage. This provides a cost-effective approach to maximize the value of MetaT sequencing.
OptoLoop: An optogenetic tool to probe the functional role of genome organization
The genome folds inside the cell nucleus into hierarchical architectural features, such as chromatin loops and domains. If and how this genome organization influences the