RiboZAP: a species-agnostic pipeline for rRNA depletion probe design in metatranscriptomics

Metatranscriptomic (MetaT) sequencing provides insights into gene expression and functional activity within microbial communities, but its utility is limited by the high abundance of ribosomal RNA (rRNA), which often accounts for ≥ 90% of total RNA. Efficient rRNA depletion is therefore essential to maximize mRNA coverage and sequencing efficiency. 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 and difficult to manufacture. To address these challenges, we developed RiboZAP, a species-agnostic computational pipeline that designs custom RNase H depletion probes directly from MetaT sequencing data without prior knowledge of sample composition.

RiboZAP-designed probe sets achieved 43-62% predicted rRNA depletion across both design and independent mouse cecal MetaT samples. Probes performed effectively on non-design samples, with depletion performance consistent with those observed in the design samples. Read composition and taxonomic diversity of residual rRNA, calculated using Shannon diversity indices, showed no evidence of probe-induced bias following depletion. In silico predictions were consistent with previously reported experimental depletion results [1-3], where RiboZAP designed probes improved mRNA recovery up to ~ 75% (P < 0.01). Comprehensive downstream validation demonstrated no bias in differential gene expression (R² = 0.96), metabolic pathway profiling (ρ = ~0.92-0.95), or taxonomic composition.

In this study, we demonstrate a data-driven, in silico approach for designing additional rRNA depletion probes that perform consistently across samples of the same sample type. Probe sets designed from a subset of samples can be applied to independent samples of the same type. This approach enables estimation of rRNA depletion prior to synthesis, reducing experimental costs, and improving the efficiency of MetaT profiling from complex microbial communities.