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RiboZAP: a species-agnostic pipeline for rRNA depletion probe design in metatranscriptomics.
Samuel Bunga1, Asako Tan1, Morgan Roos1
1Illumina, Inc, San Diego, CA, USA.
BMC Bioinformatics
|July 10, 2026
Summary
RiboZAP is a new computational tool that designs custom probes to remove ribosomal RNA (rRNA) for better metatranscriptomic (MetaT) sequencing. This species-agnostic approach improves mRNA recovery and reduces costs for microbial community analysis.
Area of Science:
- Microbiology
- Bioinformatics
- Genomics
Background:
- Metatranscriptomic (MetaT) sequencing reveals microbial gene expression but is hindered by high ribosomal RNA (rRNA) abundance.
- Existing rRNA depletion kits are often microbiome-specific and underperform on diverse sample types.
- Designing rRNA removal probes based on taxonomy can be costly and complex.
Purpose of the Study:
- To develop a species-agnostic computational pipeline (RiboZAP) for designing custom rRNA depletion probes.
- To enable efficient rRNA depletion from metatranscriptomic data without prior sample composition knowledge.
- To improve mRNA recovery and sequencing efficiency in complex microbial communities.
Main Methods:
- Developed RiboZAP, a computational pipeline for designing RNase H depletion probes directly from MetaT sequencing data.
- Utilized a species-agnostic approach, requiring no prior knowledge of sample composition.
- Validated probe performance in silico and experimentally on mouse cecal samples.
Main Results:
- RiboZAP probes achieved 43-62% predicted rRNA depletion in mouse cecal samples.
- Depletion performance was consistent across design and independent samples.
- No significant bias was observed in downstream analyses, including gene expression, metabolic pathways, and taxonomic composition.
- RiboZAP improved mRNA recovery by up to 75% compared to existing methods.
Conclusions:
- RiboZAP offers a data-driven, in silico method for designing effective rRNA depletion probes.
- Probe sets designed from a subset of samples are applicable to independent samples of the same type.
- This approach reduces experimental costs and enhances MetaT profiling efficiency.
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