Data-driven design of LNA-blockers for efficient contaminant removal in Ribo-Seq libraries

Abstract Ribo-Seq libraries often contain highly abundant non-coding RNA contaminants, which are challenging to remove due to their high sequence variability and diverse fragmentation patterns. We present an organism-independent computational pipeline that identifies experiment-specific target sequences and enables their efficient depletion using custom-tailored LNA probes in a single pipetting step. We demonstrate that LNA-based depletion is most effective during library amplification and has no effect on gene-level quantification. Contaminant depletion in Arabidopsis libraries nearly doubled the yield of coding reads, significantly improving cost-effectiveness.