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

TL;DR

This paper introduces a computational method to efficiently remove non-coding RNA contaminants in Ribo-Seq libraries using custom LNA probes, improving the yield of coding reads.

Contribution

The novel contribution is an organism-independent pipeline for designing LNA probes that efficiently deplete contaminants during library amplification.

Findings

LNA-based depletion is most effective during library amplification and does not affect gene-level quantification.

Contaminant depletion in Arabidopsis libraries nearly doubled the yield of coding reads.

The method improves the cost-effectiveness of Ribo-Seq library preparation.

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.

The online version contains supplementary material available at 10.1038/s41598-026-43117-3.

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