ArticleScientific reports2026
Data-driven design of LNA-blockers for efficient contaminant removal in Ribo-Seq libraries.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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3 authors.
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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.
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Registered trials
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