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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月9日

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  • Dario A. Ricciardi,
  • Franziska E. Peter,
  • Maik Böhmer

摘要

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.