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Comparative survey of the relative impact of mRNA features on local ribosome profiling read density

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  • Patrick B. F. O’Connor

    (School of Biochemistry and Cell Biology, University College Cork)

  • Dmitry E. Andreev

    (School of Biochemistry and Cell Biology, University College Cork
    Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University)

  • Pavel V. Baranov

    (School of Biochemistry and Cell Biology, University College Cork)

Abstract

Ribosome profiling (Ribo-seq), a promising technology for exploring ribosome decoding rates, is characterized by the presence of infrequent high peaks in ribosome footprint density and by long alignment gaps. Here, to reduce the impact of data heterogeneity we introduce a simple normalization method, Ribo-seq Unit Step Transformation (RUST). RUST is robust and outperforms other normalization techniques in the presence of heterogeneous noise. We illustrate how RUST can be used for identifying mRNA sequence features that affect ribosome footprint densities globally. We show that a few parameters extracted with RUST are sufficient for predicting experimental densities with high accuracy. Importantly the application of RUST to 30 publicly available Ribo-seq data sets revealed a substantial variation in sequence determinants of ribosome footprint frequencies, questioning the reliability of Ribo-seq as an accurate representation of local ribosome densities without prior quality control. This emphasizes our incomplete understanding of how protocol parameters affect ribosome footprint densities.

Suggested Citation

  • Patrick B. F. O’Connor & Dmitry E. Andreev & Pavel V. Baranov, 2016. "Comparative survey of the relative impact of mRNA features on local ribosome profiling read density," Nature Communications, Nature, vol. 7(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:7:y:2016:i:1:d:10.1038_ncomms12915
    DOI: 10.1038/ncomms12915
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    Cited by:

    1. Chisa Shiraishi & Akinobu Matsumoto & Kazuya Ichihara & Taishi Yamamoto & Takeshi Yokoyama & Taisuke Mizoo & Atsushi Hatano & Masaki Matsumoto & Yoshikazu Tanaka & Eriko Matsuura-Suzuki & Shintaro Iwa, 2023. "RPL3L-containing ribosomes determine translation elongation dynamics required for cardiac function," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    2. Bin Shao & Jiawei Yan & Jing Zhang & Lili Liu & Ye Chen & Allen R. Buskirk, 2024. "Riboformer: a deep learning framework for predicting context-dependent translation dynamics," Nature Communications, Nature, vol. 15(1), pages 1-10, December.

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