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Genome-wide identification and differential analysis of translational initiation

Author

Listed:
  • Peng Zhang

    (The University of Texas MD Anderson Cancer Center)

  • Dandan He

    (The University of Texas MD Anderson Cancer Center)

  • Yi Xu

    (The University of Texas MD Anderson Cancer Center)

  • Jiakai Hou

    (The University of Texas MD Anderson Cancer Center)

  • Bih-Fang Pan

    (The University of Texas MD Anderson Cancer Center)

  • Yunfei Wang

    (The University of Texas MD Anderson Cancer Center)

  • Tao Liu

    (State University of New York at Buffalo)

  • Christel M. Davis

    (Avera Institute for Human Genetics)

  • Erik A. Ehli

    (Avera Institute for Human Genetics)

  • Lin Tan

    (The University of Texas MD Anderson Cancer Center)

  • Feng Zhou

    (Fudan University)

  • Jian Hu

    (The University of Texas MD Anderson Cancer Center)

  • Yonghao Yu

    (The University of Texas Southwestern Medical Center)

  • Xi Chen

    (Baylor College of Medicine)

  • Tuan M. Nguyen

    (Baylor College of Medicine
    Baylor College of Medicine)

  • Jeffrey M. Rosen

    (Baylor College of Medicine)

  • David H. Hawke

    (The University of Texas MD Anderson Cancer Center)

  • Zhe Ji

    (Harvard Medical School
    Broad Institute of MIT and Harvard)

  • Yiwen Chen

    (The University of Texas MD Anderson Cancer Center)

Abstract

Translation is principally regulated at the initiation stage. The development of the translation initiation (TI) sequencing (TI-seq) technique has enabled the global mapping of TIs and revealed unanticipated complex translational landscapes in metazoans. Despite the wide adoption of TI-seq, there is no computational tool currently available for analyzing TI-seq data. To fill this gap, we develop a comprehensive toolkit named Ribo-TISH, which allows for detecting and quantitatively comparing TIs across conditions from TI-seq data. Ribo-TISH can also predict novel open reading frames (ORFs) from regular ribosome profiling (rRibo-seq) data and outperform several established methods in both computational efficiency and prediction accuracy. Applied to published TI-seq/rRibo-seq data sets, Ribo-TISH uncovers a novel signature of elevated mitochondrial translation during amino-acid deprivation and predicts novel ORFs in 5′UTRs, long noncoding RNAs, and introns. These successful applications demonstrate the power of Ribo-TISH in extracting biological insights from TI-seq/rRibo-seq data.

Suggested Citation

  • Peng Zhang & Dandan He & Yi Xu & Jiakai Hou & Bih-Fang Pan & Yunfei Wang & Tao Liu & Christel M. Davis & Erik A. Ehli & Lin Tan & Feng Zhou & Jian Hu & Yonghao Yu & Xi Chen & Tuan M. Nguyen & Jeffrey , 2017. "Genome-wide identification and differential analysis of translational initiation," Nature Communications, Nature, vol. 8(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_s41467-017-01981-8
    DOI: 10.1038/s41467-017-01981-8
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    Cited by:

    1. Haiwang Yang & Qianru Li & Emily K. Stroup & Sheng Wang & Zhe Ji, 2024. "Widespread stable noncanonical peptides identified by integrated analyses of ribosome profiling and ORF features," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    2. Alla D. Fedorova & Stephen J. Kiniry & Dmitry E. Andreev & Jonathan M. Mudge & Pavel V. Baranov, 2022. "Thousands of human non-AUG extended proteoforms lack evidence of evolutionary selection among mammals," Nature Communications, Nature, vol. 13(1), pages 1-13, December.

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