Genome-wide identification and differential analysis of translational initiation

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 M. Rosen, David H. Hawke, Zhe Ji and Yiwen Chen ()
Additional contact information
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
Jeffrey M. Rosen: Baylor College of Medicine
David H. Hawke: The University of Texas MD Anderson Cancer Center
Zhe Ji: Harvard Medical School
Yiwen Chen: The University of Texas MD Anderson Cancer Center

Nature Communications, 2017, vol. 8, issue 1, 1-14

Abstract: 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.

Date: 2017
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DOI: 10.1038/s41467-017-01981-8

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