Genome-wide identification and differential analysis of translational initiation. Issue 1 (December 2017)

• Record Type: Journal Article
• Authors: Zhang, Peng; He, Dandan; Xu, Yi; Hou, Jiakai; Pan, Bih-Fang; Wang, Yunfei; Liu, Tao; Davis, Christel; Ehli, Erik; Tan, Lin; Zhou, Feng; Hu, Jian; Yu, Yonghao; Chen, Xi; Nguyen, Tuan; Rosen, Jeffrey; Hawke, David; Ji, Zhe; Chen, Yiwen
• Is Part Of: Nature communications. Volume 8:Issue 1(2017)
• Page Start: 1
• Page End: 14
• Publication Date: 2017-12
• Subjects: Biology -- Periodicals; Physical sciences -- Periodicals; 505
• DOI: 10.1038/s41467-017-01981-8
• Languages: English
• ISSNs: 2041-1723

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. Translation initiation sequencing (TI-seq) has revealed unexpected diversity in protein isoforms. Here, Zhang et al. present Ribo-TISH, a computational toolkit that can detect and compare TIs across conditions and improve open reading frame prediction from different types of ribosome profiling data.