Ribo-TISH

Ribo-TISH: Ribo-seq data-driven Translation Initiation Sites Hunter

About

Translation is a critical step in gene regulation that synthesizes proteins from a given RNA template. The development of the ribosome profiling (ribo-seq) technique has enabled the measurement of translation at a genome-wide level. The basic idea of ribosome profiling is to perform deep sequencing of the ribosome-protected mRNA fragment (~30 nts), termed ribosome footprints (RPFs), to determine the occupancy of translating ribosomes on a given mRNA. There are several variants of the ribosome profiling technique that are based on the use of different translation inhibitors. The regular ribo-seq utilizes cycloheximide (CHX), a translation elongation inhibitor to freeze all translating ribosomes. In contrast to CHX, the translation inhibitor lactimidomycin (LTM) and harringtonine (Harr) have a much stronger effect on initiating ribosomes. The use of these two inhibitors allows for the global mapping of translating initiating sites (TISs) when they are coupled with ribosome profiling (TI-Seq). In addition, when LTM is used sequentially with puromycin (PMY), the TISs can be mapped quantitatively and can be compared between different conditions.

Despite broad applicability and wide adoption of the TI-seq technique, it remains challenging to differentiate signal from noise and extract useful information from TI-seq data. We develop a statistically principled and computationally efficient toolkit named Ribo-TISH (ribo-seq data-driven TIS hunter). Ribo-TISH is the first comprehensive informatics solution to the analysis of TI-seq data, starting from quality control of the aligned sequencing data to identifying and quantitatively comparing the difference in genome-wide translational initiations under different conditions. In addition to the analysis of TI-seq data, Ribo-TISH enables efficient de novo prediction of novel open reading frames (ORFs) with either AUG or near-cognate start codons from regular ribo-seq (rRibo-seq) data. Finally, Ribo-TISH allows for statistically integrating TI-seq and rRibo-seq data when both types of data are available. The application of Ribo-TISH to published TI-seq and rRibo-seq dataset has uncovered a novel signature of elevated mitochondrial translation during amino acid deprivation in human cells, as well as predicted novel ORFs in 5’ UTRs, long non-coding RNAs and introns (see Citation 1 for details).

Author

Ribo-TISH is written by Peng Zhang from Dr. Yiwen Chen’s Lab from the Department of Bioinformatics & Computational Biology at The University of Texas MD Anderson Cancer Center.

Source Code

On GitHub (opens in new tab)

Citation

Please cite the following publication for Ribo-TISH. 1. Zhang et al. Genome-wide identification and differential analysis of translational initiation. Nature Communications (2017)

License

This software is distributed under the terms of GNU General Public License (opens in new tab).

Contact

If you have any comments, suggestions, questions, bug reports, etc, feel free to contact: zhpn1024@163.com (cc’ed YChen26@mdanderson.org) and PLEASE attach your command line and log messages if possible. If you think your question/comment may be interesting to the Ribo-TISH user group, please post it to our google group (opens in new tab).