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DescriptionRibo-seq data-driven Translation Initiation Sites Hunter
Development Information
GitHub zhpn1024/ribotish
Current versionv0.1.9
Last updated2017/09/28
Newsv0.1.9 released
Citation Zhang, P., Danden, H., et al., Genome-wide identification and differential analysis of translational initiation, Nature Communications 2017 
Help and Support
Contact Yiwen Chen 
Discussion On GitHub 


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


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).


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


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


This software is distributed under the terms of GNU General Public License .


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