STATR: A simple analysis pipeline of Ribo-Seq in bacteria

Journal of Microbiology - Tập 58 - Trang 217-226 - 2020
Donghui Choe1, Bernhard Palsson2,3, Byung-Kwan Cho1,4,5
1Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
2Department of Bioengineering, University of California San Diego, La Jolla, USA
3Department of Pediatrics, University of California , San Diego, La Jolla, USA.
4KI for the BioCentury, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
5Intelligent Synthetic Biology Center, Daejeon, Republic of Korea

Tóm tắt

Gene expression changes in response to diverse environmental stimuli to regulate numerous cellular functions. Genes are expressed into their functional products with the help of messenger RNA (mRNA). Thus, measuring levels of mRNA in cells is important to understand cellular functions. With advances in next-generation sequencing (NGS), the abundance of cellular mRNA has been elucidated via transcriptome sequencing. However, several studies have found a discrepancy between mRNA abundance and protein levels induced by translational regulation, including different rates of ribosome entry and translational pausing. As such, the levels of mRNA are not necessarily a direct representation of the protein levels found in a cell. To determine a more precise way to measure protein expression in cells, the analysis of the levels of mRNA associated with ribosomes is being adopted. With an aid of NGS techniques, a single nucleotide resolution footprint of the ribosome was determined using a method known as Ribo-Seq or ribosome profiling. This method allows for the high-throughput measurement of translation in vivo, which was further analyzed to determine the protein synthesis rate, translational pausing, and cellular responses toward a variety of environmental changes. Here, we describe a simple analysis pipeline for Ribo-Seq in bacteria, so-called simple translatome analysis tool for Ribo-Seq (STATR). STATR can be used to carry out the primary processing of Ribo-Seq data, subsequently allowing for multiple levels of translatome study, from experimental validation to in-depth analyses. A command-by-command explanation is provided here to allow a broad spectrum of biologists to easily reproduce the analysis.

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