Evidence map›Paper›PMID 31989542›Full record

ArticleJournal of microbiology (Seoul, Korea)2020

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

Donghui Choe, Bernhard Palsson, Byung-Kwan Cho

Open access · hybridAbstract read
In one paragraph

Article in Journal of microbiology (Seoul, Korea), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
0.8field-weighted citation impact, top 31% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 10 citations in OpenAlex.

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  7. User guides for biologists to learn computational methods.Journal of microbiology (Seoul, Korea) · 2020
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 2 institutions in 2 countries.

Donghui ChoeDepartment of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea.
Bernhard PalssonDepartment of Bioengineering, University of California San Diego, La Jolla, CA, 92093, USA.
Byung-Kwan ChoDepartment of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea. bcho@kaist.ac.kr.
Korea Advanced Institute of Science and Technology · KRUniversity of California, San Diego · US

Funding

Systems Biology Approach to Redefine Susceptibility Testing and Treatment of MDR Pathogens in the Context of Host ImmunityU01AI124316 · NIAID · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI PALSSON, BERNHARD O · 2016 to 2020
$10.2M
Model-guided Identification of Synthetic Lethal Genes for Drug Target DevelopmentR01GM098105 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI PALSSON, BERNHARD O · 2011 to 2013
$1.8M
NIAID NIH HHS U01 AI124316NIGMS NIH HHS R01 GM098105
6 · The paper itself

Abstract

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.

Indexed as

SoftwareBacteriaData AnalysisProgrammed Instructions as TopicRibosomesRNA, BacterialRNA, MessengerRNA, RibosomalRNA-SeqRNA, BacterialRNA, MessengerRNA, RibosomalNGS analysisRibo-Seqribosome profiling

Identifiers

PMID31989542
PMCPMC7209825
OpenAlexW3004308898

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.