Evidence map›Paper›PMID 30409155›Full record

ArticleBMC genomics2018

RIVET: comprehensive graphic user interface for analysis and exploration of genome-wide translatomics data.

Amanda W Ernlund, Robert J Schneider, Kelly V Ruggles

Abstract read
In one paragraph

Article in BMC genomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
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

11 citing papers in PubMed.

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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.

Amanda W ErnlundDepartment of Microbiology, New York University School of Medicine, New York, 10016, USA.
Robert J SchneiderDepartment of Microbiology, New York University School of Medicine, New York, 10016, USA. Robert.schneider@nyulangone.org.
Kelly V RugglesDepartment of Medicine, Department of Translational Medicine, New York University School of Medicine, New York, 10016, USA. Kelly.ruggles@nyulangone.org.ORCID http://orcid.org/0000-0002-0152-0863

Funding

the mTOR translational control pathway in tamoxifen resistant ER+ breast cancerR01CA207893 · NCI · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI SCHNEIDER, ROBERT JAY · 2017 to 2021
$2.1M
Translational regulation of the breast cancer stem cell by eIF4G1R01CA178509 · NCI · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI SCHNEIDER, ROBERT JAY · 2015 to 2019
$2.0M
Breast Cancer Research Foundation BCRF-16-143 (RJS)National Institutes of Health NIH RO1CA178509National Institutes of Health NIH RO1CA207893NCI NIH HHS R01 CA178509NCI NIH HHS R01 CA207893
6 · The paper itself

Abstract

backgroundTranslatomics data, particularly genome-wide ribosome profiling and polysome profiling, provide multiple levels of gene regulatory information that can be used to assess general transcription and translation, as well translational efficiency. The increasing popularity of these techniques has resulted in multiple algorithms to detect translational regulation, typically distributed in the form of command line tools that require a basic level of programming ability. Additionally, due to the static nature of current software, dynamic transcriptional and translational comparative analysis cannot be adequately achieved. In order to streamline hypothesis generation, investigators must have the ability to manipulate and interact with their data in real-time.

resultsTo address the lack of integration in current software, we introduce RIVET, Ribosomal Investigation and Visualization to Evaluate Translation, an R shiny based graphical user interface for translatomics data exploration and differential analysis. RIVET can analyze either microarray or RNA sequencing data from polysome profiling and ribosome profiling experiments. RIVET provides multiple choices for statistical analysis as well as integration of transcription, translation, and translational efficiency data analytics and the ability to visualize all results dynamically.

conclusionsRIVET is a user-friendly tool designed for bench scientists with little to no programming background. RIVET facilitates the data analysis of translatomics data allowing for dynamic generation of results based on user-defined inputs and publication ready visualization. We expect RIVET will allow for scientists to efficiently make more comprehensive data observations that will lead to more robust hypothesis regarding translational regulation.

Indexed as

Computer GraphicsGenome, HumanProtein BiosynthesisSoftwareUser-Computer InterfaceAlgorithmsComputational BiologyGene Expression ProfilingHigh-Throughput Nucleotide SequencingHumansRibosomesDifferential expressionPolysome profilingRibosome profilingR shinyTranslational efficiencyTranslational regulation

Identifiers

PMID30409155
PMCPMC6225633

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Registered trials

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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.