Evidence map›Paper›PMID 32738892›Full record

ArticleBMC bioinformatics2020

RiboMiner: a toolset for mining multi-dimensional features of the translatome with ribosome profiling data.

Fajin Li, Xudong Xing, Zhengtao Xiao, Gang Xu, Xuerui Yang

Abstract read
In one paragraph

Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing 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

20 citing papers in PubMed.

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  19. Pan-cancer analyses of synonymous mutations based on tissue-specific codon optimality.Computational and structural biotechnology journal · 2022
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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

5 authors.

Fajin LiMOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Medical Science Building D231, Beijing, 100084, China.
Xudong XingMOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Medical Science Building D231, Beijing, 100084, China.
Zhengtao XiaoMOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Medical Science Building D231, Beijing, 100084, China.
Gang XuMOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Medical Science Building D231, Beijing, 100084, China.
Xuerui YangMOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Medical Science Building D231, Beijing, 100084, China. yangxuerui@tsinghua.edu.cn.ORCID http://orcid.org/0000-0002-7731-2147

Funding

National key research and development program, Precision Medicine Project 2016YFC0906001the National Natural Science Foundation of China 81972912 and 31671381the Tsinghua University Initiative Scientific Research Program 2019Z06QCX01
6 · The paper itself

Abstract

backgroundRibosome profiling has been widely used for studies of translation under a large variety of cellular and physiological contexts. Many of these studies have greatly benefitted from a series of data-mining tools designed for dissection of the translatome from different aspects. However, as the studies of translation advance quickly, the current toolbox still falls in short, and more specialized tools are in urgent need for deeper and more efficient mining of the important and new features of the translation landscapes.

resultsHere, we present RiboMiner, a bioinformatics toolset for mining of multi-dimensional features of the translatome with ribosome profiling data. RiboMiner performs extensive quality assessment of the data and integrates a spectrum of tools for various metagene analyses of the ribosome footprints and for detailed analyses of multiple features related to translation regulation. Visualizations of all the results are available. Many of these analyses have not been provided by previous methods. RiboMiner is highly flexible, as the pipeline could be easily adapted and customized for different scopes and targets of the studies.

conclusionsApplications of RiboMiner on two published datasets did not only reproduced the main results reported before, but also generated novel insights into the translation regulation processes. Therefore, being complementary to the current tools, RiboMiner could be a valuable resource for dissections of the translation landscapes and the translation regulations by mining the ribosome profiling data more comprehensively and with higher resolution. RiboMiner is freely available at https://github.com/xryanglab/RiboMiner and https://pypi.org/project/RiboMiner .

Indexed as

Protein BiosynthesisSoftwareAmino Acid MotifsAmino AcidsAmino Acid SequenceCodonComputational BiologyData AnalysisData MiningRibosomesAmino AcidsCodon

Identifiers

PMID32738892
PMCPMC7430821

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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.