Evidence map›Paper›PMID 31504201›Full record

ArticleBioinformatics (Oxford, England)2020

Unification of miRNA and isomiR research: the mirGFF3 format and the mirtop API.

Thomas Desvignes, Phillipe Loher, Karen Eilbeck, Jeffery Ma, Gianvito Urgese, Bastian Fromm, Jason Sydes, Ernesto Aparicio-Puerta, Victor Barrera, Roderic Espín and 12 more

Open access · greenAbstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.

0numbers the graph read from it
0cells of the map it votes in
34citing papers in PubMed
3.1field-weighted citation impact, top 7% 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

34 citing papers in PubMed, 57 citations in OpenAlex.

  1. Article
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  4. IsomiR stoichiometry changes as disease biomarkers.Molecular therapy. Nucleic acids · 2025
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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

22 authors at 12 institutions in 5 countries.

Thomas DesvignesInstitute of Neuroscience, University of Oregon, Eugene, OR 97403, USA.
Phillipe LoherComputational Medicine Center, Thomas Jefferson University, Philadelphia, PA 19144, USA.
Karen EilbeckUniversity of Utah, Biomedical Informatics, Salt Lake City, UT 84108, USA.
Jeffery MaComputational Medicine Center, Thomas Jefferson University, Philadelphia, PA 19144, USA.
Gianvito UrgeseDepartment of Control and Computer Engineering, Politecnico di Torino, Torino 10129, Italy.
Bastian FrommScience for Life Laboratory, Department of Molecular Biosciences, The Wenner-Gren Institute, Stockholm University, Stockholm 114 18, Sweden.
Jason SydesInstitute of Neuroscience, University of Oregon, Eugene, OR 97403, USA.
Ernesto Aparicio-PuertaComputational Epigenomics Laboratory, Genetics Department and Biotechnology Institute and Biosanitary Institute, University of Granada, Granada 18002, Spain.
Victor BarreraDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
Roderic EspínUniversitat Oberta de Catalunya, Barcelona 08018, Spain.
Florian ThibordSorbonne Université, Pierre Louis Doctoral School of Public Health, Paris 75006, France.
Xavier Bofill-De RosRNA Biology Laboratory, Center for Cancer Research, National Cancer Institute, Frederick, MD 21702, USA.
Eric LondinComputational Medicine Center, Thomas Jefferson University, Philadelphia, PA 19144, USA.
Aristeidis G TelonisComputational Medicine Center, Thomas Jefferson University, Philadelphia, PA 19144, USA.
Elisa FicarraDepartment of Control and Computer Engineering, Politecnico di Torino, Torino 10129, Italy.
Marc R FriedländerScience for Life Laboratory, Department of Molecular Biosciences, The Wenner-Gren Institute, Stockholm University, Stockholm 114 18, Sweden.
John H PostlethwaitInstitute of Neuroscience, University of Oregon, Eugene, OR 97403, USA.
Isidore RigoutsosComputational Medicine Center, Thomas Jefferson University, Philadelphia, PA 19144, USA.
Michael HackenbergComputational Epigenomics Laboratory, Genetics Department and Biotechnology Institute and Biosanitary Institute, University of Granada, Granada 18002, Spain.
Ioannis S VlachosNon-coding Research Lab, Department of Pathology, Cancer Research Institute, Harvard Medical School Initiative for RNA Medicine, Beth Israel Deaconess Medical Center, Boston, MA 02115, USA.
Marc K HalushkaDepartment of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
Lorena PantanoBioinformatics Core, The Picower Institute for Learning and Memory, Cambridge, MA 02139, USA.
Thomas Jefferson University · USUniversity of Oregon · USPolitecnico di Torino · ITStockholm University · SEUniversidad de Granada · ESBeth Israel Deaconess Medical Center · USHarvard University · USJohns Hopkins University · USNational Cancer Institute · USUniversitat Oberta de Catalunya · ESUniversité de Bordeaux · FRUniversity of Utah · US

Funding

Fine Dissection of Atherosclerosis Microenvironment RNA ExpressionR01HL137811 · NHLBI · JOHNS HOPKINS UNIVERSITY · PI HALUSHKA, MARC K · 2017 to 2020
$2.4M
xMD-miRNA-Seq estimates cell-specific miRNA expressionR01GM130564 · NIGMS · JOHNS HOPKINS UNIVERSITY · PI HALUSHKA, MARC K · 2018 to 2021
$1.2M
NHLBI NIH HHS R01 HL137811NIGMS NIH HHS R01 GM130564
6 · The paper itself

Abstract

motivationMicroRNAs (miRNAs) are small RNA molecules (∼22 nucleotide long) involved in post-transcriptional gene regulation. Advances in high-throughput sequencing technologies led to the discovery of isomiRs, which are miRNA sequence variants. While many miRNA-seq analysis tools exist, the diversity of output formats hinders accurate comparisons between tools and precludes data sharing and the development of common downstream analysis methods.

resultsTo overcome this situation, we present here a community-based project, miRNA Transcriptomic Open Project (miRTOP) working towards the optimization of miRNA analyses. The aim of miRTOP is to promote the development of downstream isomiR analysis tools that are compatible with existing detection and quantification tools. Based on the existing GFF3 format, we first created a new standard format, mirGFF3, for the output of miRNA/isomiR detection and quantification results from small RNA-seq data. Additionally, we developed a command line Python tool, mirtop, to create and manage the mirGFF3 format. Currently, mirtop can convert into mirGFF3 the outputs of commonly used pipelines, such as seqbuster, isomiR-SEA, sRNAbench, Prost! as well as BAM files. Some tools have also incorporated the mirGFF3 format directly into their code, such as, miRge2.0, IsoMIRmap and OptimiR. Its open architecture enables any tool or pipeline to output or convert results into mirGFF3. Collectively, this isomiR categorization system, along with the accompanying mirGFF3 and mirtop API, provide a comprehensive solution for the standardization of miRNA and isomiR annotation, enabling data sharing, reporting, comparative analyses and benchmarking, while promoting the development of common miRNA methods focusing on downstream steps of miRNA detection, annotation and quantification. AVAILABILITY AND IMPLEMENTATION: https://github.com/miRTop/mirGFF3/ and https://github.com/miRTop/mirtop. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Indexed as

MicroRNAsGene Expression RegulationHigh-Throughput Nucleotide SequencingSequence Analysis, RNATranscriptomeMicroRNAs

Identifiers

PMID31504201
PMCPMC7566869
OpenAlexW2970503289

What OpenQuestion holds

Textmetadata
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.