Evidence map›Paper›PMID 33079170›Full record

ArticleGigaScience2020

A single-cell RNA-sequencing training and analysis suite using the Galaxy framework.

Mehmet Tekman, Bérénice Batut, Alexander Ostrovsky, Christophe Antoniewski, Dave Clements, Fidel Ramirez, Graham J Etherington, Hans-Rudolf Hotz, Jelle Scholtalbers, Jonathan R Manning and 13 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in GigaScience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
1.3field-weighted citation impact, top 21% 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

10 citing papers in PubMed, 20 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
  8. Galaxy Training: A powerful framework for teaching!PLoS computational biology · 2023
    Article
  9. Article
  10. Review
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

23 authors at 11 institutions in 6 countries.

Mehmet TekmanDepartment of Bioinformatics, University of Freiburg, Georges-Köhler-Allee 106, 79110 Freiburg, Germany.
Bérénice BatutDepartment of Bioinformatics, University of Freiburg, Georges-Köhler-Allee 106, 79110 Freiburg, Germany.
Alexander OstrovskyDepartment of Biology, Johns Hopkins University, Mudd Hall 144, 3400 N. Charles Street, Baltimore, MD 21218, USA.
Christophe AntoniewskiARTbio, Sorbonne Université, CNRS FR 3631, Inserm US 037, Paris, France.
Dave ClementsDepartment of Biology, Johns Hopkins University, Mudd Hall 144, 3400 N. Charles Street, Baltimore, MD 21218, USA.
Fidel RamirezBoehringer Ingelheim International GmbH, Binger Strasse 173, 55216 Ingelheim am Rhein, Biberach, Germany.
Graham J EtheringtonEarlham Institute, Norwich Research Park, Norwich NR4 7UZ, UK.
Hans-Rudolf HotzFriedrich Miescher Institute for Biomedical Research, Maulbeerstrasse 66, 4058 Basel, Switzerland.
Jelle ScholtalbersEuropean Molecular Biology Laboratory, Genome Biology Unit, Meyerhofstraße 1, 69117 Heidelberg, Germany.
Jonathan R ManningEuropean Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.
Lea BellengerARTbio, Sorbonne Université, CNRS FR 3631, Inserm US 037, Paris, France.
Maria A DoyleResearch Computing Facility, Peter MacCallum Cancer Centre, Melbourne, 305 Grattan Street, Victoria 3000, Australia.
Mohammad HeydarianDepartment of Biology, Johns Hopkins University, Mudd Hall 144, 3400 N. Charles Street, Baltimore, MD 21218, USA.
Ni HuangEuropean Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.
Nicola SoranzoEarlham Institute, Norwich Research Park, Norwich NR4 7UZ, UK.
Pablo MorenoEuropean Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.
Stefan MautnerDepartment of Bioinformatics, University of Freiburg, Georges-Köhler-Allee 106, 79110 Freiburg, Germany.
Irene PapatheodorouEuropean Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.
Anton NekrutenkoDepartment of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, PA 16802, USA.
James TaylorDepartment of Biology, Johns Hopkins University, Mudd Hall 144, 3400 N. Charles Street, Baltimore, MD 21218, USA.
Daniel BlankenbergGenomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, 9500 Euclid Avenue, NB21 Cleveland, OH 44195, USA.
Rolf BackofenDepartment of Bioinformatics, University of Freiburg, Georges-Köhler-Allee 106, 79110 Freiburg, Germany.
Björn GrüningDepartment of Bioinformatics, University of Freiburg, Georges-Köhler-Allee 106, 79110 Freiburg, Germany.
University of Freiburg · DEEuropean Bioinformatics Institute · GBJohns Hopkins University · USCentre National de la Recherche Scientifique · FRNorwich Research Park · GBBoehringer Ingelheim (Germany) · DECleveland Clinic Lerner College of Medicine · USEuropean Molecular Biology Laboratory · DEPennsylvania State University · USSIB Swiss Institute of Bioinformatics · CHThe University of Melbourne · AU

Funding

Democratization of Data Analysis in Life Sciences Through GalaxyU41HG006620 · NHGRI · PENNSYLVANIA STATE UNIVERSITY, THE · PI NEKRUTENKO, ANTON, SCHATZ, MICHAEL · 2012 to 2020
$14.4M
Biotechnology and Biological Sciences Research Council BBS/E/T/000PR9817NHGRI NIH HHS U41 HG006620
6 · The paper itself

Abstract

backgroundThe vast ecosystem of single-cell RNA-sequencing tools has until recently been plagued by an excess of diverging analysis strategies, inconsistent file formats, and compatibility issues between different software suites. The uptake of 10x Genomics datasets has begun to calm this diversity, and the bioinformatics community leans once more towards the large computing requirements and the statistically driven methods needed to process and understand these ever-growing datasets.

resultsHere we outline several Galaxy workflows and learning resources for single-cell RNA-sequencing, with the aim of providing a comprehensive analysis environment paired with a thorough user learning experience that bridges the knowledge gap between the computational methods and the underlying cell biology. The Galaxy reproducible bioinformatics framework provides tools, workflows, and trainings that not only enable users to perform 1-click 10x preprocessing but also empower them to demultiplex raw sequencing from custom tagged and full-length sequencing protocols. The downstream analysis supports a range of high-quality interoperable suites separated into common stages of analysis: inspection, filtering, normalization, confounder removal, and clustering. The teaching resources cover concepts from computer science to cell biology. Access to all resources is provided at the singlecell.usegalaxy.eu portal.

conclusionsThe reproducible and training-oriented Galaxy framework provides a sustainable high-performance computing environment for users to run flexible analyses on both 10x and alternative platforms. The tutorials from the Galaxy Training Network along with the frequent training workshops hosted by the Galaxy community provide a means for users to learn, publish, and teach single-cell RNA-sequencing analysis.

Indexed as

EcosystemSoftwareComputational BiologyRNASequence Analysis, RNARNA10xGalaxyhigh-performance computingresourcesscRNAsingle-celltrainingWeb

Identifiers

PMID33079170
PMCPMC7574357
OpenAlexW3093068485

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.