Jupyter and Galaxy: Easing entry barriers into complex data analyses for biomedical researchers
What does it take to convert a heap of sequencing data into a publishable result? First, common tools are employed to reduce primary data (sequencing reads) to a form suitable for further analyses (i.e., the list of variable sites). The subsequent exploratory stage is much more ad hoc and requires t...
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| Veröffentlicht in: | PLoS computational biology 2017-05, Vol.13 (5), p.e1005425-e1005425 |
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| container_title | PLoS computational biology |
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| creator | Grüning, Björn A Rasche, Eric Rebolledo-Jaramillo, Boris Eberhard, Carl Houwaart, Torsten Chilton, John Coraor, Nate Backofen, Rolf Taylor, James Nekrutenko, Anton |
| description | What does it take to convert a heap of sequencing data into a publishable result? First, common tools are employed to reduce primary data (sequencing reads) to a form suitable for further analyses (i.e., the list of variable sites). The subsequent exploratory stage is much more ad hoc and requires the development of custom scripts and pipelines, making it problematic for biomedical researchers. Here, we describe a hybrid platform combining common analysis pathways with the ability to explore data interactively. It aims to fully encompass and simplify the "raw data-to-publication" pathway and make it reproducible. |
| doi_str_mv | 10.1371/journal.pcbi.1005425 |
| format | Article |
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First, common tools are employed to reduce primary data (sequencing reads) to a form suitable for further analyses (i.e., the list of variable sites). The subsequent exploratory stage is much more ad hoc and requires the development of custom scripts and pipelines, making it problematic for biomedical researchers. Here, we describe a hybrid platform combining common analysis pathways with the ability to explore data interactively. It aims to fully encompass and simplify the "raw data-to-publication" pathway and make it reproducible.</description><identifier>ISSN: 1553-7358</identifier><identifier>ISSN: 1553-734X</identifier><identifier>EISSN: 1553-7358</identifier><identifier>DOI: 10.1371/journal.pcbi.1005425</identifier><identifier>PMID: 28542180</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Automation ; Biochemistry ; Bioinformatics ; Biology and Life Sciences ; Biomedical Research - methods ; Biomedical Research - organization & administration ; Collaboration ; Computational Biology ; Computer and Information Sciences ; Computer science ; Data analysis ; Data processing ; Deoxyribonucleic acid ; DNA ; Education ; Experiments ; Galaxies ; Genomes ; High-Throughput Nucleotide Sequencing ; Humans ; Life sciences ; Medical research ; Methods ; Mitochondrial DNA ; Molecular biology ; Physical Sciences ; Pipelines ; Quality control ; Reproducibility ; Research and Analysis Methods ; Research Personnel ; Researchers ; River networks ; Scripts ; Software ; Studies ; Systems analysis ; Technology application</subject><ispartof>PLoS computational biology, 2017-05, Vol.13 (5), p.e1005425-e1005425</ispartof><rights>COPYRIGHT 2017 Public Library of Science</rights><rights>2017 Public Library of Science. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: Grüning BA, Rasche E, Rebolledo-Jaramillo B, Eberhard C, Houwaart T, Chilton J, et al. (2017) Jupyter and Galaxy: Easing entry barriers into complex data analyses for biomedical researchers. PLoS Comput Biol 13(5): e1005425. https://doi.org/10.1371/journal.pcbi.1005425</rights><rights>2017 Grüning et al 2017 Grüning et al</rights><rights>2017 Public Library of Science. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: Grüning BA, Rasche E, Rebolledo-Jaramillo B, Eberhard C, Houwaart T, Chilton J, et al. (2017) Jupyter and Galaxy: Easing entry barriers into complex data analyses for biomedical researchers. 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First, common tools are employed to reduce primary data (sequencing reads) to a form suitable for further analyses (i.e., the list of variable sites). The subsequent exploratory stage is much more ad hoc and requires the development of custom scripts and pipelines, making it problematic for biomedical researchers. Here, we describe a hybrid platform combining common analysis pathways with the ability to explore data interactively. 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| source | MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central; Public Library of Science (PLoS) |
| subjects | Automation Biochemistry Bioinformatics Biology and Life Sciences Biomedical Research - methods Biomedical Research - organization & administration Collaboration Computational Biology Computer and Information Sciences Computer science Data analysis Data processing Deoxyribonucleic acid DNA Education Experiments Galaxies Genomes High-Throughput Nucleotide Sequencing Humans Life sciences Medical research Methods Mitochondrial DNA Molecular biology Physical Sciences Pipelines Quality control Reproducibility Research and Analysis Methods Research Personnel Researchers River networks Scripts Software Studies Systems analysis Technology application |
| title | Jupyter and Galaxy: Easing entry barriers into complex data analyses for biomedical researchers |
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