Jupyter and Galaxy: Easing entry barriers into complex data analyses for biomedical researchers

Björn A. Grüning; Eric Rasche; Boris Rebolledo-Jaramillo; Carl Eberhard; Torsten Houwaart; John Chilton; Nate Coraor; Rolf Backofen; James Taylor; Anton Nekrutenko

doi:10.1371/journal.pcbi.1005425

Jupyter and Galaxy: Easing entry barriers into complex data analyses for biomedical researchers

Björn A. Grüning, Eric Rasche, Boris Rebolledo-Jaramillo, Carl Eberhard, Torsten Houwaart, John Chilton, Nate Coraor, Rolf Backofen, James Taylor, Anton Nekrutenko

Research output: Contribution to journal › Article › peer-review

41 Scopus citations

Abstract

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.

Original language	English (US)
Article number	e1005425
Journal	PLoS computational biology
Volume	13
Issue number	5
DOIs	https://doi.org/10.1371/journal.pcbi.1005425
State	Published - May 2017

All Science Journal Classification (ASJC) codes

Ecology, Evolution, Behavior and Systematics
Modeling and Simulation
Ecology
Molecular Biology
Genetics
Cellular and Molecular Neuroscience
Computational Theory and Mathematics

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10.1371/journal.pcbi.1005425

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AU - Chilton, John

AU - Coraor, Nate

AU - Backofen, Rolf

AU - Taylor, James

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Jupyter and Galaxy: Easing entry barriers into complex data analyses for biomedical researchers

Abstract

All Science Journal Classification (ASJC) codes

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