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

PLoS Comput Biol. 2017 May 25;13(5):e1005425. doi: 10.1371/journal.pcbi.1005425. eCollection 2017 May.

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.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, N.I.H., Extramural

MeSH terms

  • Biomedical Research / methods*
  • Biomedical Research / organization & administration*
  • Computational Biology*
  • High-Throughput Nucleotide Sequencing*
  • Humans
  • Research Personnel*
  • Software*