Literature DB >> 33583104

Using Galaxy to Perform Large-Scale Interactive Data Analyses-An Update.

Alexander Ostrovsky1, Jennifer Hillman-Jackson2, Dave Bouvier2, Dave Clements1, Enis Afgan1, Daniel Blankenberg3, Michael C Schatz1, Anton Nekrutenko2, James Taylor1, The Galaxy Team1,2,3,4, Delphine Lariviere2.   

Abstract

Modern biology continues to become increasingly computational. Datasets are becoming progressively larger, more complex, and more abundant. The computational savviness necessary to analyze these data creates an ongoing obstacle for experimental biologists. Galaxy (galaxyproject.org) provides access to computational biology tools in a web-based interface. It also provides access to major public biological data repositories, allowing private data to be combined with public datasets. Galaxy is hosted on high-capacity servers worldwide and is accessible for free, with an option to be installed locally. This article demonstrates how to employ Galaxy to perform biologically relevant analyses on publicly available datasets. These protocols use both standard and custom tools, serving as a tutorial and jumping-off point for more intensive and/or more specific analyses using Galaxy.
© 2021 Wiley Periodicals LLC. Basic Protocol 1: Finding human coding exons with highest SNP density Basic Protocol 2: Calling peaks for ChIP-seq data Basic Protocol 3: Compare datasets using genomic coordinates Basic Protocol 4: Working with multiple alignments Basic Protocol 5: Single cell RNA-seq. © 2021 Wiley Periodicals LLC.

Entities:  

Keywords:  Galaxy; computational biology; web application

Mesh:

Year:  2021        PMID: 33583104     DOI: 10.1002/cpz1.31

Source DB:  PubMed          Journal:  Curr Protoc        ISSN: 2691-1299


  3 in total

1.  Omics Analyses: How to Navigate Through a Constant Data Deluge.

Authors:  Thomas Denecker; Gaëlle Lelandais
Journal:  Methods Mol Biol       Date:  2022

2.  SARS-CoV-2 and human retroelements: a case for molecular mimicry?

Authors:  Benjamin Florian Koch
Journal:  BMC Genom Data       Date:  2022-04-08

Review 3.  A Detailed Catalogue of Multi-Omics Methodologies for Identification of Putative Biomarkers and Causal Molecular Networks in Translational Cancer Research.

Authors:  Efstathios Iason Vlachavas; Jonas Bohn; Frank Ückert; Sylvia Nürnberg
Journal:  Int J Mol Sci       Date:  2021-03-10       Impact factor: 5.923

  3 in total

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