Literature DB >> 20238087

Web-based analysis of (Epi-) genome data using EpiGRAPH and Galaxy.

Christoph Bock1, Greg Von Kuster, Konstantin Halachev, James Taylor, Anton Nekrutenko, Thomas Lengauer.   

Abstract

Modern life sciences are becoming increasingly data intensive, posing a significant challenge for most researchers and shifting the bottleneck of scientific discovery from data generation to data analysis. As a result, progress in genome research is increasingly impeded by bioinformatic hurdles. A new generation of powerful and easy-to-use genome analysis tools has been developed to address this issue, enabling biologists to perform complex bioinformatic analyses online - without having to learn a programming language or downloading and manually processing large datasets. In this tutorial paper, we describe the use of EpiGRAPH (http://epigraph.mpi-inf.mpg.de/) and Galaxy (http://galaxyproject.org/) for genome and epigenome analysis, and we illustrate how these two web services work together to identify epigenetic modifications that are characteristics of highly polymorphic (SNP-rich) promoters. This paper is supplemented with video tutorials (http://tinyurl.com/yc5xkqq), which provide a step-by-step guide through each example analysis.

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Year:  2010        PMID: 20238087      PMCID: PMC6529944          DOI: 10.1007/978-1-60327-367-1_15

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  5 in total

Review 1.  Next generation sequencing based approaches to epigenomics.

Authors:  Martin Hirst; Marco A Marra
Journal:  Brief Funct Genomics       Date:  2010-12       Impact factor: 4.241

2.  Integrating diverse databases into an unified analysis framework: a Galaxy approach.

Authors:  Daniel Blankenberg; Nathan Coraor; Gregory Von Kuster; James Taylor; Anton Nekrutenko
Journal:  Database (Oxford)       Date:  2011-04-29       Impact factor: 3.451

3.  Exploratory analysis of genomic segmentations with Segtools.

Authors:  Orion J Buske; Michael M Hoffman; Nadia Ponts; Karine G Le Roch; William Stafford Noble
Journal:  BMC Bioinformatics       Date:  2011-10-26       Impact factor: 3.307

4.  Gene expression and nucleotide composition are associated with genic methylation level in Oryza sativa.

Authors:  Eran Elhaik; Matteo Pellegrini; Tatiana V Tatarinova
Journal:  BMC Bioinformatics       Date:  2014-01-21       Impact factor: 3.169

5.  Putting epigenome comparison into practice.

Authors:  Aleksandar Milosavljevic
Journal:  Nat Biotechnol       Date:  2010-10       Impact factor: 54.908

  5 in total

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