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      deepTools2: a next generation web server for deep-sequencing data analysis

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          Abstract

          We present an update to our Galaxy-based web server for processing and visualizing deeply sequenced data. Its core tool set, deepTools, allows users to perform complete bioinformatic workflows ranging from quality controls and normalizations of aligned reads to integrative analyses, including clustering and visualization approaches. Since we first described our deepTools Galaxy server in 2014, we have implemented new solutions for many requests from the community and our users. Here, we introduce significant enhancements and new tools to further improve data visualization and interpretation. deepTools continue to be open to all users and freely available as a web service at deeptools.ie-freiburg.mpg.de. The new deepTools2 suite can be easily deployed within any Galaxy framework via the toolshed repository, and we also provide source code for command line usage under Linux and Mac OS X. A public and documented API for access to deepTools functionality is also available.

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          Identifying ChIP-seq enrichment using MACS.

          Jianxing Feng, Tao Liu, Bo Qin … (2012)
          Model-based analysis of ChIP-seq (MACS) is a computational algorithm that identifies genome-wide locations of transcription/chromatin factor binding or histone modification from ChIP-seq data. MACS consists of four steps: removing redundant reads, adjusting read position, calculating peak enrichment and estimating the empirical false discovery rate (FDR). In this protocol, we provide a detailed demonstration of how to install MACS and how to use it to analyze three common types of ChIP-seq data sets with different characteristics: the sequence-specific transcription factor FoxA1, the histone modification mark H3K4me3 with sharp enrichment and the H3K36me3 mark with broad enrichment. We also explain how to interpret and visualize the results of MACS analyses. The algorithm requires ∼3 GB of RAM and 1.5 h of computing time to analyze a ChIP-seq data set containing 30 million reads, an estimate that increases with sequence coverage. MACS is open source and is available from http://liulab.dfci.harvard.edu/MACS/.
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            Genomation: a toolkit to summarize, annotate and visualize genomic intervals.

            Altuna Akalin, Vedran Franke, Kristian Vlahoviček … (2015)
            Biological insights can be obtained through computational integration of genomics data sets consisting of diverse types of information. The integration is often hampered by a large variety of existing file formats, often containing similar information, and the necessity to use complicated tools to achieve the desired results. We have built an R package, genomation, to expedite the extraction of biological information from high throughput data. The package works with a variety of genomic interval file types and enables easy summarization and annotation of high throughput data sets with given genomic annotations.
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              Dissemination of scientific software with Galaxy ToolShed

              Daniel Blankenberg, Gregory Von Kuster, Emil Bouvier … (2014)
              The proliferation of web-based integrative analysis frameworks has enabled users to perform complex analyses directly through the web. Unfortunately, it also revoked the freedom to easily select the most appropriate tools. To address this, we have developed Galaxy ToolShed.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Nucleic Acids Res
                Journal ID (iso-abbrev): Nucleic Acids Res
                Journal ID (hwp): nar
                Journal ID (publisher-id): nar
                Title: Nucleic Acids Research
                Publisher: Oxford University Press
                ISSN (Print): 0305-1048
                ISSN (Electronic): 1362-4962
                Publication date (Print): 08 July 2016
                Publication date (Electronic): 13 April 2016
                Publication date PMC-release: 13 April 2016
                Volume: 44
                Issue: Web Server issue
                Pages: W160-W165
                Affiliations
                [1 ]Max Planck Institute of Immunobiology and Epigenetics, 79108 Freiburg, Germany
                [2 ]University of Freiburg, Department of Computer Science, 79110 Freiburg, Germany
                [3 ]Faculty of Biology, University of Freiburg, 79104 Freiburg, Germany
                [4 ]Weill Cornell Medical College, Applied Bioinformatics Core, Department of Physiology and Biophysics, New York, NY 10065, USA
                Author notes
                [* ]To whom correspondence should be addressed. Tel: +49 761 5108738; Fax: +49 761 5108 80738; Email: manke@ 123456ie-freiburg.mpg.de
                [† ]These authors contributed equally to the paper as first authors.
                Author information
                http://orcid.org/0000-0002-3079-6586
                http://orcid.org/0000-0002-4166-0991
                Article
                DOI: 10.1093/nar/gkw257
                PMC ID: 4987876
                PubMed ID: 27079975
                SO-VID: 9657bb68-8809-45be-a1a7-3b3dc430a0f8
                Copyright © © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@ 123456oup.com

                History
                Date accepted : 02 April 2016
                Date revision received : 22 March 2016
                Date received : 02 February 2016
                Page count
                Pages: 6
                Categories
                Subject: Web Server issue
                Custom metadata
                cover-date 08 July 2016

                ScienceOpen disciplines: Genetics
                Data availability:
                ScienceOpen disciplines: Genetics

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