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      A comprehensive comparison of tools for differential ChIP-seq analysis

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          Abstract

          ChIP-seq has become a widely adopted genomic assay in recent years to determine binding sites for transcription factors or enrichments for specific histone modifications. Beside detection of enriched or bound regions, an important question is to determine differences between conditions. While this is a common analysis for gene expression, for which a large number of computational approaches have been validated, the same question for ChIP-seq is particularly challenging owing to the complexity of ChIP-seq data in terms of noisiness and variability. Many different tools have been developed and published in recent years. However, a comprehensive comparison and review of these tools is still missing. Here, we have reviewed 14 tools, which have been developed to determine differential enrichment between two conditions. They differ in their algorithmic setups, and also in the range of applicability. Hence, we have benchmarked these tools on real data sets for transcription factors and histone modifications, as well as on simulated data sets to quantitatively evaluate their performance. Overall, there is a great variety in the type of signal detected by these tools with a surprisingly low level of agreement. Depending on the type of analysis performed, the choice of method will crucially impact the outcome.

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          A clustering approach for identification of enriched domains from histone modification ChIP-Seq data.

          Chromatin states are the key to gene regulation and cell identity. Chromatin immunoprecipitation (ChIP) coupled with high-throughput sequencing (ChIP-Seq) is increasingly being used to map epigenetic states across genomes of diverse species. Chromatin modification profiles are frequently noisy and diffuse, spanning regions ranging from several nucleosomes to large domains of multiple genes. Much of the early work on the identification of ChIP-enriched regions for ChIP-Seq data has focused on identifying localized regions, such as transcription factor binding sites. Bioinformatic tools to identify diffuse domains of ChIP-enriched regions have been lacking. Based on the biological observation that histone modifications tend to cluster to form domains, we present a method that identifies spatial clusters of signals unlikely to appear by chance. This method pools together enrichment information from neighboring nucleosomes to increase sensitivity and specificity. By using genomic-scale analysis, as well as the examination of loci with validated epigenetic states, we demonstrate that this method outperforms existing methods in the identification of ChIP-enriched signals for histone modification profiles. We demonstrate the application of this unbiased method in important issues in ChIP-Seq data analysis, such as data normalization for quantitative comparison of levels of epigenetic modifications across cell types and growth conditions. http://home.gwu.edu/ approximately wpeng/Software.htm. Supplementary data are available at Bioinformatics online.
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            Assessing computational tools for the discovery of transcription factor binding sites.

            The prediction of regulatory elements is a problem where computational methods offer great hope. Over the past few years, numerous tools have become available for this task. The purpose of the current assessment is twofold: to provide some guidance to users regarding the accuracy of currently available tools in various settings, and to provide a benchmark of data sets for assessing future tools.
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              A rapid, extensive, and transient transcriptional response to estrogen signaling in breast cancer cells.

              We report the immediate effects of estrogen signaling on the transcriptome of breast cancer cells using global run-on and sequencing (GRO-seq). The data were analyzed using a new bioinformatic approach that allowed us to identify transcripts directly from the GRO-seq data. We found that estrogen signaling directly regulates a strikingly large fraction of the transcriptome in a rapid, robust, and unexpectedly transient manner. In addition to protein-coding genes, estrogen regulates the distribution and activity of all three RNA polymerases and virtually every class of noncoding RNA that has been described to date. We also identified a large number of previously undetected estrogen-regulated intergenic transcripts, many of which are found proximal to estrogen receptor binding sites. Collectively, our results provide the most comprehensive measurement of the primary and immediate estrogen effects to date and a resource for understanding rapid signal-dependent transcription in other systems. Copyright © 2011 Elsevier Inc. All rights reserved.
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                Author and article information

                Journal
                Brief Bioinform
                Brief. Bioinformatics
                bib
                bib
                Briefings in Bioinformatics
                Oxford University Press
                1467-5463
                1477-4054
                November 2016
                13 January 2016
                13 January 2016
                : 17
                : 6
                : 953-966
                Author notes
                Corresponding author: Carl Herrmann, IPMB Universit㲠Heidelberg and Department of Theoretical Bioinformatics, DKFZ, Im Neuenheimer Feld 364, D-69120 Heidelberg, Tel.: (+49) 6221 423612; E-mail: carl.herrmann@ 123456uni-heidelberg.de
                Article
                bbv110
                10.1093/bib/bbv110
                5142015
                26764273
                34edcd7e-ff30-4d07-84d0-77f3bf86e9f8
                © The Author 2016. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial 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@oup.com

                History
                : 9 October 2015
                : 21 November 2015
                Page count
                Pages: 14
                Categories
                Software Review

                Bioinformatics & Computational biology
                chip-seq,differential analysis,software
                Bioinformatics & Computational biology
                chip-seq, differential analysis, software

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