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      PscanChIP: finding over-represented transcription factor-binding site motifs and their correlations in sequences from ChIP-Seq experiments

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          Abstract

          Chromatin immunoprecipitation followed by sequencing with next-generation technologies (ChIP-Seq) has become the de facto standard for building genome-wide maps of regions bound by a given transcription factor (TF). The regions identified, however, have to be further analyzed to determine the actual DNA-binding sites for the TF, as well as sites for other TFs belonging to the same TF complex or in general co-operating or interacting with it in transcription regulation. PscanChIP is a web server that, starting from a collection of genomic regions derived from a ChIP-Seq experiment, scans them using motif descriptors like JASPAR or TRANSFAC position-specific frequency matrices, or descriptors uploaded by users, and it evaluates both motif enrichment and positional bias within the regions according to different measures and criteria. PscanChIP can successfully identify not only the actual binding sites for the TF investigated by a ChIP-Seq experiment but also secondary motifs corresponding to other TFs that tend to bind the same regions, and, if present, precise positional correlations among their respective sites. The web interface is free for use, and there is no login requirement. It is available at http://www.beaconlab.it/pscan_chip_dev.

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          Most cited references14

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          TRANSFAC: transcriptional regulation, from patterns to profiles.

          The TRANSFAC database on eukaryotic transcriptional regulation, comprising data on transcription factors, their target genes and regulatory binding sites, has been extended and further developed, both in number of entries and in the scope and structure of the collected data. Structured fields for expression patterns have been introduced for transcription factors from human and mouse, using the CYTOMER database on anatomical structures and developmental stages. The functionality of Match, a tool for matrix-based search of transcription factor binding sites, has been enhanced. For instance, the program now comes along with a number of tissue-(or state-)specific profiles and new profiles can be created and modified with Match Profiler. The GENE table was extended and gained in importance, containing amongst others links to LocusLink, RefSeq and OMIM now. Further, (direct) links between factor and target gene on one hand and between gene and encoded factor on the other hand were introduced. The TRANSFAC public release is available at http://www.gene-regulation.com. For yeast an additional release including the latest data was made available separately as TRANSFAC Saccharomyces Module (TSM) at http://transfac.gbf.de. For CYTOMER free download versions are available at http://www.biobase.de:8080/index.html.
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            DNA binding sites: representation and discovery.

            G Stormo (2000)
            The purpose of this article is to provide a brief history of the development and application of computer algorithms for the analysis and prediction of DNA binding sites. This problem can be conveniently divided into two subproblems. The first is, given a collection of known binding sites, develop a representation of those sites that can be used to search new sequences and reliably predict where additional binding sites occur. The second is, given a set of sequences known to contain binding sites for a common factor, but not knowing where the sites are, discover the location of the sites in each sequence and a representation for the specificity of the protein.
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              Computation for ChIP-seq and RNA-seq studies.

              Genome-wide measurements of protein-DNA interactions and transcriptomes are increasingly done by deep DNA sequencing methods (ChIP-seq and RNA-seq). The power and richness of these counting-based measurements comes at the cost of routinely handling tens to hundreds of millions of reads. Whereas early adopters necessarily developed their own custom computer code to analyze the first ChIP-seq and RNA-seq datasets, a new generation of more sophisticated algorithms and software tools are emerging to assist in the analysis phase of these projects. Here we describe the multilayered analyses of ChIP-seq and RNA-seq datasets, discuss the software packages currently available to perform tasks at each layer and describe some upcoming challenges and features for future analysis tools. We also discuss how software choices and uses are affected by specific aspects of the underlying biology and data structure, including genome size, positional clustering of transcription factor binding sites, transcript discovery and expression quantification.
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                Author and article information

                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                July 2013
                7 June 2013
                7 June 2013
                : 41
                : Web Server issue
                : W535-W543
                Affiliations
                1Dipartimento di Bioscienze, Università di Milano, Via Celoria 26, 20133 Milano, Italy, 2Istituto di Biomembrane e Bioenergetica, Consiglio Nazionale delle Ricerche, Via Amendola 165/A, 70126 Bari, Italy and 3Dipartimento di Bioscienze, Biotecnologie e Biofarmaceutica, Università di Bari, Via Orabona 4, 70124 Bari, Italy
                Author notes
                *To whom correspondence should be addressed. Tel: +39 50314884; Fax: +39 50315042; Email: giulio.pavesi@ 123456unimi.it

                Present address: Giulio Pavesi, Dipartimento di Bioscienze, Università di Milano, Via Celoria 26, 20133 Milano, Italy.

                Article
                gkt448
                10.1093/nar/gkt448
                3692095
                23748563
                4f20b265-4efe-48b4-ab70-0ad723eea40b
                © The Author(s) 2013. 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/3.0/), 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
                : 17 February 2013
                : 1 May 2013
                : 2 May 2013
                Page count
                Pages: 9
                Categories
                Articles
                Custom metadata
                1 July 2013

                Genetics
                Genetics

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