Literature DB >> 24861628

COUGER--co-factors associated with uniquely-bound genomic regions.

Alina Munteanu1, Uwe Ohler2, Raluca Gordân3.   

Abstract

Most transcription factors (TFs) belong to protein families that share a common DNA binding domain and have very similar DNA binding preferences. However, many paralogous TFs (i.e. members of the same TF family) perform different regulatory functions and interact with different genomic regions in the cell. A potential mechanism for achieving this differential in vivo specificity is through interactions with protein co-factors. Computational tools for studying the genomic binding profiles of paralogous TFs and identifying their putative co-factors are currently lacking. Here, we present an interactive web implementation of COUGER, a classification-based framework for identifying protein co-factors that might provide specificity to paralogous TFs. COUGER takes as input two sets of genomic regions bound by paralogous TFs, and it identifies a small set of putative co-factors that best distinguish the two sets of sequences. To achieve this task, COUGER uses a classification approach, with features that reflect the DNA-binding specificities of the putative co-factors. The identified co-factors are presented in a user-friendly output page, together with information that allows the user to understand and to explore the contributions of individual co-factor features. COUGER can be run as a stand-alone tool or through a web interface: http://couger.oit.duke.edu.
© The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

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Year:  2014        PMID: 24861628      PMCID: PMC4086139          DOI: 10.1093/nar/gku435

Source DB:  PubMed          Journal:  Nucleic Acids Res        ISSN: 0305-1048            Impact factor:   16.971


  28 in total

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3.  Normalized mutual information feature selection.

Authors:  Pablo A Estévez; Michel Tesmer; Claudio A Perez; Jacek M Zurada
Journal:  IEEE Trans Neural Netw       Date:  2009-01-13

4.  Requirement of c-jun transcription factor on the mouse mast cell protease-6 expression in the mast cells.

Authors:  Dae-Ki Kim; Young-Mi Lee
Journal:  Arch Biochem Biophys       Date:  2004-11-01       Impact factor: 4.013

5.  Genomic regions flanking E-box binding sites influence DNA binding specificity of bHLH transcription factors through DNA shape.

Authors:  Raluca Gordân; Ning Shen; Iris Dror; Tianyin Zhou; John Horton; Remo Rohs; Martha L Bulyk
Journal:  Cell Rep       Date:  2013-04-04       Impact factor: 9.423

6.  UniPROBE, update 2011: expanded content and search tools in the online database of protein-binding microarray data on protein-DNA interactions.

Authors:  Kimberly Robasky; Martha L Bulyk
Journal:  Nucleic Acids Res       Date:  2010-10-30       Impact factor: 16.971

7.  TRANSFAC and its module TRANSCompel: transcriptional gene regulation in eukaryotes.

Authors:  V Matys; O V Kel-Margoulis; E Fricke; I Liebich; S Land; A Barre-Dirrie; I Reuter; D Chekmenev; M Krull; K Hornischer; N Voss; P Stegmaier; B Lewicki-Potapov; H Saxel; A E Kel; E Wingender
Journal:  Nucleic Acids Res       Date:  2006-01-01       Impact factor: 16.971

8.  Stability selection for regression-based models of transcription factor-DNA binding specificity.

Authors:  Fantine Mordelet; John Horton; Alexander J Hartemink; Barbara E Engelhardt; Raluca Gordân
Journal:  Bioinformatics       Date:  2013-07-01       Impact factor: 6.937

9.  PIPs: human protein-protein interaction prediction database.

Authors:  Mark D McDowall; Michelle S Scott; Geoffrey J Barton
Journal:  Nucleic Acids Res       Date:  2008-11-06       Impact factor: 16.971

10.  Design and analysis of ChIP-seq experiments for DNA-binding proteins.

Authors:  Peter V Kharchenko; Michael Y Tolstorukov; Peter J Park
Journal:  Nat Biotechnol       Date:  2008-11-16       Impact factor: 54.908

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