Discovering Motifs in Ranked Lists of DNA Sequences
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TLDR
The implementation of this framework in a software application, termed DRIM (discovery of rank imbalanced motifs), which identifies sequence motifs in lists of ranked DNA sequences, is demonstrated, demonstrating that the statistical framework embodied in the DRIM software tool is highly effective for identifying regulatory sequence elements in a variety of applications.Abstract:
Computational methods for discovery of sequence elements that are enriched in a target set compared with a background set are fundamental in molecular biology research. One example is the discovery of transcription factor binding motifs that are inferred from ChIP–chip (chromatin immuno-precipitation on a microarray) measurements. Several major challenges in sequence motif discovery still require consideration: (i) the need for a principled approach to partitioning the data into target and background sets; (ii) the lack of rigorous models and of an exact p-value for measuring motif enrichment; (iii) the need for an appropriate framework for accounting for motif multiplicity; (iv) the tendency, in many of the existing methods, to report presumably significant motifs even when applied to randomly generated data. In this paper we present a statistical framework for discovering enriched sequence elements in ranked lists that resolves these four issues. We demonstrate the implementation of this framework in a software application, termed DRIM (discovery of rank imbalanced motifs), which identifies sequence motifs in lists of ranked DNA sequences. We applied DRIM to ChIP–chip and CpG methylation data and obtained the following results. (i) Identification of 50 novel putative transcription factor (TF) binding sites in yeast ChIP–chip data. The biological function of some of them was further investigated to gain new insights on transcription regulation networks in yeast. For example, our discoveries enable the elucidation of the network of the TF ARO80. Another finding concerns a systematic TF binding enhancement to sequences containing CA repeats. (ii) Discovery of novel motifs in human cancer CpG methylation data. Remarkably, most of these motifs are similar to DNA sequence elements bound by the Polycomb complex that promotes histone methylation. Our findings thus support a model in which histone methylation and CpG methylation are mechanistically linked. Overall, we demonstrate that the statistical framework embodied in the DRIM software tool is highly effective for identifying regulatory sequence elements in a variety of applications ranging from expression and ChIP–chip to CpG methylation data. DRIM is publicly available at http://bioinfo.cs.technion.ac.il/drim.read more
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References
More filters
Journal ArticleDOI
Gene Ontology: tool for the unification of biology
M Ashburner,Catherine A. Ball,Judith A. Blake,David Botstein,Heather Butler,J. M. Cherry,Allan Peter Davis,Kara Dolinski,Selina S. Dwight,J.T. Eppig,Midori A. Harris,David P. Hill,Laurie Issel-Tarver,Andrew Kasarskis,Suzanna E. Lewis,John C. Matese,Joel E. Richardson,M. Ringwald,Gerald M. Rubin,Gavin Sherlock +19 more
TL;DR: The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing.
Proceedings Article
Fitting a mixture model by expectation maximization to discover motifs in biopolymers.
Timothy L. Bailey,Charles Elkan +1 more
TL;DR: The algorithm described in this paper discovers one or more motifs in a collection of DNA or protein sequences by using the technique of expectation maximization to fit a two-component finite mixture model to the set of sequences.
Journal ArticleDOI
Transcriptional Regulatory Networks in Saccharomyces cerevisiae
Tong Ihn Lee,Nicola J. Rinaldi,François Robert,Duncan T. Odom,Ziv Bar-Joseph,Georg K. Gerber,Nancy M. Hannett,Christopher T. Harbison,Craig M. Thompson,Itamar Simon,Julia Zeitlinger,Ezra G. Jennings,Heather L. Murray,D. Benjamin Gordon,Bing Ren,John J. Wyrick,Jean-Bosco Tagne,Thomas L. Volkert,Ernest Fraenkel,David K. Gifford,Richard A. Young +20 more
TL;DR: This work determines how most of the transcriptional regulators encoded in the eukaryote Saccharomyces cerevisiae associate with genes across the genome in living cells, and identifies network motifs, the simplest units of network architecture, and demonstrates that an automated process can use motifs to assemble a transcriptional regulatory network structure.
Journal ArticleDOI
Control of developmental regulators by Polycomb in human embryonic stem cells.
Tong Ihn Lee,Richard G. Jenner,Laurie A. Boyer,Matthew G. Guenther,Stuart S. Levine,Roshan M. Kumar,Brett Chevalier,Sarah E. Johnstone,Megan F. Cole,Kyoichi Isono,Haruhiko Koseki,Takuya Fuchikami,Kuniya Abe,Heather L. Murray,Jacob P. Zucker,Bingbing Yuan,George W. Bell,Elizabeth Herbolsheimer,Nancy M. Hannett,Kaiming Sun,Duncan T. Odom,Arie P. Otte,Thomas L. Volkert,David P. Bartel,Douglas A. Melton,David K. Gifford,David K. Gifford,Rudolf Jaenisch,Richard A. Young +28 more
TL;DR: It is found that PRC2 target genes are preferentially activated during ES cell differentiation and that the ES cell regulators OCT4, SOX2, and NANOG cooccupy a significant subset of these genes.
Journal ArticleDOI
Transcriptional regulatory code of a eukaryotic genome
Christopher T. Harbison,D. Benjamin Gordon,Tong Ihn Lee,Nicola J. Rinaldi,Kenzie D MacIsaac,Timothy Danford,Nancy M. Hannett,Jean-Bosco Tagne,David B. Reynolds,Jane Yoo,Ezra G. Jennings,Julia Zeitlinger,Dmitry K. Pokholok,Manolis Kellis,Manolis Kellis,P. Alex Rolfe,Ken T. Takusagawa,Eric S. Lander,Eric S. Lander,David K. Gifford,David K. Gifford,Ernest Fraenkel,Richard A. Young,Richard A. Young +23 more
TL;DR: An initial map of yeast's transcriptional regulatory code is constructed by identifying the sequence elements that are bound by regulators under various conditions and that are conserved among Saccharomyces species.
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