Computational biology of transcription factor binding / edited by Istvan Ladunga.Material type: TextSeries: Springer protocols (Series) | Methods in molecular biology (Clifton, N.J.) ; v. 674.Publication details: New York, NY : Humana Press, 2010. Description: 1 online resource (xi, 454 pages) : illustrationsContent type: text Media type: computer Carrier type: online resourceISBN: 9781607618546; 1607618540Subject(s): Computational biology | Transcription factors | Computational Biology | Transcription Factors | Computational biology | Transcription factors | eiwitten | proteins | levenswetenschappen | life sciences | computertechnieken | computer techniques | Biology (General) | Biologie (algemeen)Genre/Form: Electronic books. Additional physical formats: Print version:: Computational biology of transcription factor binding.DDC classification: 572.80285 LOC classification: QH324.2 | .C66 2010NLM classification: QU26.5Online resources: Click here to access online
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Includes bibliographical references and index.
An overview of the computational analyses and discovery of transcription factor binding sites -- Components and mechanisms of regulation of gene expression -- Regulatory regions in DNA: promoters, enhancers, silencers, and insulators -- Three-dimensional structures of DNA-bound transcriptional regulators -- Identification of promoter regions and regulatory sites -- Motif discovery using expectation maximization and Gibbs' sampling -- Probabilistic approaches to transcription factor binding site prediction -- The motif tool assessment platform (MTAP) for sequence-based transcription factor binding site prediction tools -- Computational analysis of ChIP-seq data -- Probabilistic peak calling and controlling false discovery rate estimations in transcription factor binding site mapping from ChIP-seq -- Sequence analysis of chromatin immunoprecipitation data for transcription factors -- Inferring protein-DNA interaction parameters from SELEX experiments -- Kernel-based identification of regulatory modules -- Identification of transcription factor binding sites derived from transposable element sequences using ChIP-seq -- Target gene identification via nuclear receptor binding site prediction -- Computing chromosome conformation -- Large-scale identification and analysis of C-proteins -- Evolution of cis-regulatory sequences in drosophila -- Regulating the regulators: modulators of transcription factor activity -- Annotating the regulatory genome -- Computational identification of plant transcription factors and the construction of the plant TFDB database -- Practical computational methods for regulatory genomics: a cisGRN-lexicon and cisGRN-browser for gene regulatory networks -- Reconstructing transcriptional regulatory networks using three-way mutual information and bayesian networks -- Computational methods for analyzing dynamic regulatory networks.
Print version record.
Through great experimental difficulty, we've witnessed rapid, crucial developments at the intersection of computational biology, experimental technology, and statistics through which the vital process of transcriptional regulation can be further examined. In Computational Biology of Transcription Factor Binding, experts in the field examine the basic principles and provide detailed guidance for the computational analyses and biological interpretations of transcription factor binding, while disclosing critical practical information and caveats that are missing from many research publications. The volume serves not only computational biologists but experimentalists as well, who may want to better understand how to design and execute experiments and to communicate more effectively with computational biologists, computer scientists, and statisticians. Written for the highly successful Methods in Molecular Biology series, this work provides the kind of detailed description and implementation advice that is crucial for getting optimal results in the lab. Authoritative and easy to use, Computational Biology of Transcription Factor Binding guides scientists working in this area and demands not only new experiments but also the re-annotation of existing experimental data and computational predictions leading to important ongoing, major paradigm changes for us all.