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Computational Biology of Transcription Factor Binding

  • Book
  • © 2010

Overview

Editors:
  1. Istvan Ladunga

    1. , Department of Statistics, University of Nebraska-Lincoln, Lincoln, USA

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  • Guides scientists of all disciplines through the jungle of regulatory regions, ChIP-seq, about 200 motif discovery tools and others
  • Includes vital tricks-of-the-trade from experts well-versed in the technology whenever available
  • Provides cutting-edge protocols for computer biologists and for researchers who wish to better communicate with them
  • Includes supplementary material: sn.pub/extras

Part of the book series: Methods in Molecular Biology (MIMB, volume 674)

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Table of contents (24 protocols)

  1. Front Matter

    Pages i-xi
    Download chapter PDF

  2. An Overview of the Computational Analyses and Discovery of Transcription Factor Binding Sites

    • Istvan Ladunga
    Pages 1-22

  3. Components and Mechanisms of Regulation of Gene Expression

    • Alper Yilmaz, Erich Grotewold
    Pages 23-32

  4. Regulatory Regions in DNA: Promoters, Enhancers, Silencers, and Insulators

    • Jean-Jack M. Riethoven
    Pages 33-42

  5. Three-Dimensional Structures of DNA-Bound Transcriptional Regulators

    • Tripti Shrivastava, Tahir H. Tahirov
    Pages 43-55

  6. Identification of Promoter Regions and Regulatory Sites

    • Victor V. Solovyev, Ilham A. Shahmuradov, Asaf A. Salamov
    Pages 57-83

  7. Motif Discovery Using Expectation Maximization and Gibbs’ Sampling

    • Gary D. Stormo
    Pages 85-95

  8. Probabilistic Approaches to Transcription Factor Binding Site Prediction

    • Stefan Posch, Jan Grau, André Gohr, Jens Keilwagen, Ivo Grosse
    Pages 97-119

  9. The Motif Tool Assessment Platform (MTAP) for Sequence-Based Transcription Factor Binding Site Prediction Tools

    • Daniel Quest, Hesham Ali
    Pages 121-141

  10. Computational Analysis of ChIP-seq Data

    • Hongkai Ji
    Pages 143-159

  11. Probabilistic Peak Calling and Controlling False Discovery Rate Estimations in Transcription Factor Binding Site Mapping from ChIP-seq

    • Shuo Jiao, Cheryl P. Bailey, Shunpu Zhang, Istvan Ladunga
    Pages 161-177

  12. Sequence Analysis of Chromatin Immunoprecipitation Data for Transcription Factors

    • Kenzie D. MacIsaac, Ernest Fraenkel
    Pages 179-193

  13. Inferring Protein–DNA Interaction Parameters from SELEX Experiments

    • Marko Djordjevic
    Pages 195-211

  14. Kernel-Based Identification of Regulatory Modules

    • Sebastian J. Schultheiss
    Pages 213-223

  15. Identification of Transcription Factor Binding Sites Derived from Transposable Element Sequences Using ChIP-seq

    • Andrew B. Conley, I. King Jordan
    Pages 225-240

  16. Target Gene Identification via Nuclear Receptor Binding Site Prediction

    • Gabor Varga
    Pages 241-249

  17. Computing Chromosome Conformation

    • James Fraser, Mathieu Rousseau, Mathieu Blanchette, Josée Dostie
    Pages 251-268

  18. Large-Scale Identification and Analysis of C-Proteins

    • Valery Sorokin, Konstantin Severinov, Mikhail S. Gelfand
    Pages 269-282

  19. Evolution of cis-Regulatory Sequences in Drosophila

    • Xin He, Saurabh Sinha
    Pages 283-296

  20. Regulating the Regulators: Modulators of Transcription Factor Activity

    • Logan Everett, Matthew Hansen, Sridhar Hannenhalli
    Pages 297-312
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Keywords

About this book

Transcriptional regulation controls the basic processes of life. Its complex, dynamic, and hierarchical networks control the momentary availability of messenger RNAs for protein synthesis. Transcriptional regulation is key to cell division, development, tissue differen- ation, and cancer as discussed in Chapters 1 and 2. We have witnessed rapid, major developments at the intersection of computational biology, experimental technology, and statistics. A decade ago, researches were struggling with notoriously challenging predictions of isolated binding sites from low-throughput experiments. Now we can accurately predict cis-regulatory modules, conserved cl- ters of binding sites (Chapters 13 and 15), partly based on high-throughput ch- matin immunoprecipitation experiments in which tens of millions of DNA segments are sequenced by massively parallel, next-generation sequencers (ChIP-seq, Chapters 9, 10, and 11). These spectacular developments have allowed for the genome-wide mappings of tens of thousands of transcription factor binding sites in yeast, bacteria, mammals, insects, worms, and plants. Please also note the no less spectacular failures in many laboratories around the world.

Reviews

From the reviews:

“Gives us an overview of transcription and its regulation. … The methods are very up-to-date and clearly presented by authors … . The use of tools for predicting transcription factors binding sites is also very carefully presented in a text that is accessible for beginners … . In summary, this book presents an important tool for entering the growing field of bioinformatics and its use for the study of transcription. … If we cannot read all books ever written, this one is a good choice.” (Carla Columbano Oliveira, Brazilian Journal of Pharmaceutical Sciences, Vol. 47 (2), Summer, 2011)

Editors and Affiliations

  • , Department of Statistics, University of Nebraska-Lincoln, Lincoln, USA

    Istvan Ladunga

Bibliographic Information

  • Book Title: Computational Biology of Transcription Factor Binding

  • Editors: Istvan Ladunga

  • Series Title: Methods in Molecular Biology

  • DOI: https://doi.org/10.1007/978-1-60761-854-6

  • Publisher: Humana Totowa, NJ

  • eBook Packages: Springer Protocols

  • Copyright Information: Springer Science+Business Media, LLC 2010

  • Hardcover ISBN: 978-1-60761-853-9Published: 13 September 2010

  • Softcover ISBN: 978-1-4939-6166-5Published: 23 August 2016

  • eBook ISBN: 978-1-60761-854-6Published: 09 September 2010

  • Series ISSN: 1064-3745

  • Series E-ISSN: 1940-6029

  • Edition Number: 1

  • Number of Pages: XI, 454

  • Topics: Protein Science, Computer Appl. in Life Sciences

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