Evolutionary Genomics

Statistical and Computational Methods, Volume 1

  • Maria Anisimova

Part of the Methods in Molecular Biology book series (MIMB, volume 855)

Table of contents

  1. Front Matter
    Pages i-xiv
  2. Introduction: Bioinformatician’s Primers

    1. Front Matter
      Pages 1-1
    2. Aidan Budd
      Pages 51-76
    3. Niko Beerenwinkel, Juliane Siebourg
      Pages 77-110
    4. Stéphane Aris-Brosou, Nicolas Rodrigue
      Pages 111-152
  3. Genomic Data Assembly, Alignment, and Homology Inference

    1. Front Matter
      Pages 153-153
    2. Tyler Alioto
      Pages 175-201
    3. Colin N. Dewey
      Pages 237-257
    4. Adrian M. Altenhoff, Christophe Dessimoz
      Pages 259-279
    5. Rajeev K. Azad, Jeffrey G. Lawrence
      Pages 281-308
  4. Genome Evolution: Insights from Statistical Analyses

    1. Front Matter
      Pages 309-309
    2. Sylvain Glémin, Nicolas Galtier
      Pages 311-335
    3. Wojciech Makałowski, Amit Pande, Valer Gotea, Izabela Makałowska
      Pages 337-359
    4. Nadia El-Mabrouk, David Sankoff
      Pages 397-429
    5. Laxmi Parida, Niina Haiminen
      Pages 431-455

About this book

Introduction

Together with early theoretical work in population genetics, the debate on sources of genetic makeup initiated by proponents of the neutral theory made a solid contribution to the spectacular growth in statistical methodologies for molecular evolution. Evolutionary Genomics: Statistical and Computational Methods is intended to bring together the more recent developments in the statistical methodology and the challenges that followed as a result of rapidly improving sequencing technologies.  Presented by top scientists from a variety of disciplines, the collection includes a wide spectrum of articles encompassing theoretical works and hands-on tutorials, as well as many reviews with key biological insight.  Volume 1 includes a helpful introductory section of bioinformatician primers followed by detailed chapters detailing genomic data assembly, alignment, and homology inference as well as insights into genome evolution from statistical analyses.  Written in the highly successful Methods in Molecular Biology™ series format, this work provides the kind of advice on methodology and implementation that is crucial for getting ahead in genomic data analyses.

 

Comprehensive and cutting-edge, Evolutionary Genomics: Statistical and Computational Methods is a treasure chest of state-of the-art methods to study genomic and omics data, certain to inspire both young and experienced readers to join the interdisciplinary field of evolutionary genomics.

Keywords

Bioinformatics Computational techniques Data assembly Genome biology Genomic evolution Homology inference Sequence alignment Statistical methodology

Editors and affiliations

  • Maria Anisimova
    • 1
  1. 1.Department of Computer ScienceETH ZürichZürichSwitzerland

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-61779-582-4
  • Copyright Information Springer Science+Business Media, LLC 2012
  • Publisher Name Humana Press, Totowa, NJ
  • eBook Packages Springer Protocols
  • Print ISBN 978-1-61779-581-7
  • Online ISBN 978-1-61779-582-4
  • Series Print ISSN 1064-3745
  • Series Online ISSN 1940-6029
  • About this book