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Comparative Gene Finding

Models, Algorithms and Implementation

  • Book
  • © 2015

Overview

Authors:
  1. Marina Axelson-Fisk

    1. Chalmers University of Technology, Göteborg, Sweden

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  • Provides detailed descriptions of the models and algorithms and how to implement them
  • Summarizes the advances in the field and gives clear and concise instructions on how to proceed though the project process
  • Updated and expanded new edition, now covering next-generation sequencing technology and conditional random fields
  • Includes supplementary material: sn.pub/extras

Part of the book series: Computational Biology (COBO, volume 20)

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Table of contents (8 chapters)

  1. Front Matter

    Pages i-xx
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  2. Introduction

    • Marina Axelson-Fisk
    Pages 1-28

  3. Single Species Gene Finding

    • Marina Axelson-Fisk
    Pages 29-105

  4. Sequence Alignment

    • Marina Axelson-Fisk
    Pages 107-174

  5. Comparative Gene Finding

    • Marina Axelson-Fisk
    Pages 175-200

  6. Gene Structure Submodels

    • Marina Axelson-Fisk
    Pages 201-267

  7. Parameter Training

    • Marina Axelson-Fisk
    Pages 269-310

  8. Implementation of a Comparative Gene Finder

    • Marina Axelson-Fisk
    Pages 311-324

  9. Annotation Pipelines for Next-Generation Sequencing Projects

    • Marina Axelson-Fisk
    Pages 325-367

  10. Back Matter

    Pages 369-382
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Keywords

About this book

This book presents a guide to building computational gene finders, and describes the state of the art in computational gene finding methods, with a focus on comparative approaches. Fully updated and expanded, this new edition examines next-generation sequencing (NGS) technology. The book also discusses conditional random fields, enhancing the broad coverage of topics spanning probability theory, statistics, information theory, optimization theory and numerical analysis. Features: introduces the fundamental terms and concepts in the field; discusses algorithms for single-species gene finding, and approaches to pairwise and multiple sequence alignments, then describes how the strengths in both areas can be combined to improve the accuracy of gene finding; explores the gene features most commonly captured by a computational gene model, and explains the basics of parameter training; illustrates how to implement a comparative gene finder; examines NGS techniques and how to build a genome annotation pipeline.

Reviews

“The structure of the book mirrors the learning steps for understanding how to perform gene finding. … Its target audience is mainly post-graduate researchers or established researchers with a background in mathematics or statistics applied in bioinformatics who need a thorough yet concise overview of this field.” (Irina Ioana Mohorianu, zbMATH 1350.92001, 2017)

“It skillfully introduces readers to a difficult subject, while at the same time motivating them to enter this very important area. … It is best suited for a graduate course or as an introduction for researchers not familiar with this field. … this is an excellent introduction to comparative gene finding. … I especially recommend this book to any computer scientist with an interest in current problems in bioinformatics.” (Burkhard Englert, Computing Reviews, December, 2015)

Authors and Affiliations

  • Chalmers University of Technology, Göteborg, Sweden

    Marina Axelson-Fisk

Bibliographic Information

  • Book Title: Comparative Gene Finding

  • Book Subtitle: Models, Algorithms and Implementation

  • Authors: Marina Axelson-Fisk

  • Series Title: Computational Biology

  • DOI: https://doi.org/10.1007/978-1-4471-6693-1

  • Publisher: Springer London

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: Springer-Verlag London 2015

  • Hardcover ISBN: 978-1-4471-6692-4Published: 22 April 2015

  • Softcover ISBN: 978-1-4471-6875-1Published: 05 October 2016

  • eBook ISBN: 978-1-4471-6693-1Published: 13 April 2015

  • Series ISSN: 1568-2684

  • Series E-ISSN: 2662-2432

  • Edition Number: 2

  • Number of Pages: XX, 382

  • Number of Illustrations: 81 b/w illustrations

  • Topics: Computational Biology/Bioinformatics, Bioinformatics

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