Comparative Gene Finding

Models, Algorithms and Implementation

By Marina Axelson-Fisk

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This unique text describes the state-of-the-art in computational gene finding, with a particular focus on comparative approaches. It summarizes advances in the field and includes detailed descriptions of the models and algorithms and how to implement them.

Full Description

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  • ISBN13: 978-1-8499-6103-5
  • 324 Pages
  • User Level: Science
  • Publication Date: January 30, 2010
  • Available eBook Formats: PDF

Related Titles

  • Information Systems and Neuroscience
Full Description
This unique text/reference describes the state of the art in computational gene finding, with a particular focus on comparative approaches. Providing both an overview of the various methods that are applied in the field, and a concise guide on how computational gene finders are built, the book covers a broad range of topics from probability theory, statistics, information theory, optimization theory and numerical analysis. Features: describes how algorithms and sequence alignments can be combined to improve the accuracy of gene finding; introduces the basic biological terms and concepts in genetics, and provides an historical overview of algorithm development; explores the gene features most commonly captured by a computational gene model; discusses the algorithms most commonly used for single-species gene finding; investigates approaches to pairwise and multiple sequence alignments; explains the basics of parameter training; illustrates how to implement a comparative gene finder.
Table of Contents

Table of Contents

  1. Introduction.
  2. Single Species Gene Finding.
  3. Sequence Alignment.
  4. Comparative Gene Finding.
  5. Gene Structure Submodels.
  6. Parameter Training.
  7. Implementation of a Comparative Gene Finder.

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