Comparative genomics is a new and emerging ?eld, and with the explosion of ava- able biological sequences the requests for faster, more ef?cient and more robust algorithms to analyze all this data are immense. This book is meant to serve as a self-contained instruction of the state-of-the-art of computational gene ?nding in general and of comparative approaches in particular. It is meant as an overview of the various methods that have been applied in the ?eld, and a quick introduction into how computational gene ?nders are built in general. A beginner to the ?eld could use this book as a guide through to the main points to think about when constructing a gene ?nder, and the main algorithms that are in use. On the other hand, the more experienced gene ?nder should be able to use this book as a reference to different methods and to the main components incorporated in these methods. I have focused on the main uses of the covered methods and avoided much of the technical details and general extensions of the models. In exchange I have tried to supply references to more detailed accounts of the different research areas touched upon. The book, however, makes no claim on being comprehensive.
This practical guide provides detailed descriptions of the models and algorithms and how to implement them in an easy-to-follow style
The book summarizes the advances in the field and gives clear and concise instructions on how to proceed though the project process, enabling readers to be able to construct their own gene finding software
Comparative genomics is an emerging field, which is being fed by an explosion in the number of possible biological sequences. This has led to an immense demand for faster, more efficient and more robust computer algorithms to analyze this large amount of data.
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. The text assumes the reader has some background in bioinformatics, especially in mathematics and mathematical statistics. A basic knowledge of analysis, probability theory and random processes would also aid the reader.
Topics and 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, and describes the most important sub-models used
- 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, covering a number of the different parameter estimation and optimization techniques commonly used in gene finding
- Illustrates how to implement a comparative gene finder, explaining the different steps and various accuracy assessment measures used to debug and benchmark the software
A useful text for postgraduate students, this book provides valuable insights and examples for researchers wishing to enter the field quickly. In addition to the specific focus on the algorithmic details surrounding computational gene finding, readers obtain an introduction to the fundamentals of computational biology and biological sequence analysis, as well as an overview of the important mathematical and statistical applications in bioinformatics.
Dr. Marina Axelson-Fisk is an Associate Professor at the Department of Mathematical Sciences of Chalmers University of Technology, Gothenburg, Sweden.
Single Species Gene Finding
Comparative Gene Finding
Gene Structure Submodels
Implementation of a Comparative Gene Finder