Springer Comparative Gene Finding: Models, Algorithms and Implementation: 11 (Computational Biology, 11)

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. Review From the reviews:“I found it to be a sufficiently comprehensive documentation of gene finding approaches and techniques. … Therefore, this book’s publication was extremely welcome news. Readers should really have a good background … in order to fully benefit from this book. … synopsis will hopefully give potential readers a fairly good idea of the nature of the book. In summary, this book is as good as it could be for the purposes it states.” (M. M. Tanik, ACM Computing Reviews, September, 2010) From the Back Cover 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.