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.
Comparative Gene Finding: Models, Algorithms and Implementation
