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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.
List of contents
Introduction.- Single Species Gene Finding.- Sequence Alignment.- Comparative Gene Finding.- Gene Structure Submodels.- Parameter Training.- Implementation of a Comparative Gene Finder.- Annotation Pipelines for Next Generation Sequencing Projects.
Summary
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.
Additional text
“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)
Report
"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)
