Comparative Gene Finding: Models, Algorithms and Implementation: 20 (Computational Biology) - Tapa dura

Críticas

“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)

Reseña del editor

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.