Methods for Computational Gene Prediction

About this book

Inferring the precise locations and splicing patterns of genes in DNA is a difficult but important task, with broad applications to biomedicine. The mathematical and statistical techniques that have been applied to this problem are surveyed and organized into a logical framework based on the theory of parsing. Both established approaches and methods at the forefront of current research are discussed. Numerous case studies of existing software systems are provided, in addition to detailed examples that work through the actual implementation of effective gene-predictors using hidden Markov models and other machine-learning techniques. Background material on probability theory, discrete mathematics, computer science, and molecular biology is provided, making the book accessible to students and researchers from across the life and computational sciences.

Contents

Foreword Steven Salzberg; 1. Introduction; 2. Mathematical preliminaries; 3. Overview of gene prediction; 4. Gene finder evaluation; 5. A toy Exon finder; 6. Hidden Markov models; 7. Signal and content sensors; 8. Generalized hidden Markov models; 9. Comparative gene finding; 10. Machine Learning methods; 11. Tips and tricks; 12. Advanced topics; Appendix - online resources; References; Index.

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Biography

W. H. Majoros is Staff Scientist at the Center for Bioinformatics and Computational Biology, in the Institute for Genome Sciences and Policy at Duke University. He has worked as a research scientist in the fields of computational biology, natural language processing, and information retrieval for over a decade. He was part of the human genome project at Celera Genomics and has taken part in the sequencing and analysis of numerous organisms including human, mouse, fly and mosquito.