Computational and Evolutionary Analysis of HIV Molecular Sequences

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Rodrigo, Allen G. (Επιμελητής έκδοσης), Learn, Gerald H. (Επιμελητής έκδοσης)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Boston, MA : Springer US : Imprint: Springer, 2001.
• Θέματα: Medicine.; Pharmacology.; Internal medicine.; Data structures (Computer science).; Evolutionary biology.; Animal anatomy.; Medicine & Public Health.; Internal Medicine.; Data Structures, Cryptology and Information Theory.; Animal Anatomy / Morphology / Histology.; Pharmacology/Toxicology.; Evolutionary Biology.

 Computational and Evolutionary Analysis of HIV Molecular Sequences is for all researchers interested in HIV research, even those who only have a nodding acquaintance with computational biology (or those who are familiar with some, but not all, aspects of the field). HIV research is unusual in that it brings together scientists from a wide range of disciplines: clinicians, pathologists, immunologists, epidemiologists, virologists, computational biologists, structural biologists, evolutionary biologists, statisticians and mathematicians. This book seeks to bridge the gap between these groups, in both subject matter and terminology. Focused largely on HIV genetic variation, Computational and Evolutionary Analysis of HIV Molecular Sequences covers such issues as sampling and processing sequences, population genetics, phylogenetics and drug targets. 

 Sampling and Processing HIV Molecular Sequences: A Computational Evolutionary Biologist’s Perspective -- Accessing HIV Molecular Information -- HIV-1 Subtyping -- HIV Sequence Sigmatires and Similarities -- Graphical Methods for Exploring Sequence Relationships -- Quantifying Heterogeneity in the HIV Genome -- Phylogenetics of HIV -- Goals and Strategies for Analysis of Recombination among HIV Molecular Sequences -- Molecular Population Genetics: Coalescent Methods Based on Summary Statistics -- Population Geetics of HIV: Parameter Estimation Using Genealogy-based Methods -- Detecting Selection in Protein Coding Genes Using the Rate of Nonsynonymous and Synonymous Divergence -- Drugs Targeted at HIV — Successes and Resistance.