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Computational and Evolutionary Analysis of HIV Molecular Sequences

Jerry Learn learn at u.washington.edu
Thu Oct 12 02:39:21 EST 2000

(Editors:  Allen G. Rodrigo & Gerald H. Learn, Jr.)

We are pleased to announce the publication of the edited volume
"Computational and Evolutionary Analysis of HIV Molecular Sequences" by
Kluwer Academic Publishers (for details, please see

Computational and Evolutionary Analysis of HIV Molecular Sequences is for
all researchers invested in HIV research, even those who have only 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.  

Contents and contributors:

1. Sampling and Processing HIV Molecular Sequences: a Computational
Evolutionary Biologist's Perspective; A.G. Rodrigo, E. Hanley, P. C.
Goracke, G. H. Learn, Jr. 
2. Accessing HIV Molecular Information; B.T. Foley. 
3. HIV-1 Subtyping; C.L. Kuiken, T. Leitner. 
4. HIV Sequence Signatures and Similarities; B. Korber. 
5. Graphical Methods for Exploring Sequence Relationships; G.F. Weiller. 
6. Quantifying Heterogeneity in the HIV Genome; H.P. Pinheiro, F.
7. Phylogenetics of HIV; D. Posada, K. A. Crandall, D. M. Hillis. 
8. Goals and Strategies for Analysis of Recombination Among Molecular
Sequences; J.C. Stephens. 
9. Molecular Population Genetics: Coalescent Methods Based on Summary
Statistics; D.A. Vasco, K. A. Crandall, Y.-X. Fu. 
10. Population Genetics of HIV: Parameter Estimation Using 
Genealogy-Based Methods; P. Beerli, N. C. Grassly, M. K. Kuhner, D. 
Nickle, O. Pybus, M. Rain, A. Rambaut, A. G. Rodrigo, Y. Wang. 
11. Detecting Selection in Protein Coding Genes Using the Rate of
Nonsynonymous and Synonymous Divergence; R. Neilsen. 
12. Drugs Targeted at HIV – Successes and Resistance; C. Sansom, A. 

We welcome comments and queries.  

Allen Rodrigo
School of Biological Sciences
University of Auckland
a.rodrigo at auckland.ac.nz

Jerry Learn
Department of Microbiology
University of Washington
learn at u.washington.edu


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