Models and Algorithms for Genome Evolution
Description
This authoritative text/reference presents a review of the history, current status, and potential future directions of computational biology in molecular evolution. Gathering together the unique insights of an international selection of prestigious researchers, this must-read volume examines the latest developments in the field, the challenges that remain, and the new avenues emerging from the growing influx of sequence data. These viewpoints build upon the pioneering work of David Sankoff, one of the founding fathers of computational biology, and mark the 50 th anniversary of his first scientific article. The broad spectrum of rich contributions in this essential collection will appeal to all computer scientists, mathematicians and biologists involved in comparative genomics, phylogenetics and related areas.
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Persons
Dr. Cedric Chauve is an associate professor at the Department of Mathematics of Simon Fraser University, Canada.
Dr. Nadia El-Mabrouk is a full professor at the Computer Science Department of the University of Montreal, Canada.
Dr. Eric Tannier is a researcher at the Bioinformatics and Evolutionary Genomics Group of Inria Grenoble - Rhône-Alpes, France.
Content
Part I: Emergence of Standard Algorithms.- What's Behind Blast.- Forty Years of Model-Based Phylogeography.- How to Infer Ancestral Genome Features by Parsimony.- Duplication, Rearrangement and Reconciliation.- The Genesis of the DCJ Formula.- Part II: New Lights on Current Paradigms.- Large-Scale Multiple Sequence Alignment and Phylogeny Estimation.- Rearrangements in Phylogenetic Inference.- Status of Research on Insertion and Deletion Variations in the Human Population.- A Retrospective on Genomic Preprocessing for Comparative Genomics.- A Comparison of DCJ and Algebraic Distances.- Part III: Promising Directions.- Fractionation, Rearrangement, Consolidation, Reconstruction.- Error Detection and Correction of Gene Trees.- The Potential of Family-Free Genome Comparison.- Genetic History of Populations.