Phylogenomics

Edited by:   Igor Mokrousov (Head of the Laboratory of Molecular Epidemiology and Evolutionary Genetics St. Petersburg Pasteur Institute Russia), Egor Shitikov (Head of the Laboratory of Molecular Genetics of Microorganisms, Federal Research and Clinical Center of Physical-Chemical Medicine, Russia)
Imprint:   Academic Press Inc
Country of Publication:   United Kingdom
Dimensions:   Height: 234mm,  Width: 191mm, 
ISBN:   9780323998864
ISBN 10:   0323998860
Pages:   520
Publication Date:   01 May 2024
Audience:   Professional and scholarly ,  Undergraduate
Format:   Paperback
Publisher's Status:   Active

Igor Mokrousov, 53 years old, PhD, DSc, is the Head of Laboratory of Molecular Epidemiology and Evolutionary Genetics at St. Petersburg Pasteur Institute, Russia. His research interests include study of evolution, phylogenomics, and molecular epidemiology of tuberculosis; phylogeography of Mycobacterium tuberculosis and co-evolution with humans; molecular mechanisms and genotypic detection of drug resistance. His current projects focus on the application of next-generation sequencing for genome-wide analysis to understand the pathogenetic characteristics and evolutionary trajectory of various M. tuberculosis lineages and emerging clones. Dr Mokrousov made a recognized contribution to the study of human-M. tuberculosis coevolution and put forward a hypothesis that evolutionary histories of H. sapiens and human pathogens are comirrored and coshaped. He proposed a new simple measure of genetic distance between geographic populations within a microbial species based on the observed difference in the frequencies of its genotypes. Egor Shitikov, PhD, is the Head of Laboratory of Molecular Genetics of Microorganisms at Federal Research and Clinical Center of Physical-Chemical Medicine, Russia. His research interests are closely related to the systematic analysis of Mycobacterium tuberculosis: phylogenetic relationship of various strains, their definition and classification; the search for new drug-resistance determinants, virulence factors, and pathogenicity; development of rapid screening systems for certain pathogen groups; experiments on model organisms. The current topic focuses on the deep machine learning methods in Mycobacterium tuberculosis genomics for the building of an open platform for the analysis of the pathogen’s evolutionary signatures.