Insect genomics
Author(s)
Bibliographic Information
Insect genomics
（Entomology in focus / Series editor, Fernando L. Cônsoli, v. 3 . Short views on insect genomics and proteomics ; v. 1）
Springer, c2015
Available at 1 libraries
Note
Includes bibliographical references
Description and Table of Contents
Description
Entomology as a science of inter-depended branches like molecular entomology, insect biotechnology, has made rapid progress. This also implies that there is an urgent need to manage the available resources. In the past five decades, entomology has taken giant steps ahead. The aim of this work is to integrate perspectives across molecular and biochemistry, physiology, reproduction, developmental biology, molecular evolution, genetics and RNAi applications. This century is proclaimed as the Era of Biotechnology and it consists of all types of Mol-Bio-Gen applications, which is an essential component for a thorough understanding of the insect biology. The aim of this work is to provide the comprehensive review of recent research from various geographic areas around the world and contributing authors that are recognized experts in their respective field of Genomic entomology. This Volume emphasizes upon the need for and relevance of studying molecular aspects of entomology in Universities, Agricultural Universities and other centers of molecular research. It will also serve as a landmark source for Insect advance science technology.
Table of Contents
1. Body lice: From the genome project to functional genomics and reverse genetics.- 2. Advances in the genomics of the whitefly Bemisia tabaci: an insect pest and a virus vector.- 3. Updating genomic data of Lepidoptera.- 4. Molecular adaptations of aphid biotypes in overcoming host plant resistance.- 5. Integrative genomic approaches to studying epigenetic mechanisms of phenotypic plasticity in the aphid.- 6.
Insect regulatory genomics.- 7. Comparative genomics of transcription factor binding in Drosophila.- 8. The little known universe of short proteins in insects: A machine learning approach
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