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Optimization and Comparative Analysis of Plant Organellar DNA Enrichment Methods Suitable for Next-generation Sequencing
Published on: July 28, 2017
Comparative genomics approach to infer ancestral cell karyotypes and reconstruct the evolutionary trajectories of
Xiyin Wang1,2,3, Bowen Song4, Weiwei Liu5
1Hebei Provincial Key Laboratory for Data Science and Application, College of Mathematics and Sciences, North China University of Science and Technology, Tangshan, China. wangxiyin@vip.sina.com.
Abstract:
Chromosomes are key structural and functional components of heredity. Reconstruction of ancestral cell karyotypes (ACKs) and evolutionary trajectories of chromosomes (CETs) can reveal how structural and functional changes in chromosomes have occurred during evolution. The whole-genome duplication integrated analysis toolkit implements a telomere-centric model on the basis of the comparative analysis of gene collinearity within and between plant genomes to reconstruct the ACKs and CETs of many angiosperm plants whose genomes have been complicated by repeated polyploidization and subsequent repatterning. Here we summarize the steps for using whole-genome duplication integrated analysis to infer the gene collinearity within a genome or between genomes and to infer the repeated polyploidization specific to a plant or common to multiple plants or plant families. In addition, we provide an example with three grass genomes. We also describe procedures to infer ancestral chromosomes at key evolutionary nodes, to reconstruct CETs from the deep past to extant plants and to generate event-related hierarchical alignment of multiple genomes, which is realized by deciphering collinear genes produced by different polyploidization or speciation events. The Protocol guides users to infer ACKs and CETs in a plant taxon and between selected plants from different taxa, which is crucial to understand important sources of genetic innovation including chromosome evolution, genome complexity and origination and evolution of duplicated genes. This Protocol requires minimal bioinformatics knowledge, for example, retrieving data from public databases and running Python programs. Completing the protocol with the example data takes around 8 h.
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