4.7 Article

A Collinearity-Incorporating Homology Inference Strategy for Connecting Emerging Assemblies in the Triticeae Tribe as a Pilot Practice in the Plant Pangenomic Era

Journal

MOLECULAR PLANT
Volume 13, Issue 12, Pages 1694-1708

Publisher

CELL PRESS
DOI: 10.1016/j.molp.2020.09.019

Keywords

homology inference; collinearity; database; pangenome; Triticeae tribe; polyploid

Funding

  1. Major Program of the National Natural Science Foundation of China [31991210]
  2. National Natural Science Foundation of China [31701415]

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Plant genome sequencing has dramatically increased, and some species even have multiple high-quality reference versions. Demands for Glade-specific homology inference and analysis have increased in the pangenomic era. Here we present a novel method, GeneTribe (https://chenym1.github.io/genetribe/), for homology inference among genetically similar genomes that incorporates gene collinearity and shows better performance than traditional sequence-similarity-based methods in terms of accuracy and scalability. The Triticeae tribe is a typical allopolyploid-rich Glade with complex species relationships that includes many important crops, such as wheat, barley, and rye. We built Triticeae-GeneTribe (https://wheat.cau.edu.cn/TGT/), a homology database, by integrating 12 Triticeae genomes and 3 outgroup model genomes and implemented versatile analysis and visualization functions. With macrocollinearity analysis, we were able to construct a refined model illustrating the structural rearrangements of the 4A-5A-7B chromosomes in wheat as two major translocation events. With collinearity analysis at both the macro- and microscale, we illustrated the complex evolutionary history of homologs of the wheat vernalization gene Vrn2, which evolved as a combined result of genome translocation, duplication, and polyploidization and gene loss events. Our work provides a useful practice for connecting emerging genome assemblies, with awareness of the extensive polyploidy in plants, and will help researchers efficiently exploit genome sequence resources.

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