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EYE ON CHINA
Constructing High Quality Graph-based Soybean Pan-Genome
Researchers from China explore the extensive genetic diversity among soybean germplasms through the construction of a complete pan-genome from diverse soybean accessions.

The research group led by Professor Tian Zhixi from the Institute of Genetics and Developmental Biology (IGDB) of the Chinese Academy of Sciences (CAS), in cooperation with Professors Liang Chengzhi and Zhu Baoge's team, Professor Bin's team from the Center for Excellence in Molecular Plant Sciences of CAS, Professor Huang Xuehui's team from Shanghai Normal University, and the Berry Genomics Corporation, individually de novo assembled 26 soybean genomes and constructed a high quality graph-based soybean pan-genome.

Phylogenetic analysis of 2,898 soybean accessions, the team selected 26 and performed de novo genome assembly for each accession. Overlapping DNA segments of the 26 whole-genome assemblies ranged from 18.8 to 26.8 Mb pairs with a mean of 22.6 Mb.

The scientists also conducted a comparative genome analysis of the 26 genomes including three previously reported genomes to identify any genetic anomalies as well as structural variations within the genomes.

A graph-based genome was then built using the ZH13 genome as a standard linear reference genome – integrating the structural variations.

Having a reference genome opens the door to functional genomics and molecular design breeding for a species. However, an increasing number of reports has suggested that one or a few reference genomes cannot represent the full range of genetic diversity of a species. Therefore, pan-genome construction is becoming increasingly necessary.

Further investigation revealed that these structural variations play key roles in driving genome evolution, gene structure variation and gene functional divergence, which in turn contribute to agronomic trait variations in the soybean population.

As the first reported graph-based genome of a plant, it can be used to re-analyse previously re-sequenced data thereby generating more comprehensive information. The results of this study will go on to facilitate functional study and breeding of the soybean.

 

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