|ZHANG, BOSEN - Washington State University|
|HUANG, HAIYAN - Washington State University|
|ZHANG, ZHIWU - Washington State University|
|SANGUINET, KAREN - Washington State University|
|YU, JIANMING - Iowa State University|
Submitted to: Molecular Plant
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/15/2023
Publication Date: 6/5/2023
Citation: Zhang, B., Huang, H., Tibbs, L.E., Zhang, Z., Sanguinet, K., Vanous, A.E., Garland Campbell, K.A., Yu, J., Li, X. 2023. Streamline unsupervised machine learning to survey and graph indel-based haplotypes from pan-genomes. Molecular Plant. 16(6):975-978. https://doi.org/10.1016/j.molp.2023.05.005.
Interpretive Summary: Large insertion and deletion (indel) polymorphisms contributed significantly to natural variation. Although pan-genomes with multiple high quality de novo genome assemblies are accessible, identification of large indel is a challenging bioinformatic task. This study developed a user-friendly interactive webapp, BRIDGEcereal, to efficiently mine pan-genome for large indels associated with genes of interest. Five wheat candidate genes were used as the demonstration.
Technical Abstract: Identification of large insertion and deletion (indel) polymorphisms, which contribute significantly to natural variation, is challenging from a pan-genome. Here, we introduced BRIDGE, a versatile tool, for surveying potential indels for genes of interest. BRIDGE incorporates user’s intelligent inputs into the identification process. A use-friendly webapp, BRIDGEcereal, including pan-genomes from five major cereals, was further developed. With five wheat candidate genes underlying three QTLs and one GWAS signal, we demonstrated the potential of BRIDGE in exploring natural variation.