Abstract

Identification and visualization of large insertion and deletion (indel) polymorphisms, which contribute significantly to natural phenotypic variation, are challenge from a pan-genome. Here, through streamlining two unsupervised machine learning algorithms, we developed a BRIDGEcereal webapp for surveying and graphing indel-based haplotypes for genes of interest from publicly accessible pan-genomes. Over hundreds of assemblies from five major cereals were compiled. We demonstrated the potential of BRIDGEcereal in exploring natural variation with wheat candidate genes within QTLs and GWAS intervals. BRIDGEcereal is available from https://bridgecereal.scinet.usda.gov.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

* https://bridgecereal.scinet.usda.gov/

Details

Title
Streamline unsupervised machine learning to survey and graph indel-based haplotypes from pan-genomes
Author
Zhang, Bosen; Huang, Haiyan; Tibbs-Cortes, Laura E; Vanous, Adam; Zhang, Zhiwu; Sanguinet, Karen; Garland-Campbell, Kimberly A; Yu, Jianming; Li, Xianran
University/institution
Cold Spring Harbor Laboratory Press
Section
New Results
Publication year
2023
Publication date
Feb 13, 2023
Publisher
Cold Spring Harbor Laboratory Press
ISSN
2692-8205
Source type
Working Paper
Language of publication
English
ProQuest document ID
2775839696
Copyright
© 2023. This article is published under http://creativecommons.org/licenses/by-nd/4.0/ (“the License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.