Streamline unsupervised machine learning to survey and graph indel-based haplotypes from pan-genomes.

Abstract

Authors

• Bosen Zhang
  Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99164, USA.
• Haiyan Huang
  Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99164, USA.
• Laura E Tibbs-Cortes
  USDA-ARS, Wheat Health, Genetics, and Quality Research Unit, Pullman, WA 99164, USA; USDA-ARS, Corn Insects and Crop Genetics Research Unit, Ames, IA 50011, USA.
• Adam Vanous
  USDA-ARS, Plant Introduction Research, Ames, IA 50011, USA.
• Zhiwu Zhang
  Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99164, USA.
• Karen Sanguinet
  Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99164, USA.
• Kimberly A Garland-Campbell
  Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99164, USA; USDA-ARS, Wheat Health, Genetics, and Quality Research Unit, Pullman, WA 99164, USA.
• Jianming Yu
  Department of Agronomy, Iowa State University, Ames, IA 50011, USA.
• Xianran Li
  USDA, Agricultural Research Service, Wheat Health, Genetics, and Quality Research Unit, Pullman, WA 99164, USA; Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99164, USA. Electronic address: xianran.li@usda.gov.

Keywords

External Resources