Location: Plant Genetics Research
Project Number: 5070-21000-045-025-A
Project Type: Cooperative Agreement
Start Date: Oct 1, 2025
End Date: Sep 30, 2026
Objective:
Develop and validate a suite of multi-disciplinary data analysis and mining strategies and tools to discover genes and causative alleles underlying trait QTLs and desirable germplasm for breeding.
Approach:
Recently, the ARS scientists consolidated and analyzed genome sequencing data from 12,000 diverse wild and cultivated soybean accessions, and about 8,000 soybean RNA-seq datasets, millions of phenotypic data points and other -omics data including DNA methylome and small RNA sequencing data generated in ARS lab and Danforth Center and available in the public. The collaboration will develop new tools to integrative multi-disciplinary approaches with data science including AI/ML algorithm to mine the massive amount of multi-omics data for discovering new knowledge and strategies for soybean improvement. The integrative disciplinary and data driven approach has been effectively used to discover a large number of causative alleles (highly-confident candidates) for causative QTL alleles and germplasm containing beneficial alleles important for soybean
seed quality and yield traits. The collaboration will work with the community to validate the data-mining technologies and integrate the big-data technologies into their research program to facilitate modernization of their research and product development.