Ravi Mural
Assistant Professor
Biography
Ravi Mural is an assistant professor in the Department of Agronomy, Horticulture and Plant Sciences at South Dakota State University. His research focuses on enhancing the sustainable production of crop plants for commercial use through genomics, quantitative genetics and breeding. Currently, his work is centered on sorghum, maize, and other cereals, exploring genomics, genetics and genotype-environment interactions. He also plans to expand his research to include various millets and pulse crops in the future.
Students in the Mural Lab receive comprehensive training in quantitative genetics, genomics techniques and big data analytics. They collaborate closely with faculty members not only in plant science but also in animal science, statistics, computer science, electrical engineering and electronics engineering.
The primary objective of the Mural Lab is to bridge the gap between genotypes and phenotypes by integrating large-scale biological data, including phenomics, genomics, transcriptomics and functional annotations.
Students in the Mural Lab receive comprehensive training in quantitative genetics, genomics techniques and big data analytics. They collaborate closely with faculty members not only in plant science but also in animal science, statistics, computer science, electrical engineering and electronics engineering.
The primary objective of the Mural Lab is to bridge the gap between genotypes and phenotypes by integrating large-scale biological data, including phenomics, genomics, transcriptomics and functional annotations.
Research and Scholar Work
Areas of Research
Key research areas in the lab include:
- Utilizing quantitative genetics and genomics techniques to unravel the genetic architecture of crucial agronomic traits.
- Investigating genotype by environment interactions, trait plasticities and understanding the genetic architecture of these interactions across various traits.
- Employing high-throughput phenotyping and various sensors to collect multiple traits either simultaneously at a single time point or in a time-series manner, as well as through image-based analyses.
- Exploring sparse testing methodologies for efficient trait evaluation.
Associated Areas
