What is pheno-informatics?
Large-scale phenotyping, aka phenomics, is an emerging science that simultaneously characterizes many phenotypic traits of many objects above the molecular level. It promises to bridge wide gaps between genome and disease, inspiring science aimed at understanding gene function, cell development, evolution, and so on. In the Chen lab, we develop and apply phenomics tools for precision medicine and for plant science.
Dr. Isaac Kohane said, although gene sequencing was a major step forward, wringing clinical value from the flood of genomic information would depend on the more pedestrian practice of phenotyping.
New phenotyping techniques have been developed to allow high-throughput, detailed phenotyping. In madicine, the clinically characterizing traits signify health or disease, such as fever, rash, limp or an irregular heartbeat. In the realm of agriculture, they help improving yield, efficiency of nutrient-use and photosynthesis, to meet our growing needs for food and fuel in a changing climate.
While these developments are exciting, researchers are limited by the tools to fully analyze the phenomics data. Removing that limitation is the proposed goal of this research statement. To this clear need, we are designing, developing and applying phenomics data analytics solutions, or called pheno-informatics, such that big phenomics data can be transformed into knowledge or testable hypotheses to identify important genes in various aspects. The solutions will ensure high data quality, identify and visualize important genes from complex phenotype data, and will advance knowledge discovery in the broader community.
The figure in below briefly introduces the four key components the plant phenotyping workflow. First, important plants traits are captured under simulated environmental conditions. Second, phenptyping images are processed to compute various photosynthesis and growth phenotypes at the plant level. Third, leaf-level photosynthesis, movement and growth are measured using leaf alignment techniques. Fourth, temporal and spatial heterogeneity patterns in plant phenotype images are captured using advanced computer vision and statistics.
In conclusion, phenomics has broad importance in applied and basic biology. Unprecedented advances have been made in the throughput and pace of large-scale phenotyping platforms. However, our ability to find, associate and implicate the effects of the interactions of genetic variants and environmental conditions far outstrips our ability to understand them. This has imposed increasingly high demands on the pheno-informatics tools necessary for analysis of the exponentially growing phenomics data. we aim to develop, test and apply the phenomics data analytics solutions. These computational tools will turn the sophisticated phenomics data into testable hypotheses, facilitate scientific discovery on novel gene function, and thus significantly broaden the research area of phenomics.
We weclome collaborations from research groups working on sensor development, phenotyping, computer vision and biology, to solve theoratical and real world problems. Meanwhile, strength will continue to be maintained in pure computer science serving as a foundation for work in bioinformatic projects.