Institute for Advanced Computing Applications and Technologies
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Event Detail Information
Event Detail Information
IACAT Brown Bag
Juan J. Loor, head of the Mammalian NutriPhysioGenomics Lab and an associate professor of Animal Sciences and Nutritional Sciences, will give a brown bag talk outlining progress made during his IACAT fellowship collaboration with NCSA. Rosati’s pizza and water will be provided. See the title and abstract of the talk below.
Title: Visual analytics of bovine nutrigenomics datasets
Abstract: High-throughput microarray technology has provided a wealth of information on the dynamism of the transcriptome in key tissues of dairy cattle during physiologically-challenging stages of the life cycle such as the transition period from pregnancy to lactation. Together with blood and tissue biomarkers and phenotypic data (nutrient intake, milk synthesis rate and composition), an important aim in modern bovine nutritional physiology is to use these various levels of biological knowledge to generate a systems understanding of the underlying biology. The objective of this project is to develop new, innovative visual and interactive techniques for effectively studying, exploring and experimenting with the data to help form and confirm hypotheses. Technique development is focused on the use of statistical and machine learning tools and approaches in support of data driven biology that are required to underpin and enable modern Nutri-Physio-Genomics research. Five different datasets including phenotypic data and transcriptomics of mammary gland, adipose tissue and liver were used. Mammary data encompass d -30, -15, 1, 15, 30, 60, 120, 240 and 300 relative to parturition. Adipose data encompass -65, -42, -14, 1, and 14 d in cows fed control or a moderate-energy diet prepartum. Liver data encompass -65, -30, -14, 1, 14, 30, and 49 d in cows fed control, moderate-energy, or underfed energy prepartum. Specifically we are applying approaches in the following areas 1) analyzing and interrogating microarray data sets; 2) extracting quantitative features from these large and complex data sets; 3) capturing variation and linking biological processes to phenotypic traits; 4) supporting knowledge-discovery in biological data using visualization approaches. These approaches will allow researchers to extract relevant features to explain phenotypic behavior. These include the use of “small-multiples” each visually representing the distribution of the data, the display of gene expression amplification for comparison over time and tissues, and techniques that support the exploration of “what if scenarios” to produce alternative phenotypic outcomes.