School of Computing Seminar

Fridays 2:30-3:30 pm
McAdams 114

JANUARY 18, 2019

Alex Feltus
Clemson University
Genetics and Biochemistry Department


Mining GEMs for Genes at the Petascale

Abstract:

The Feltus group is focused on discovering genes underlying complex traits in plants and animals. Our core data type, the Gene Expression Matrices (GEM), is a mix of in house genetic data with datasets from public sources including The National Center for Biotechnology Information (NCBI), Genotype-Tissue Expression (GTEx), The Cancer Genome Atlas (TCGA), and Library of Integrated Network-Based Cellular Signatures (LINCS). Then we detect gene dependencies using various genome analytic approaches coded in house or from the open source genomics community. Since we operate at the tera- to petascale, we also develop cyberinfrastructure software and systems that are required to do our work. This presentation will describe computational biology problems we are trying to solve and the democratized computer systems we use to solve them: Palmetto cluster, Open Science Grid, The National Resource Platform, Internet2, and our distributed cloud computing project SciDAS (Scientific Data Analysis at Scale).

Bio:

Dr. F. Alex Feltus received a B.Sc. in Biochemistry from Auburn University in 1992, served two years in the Peace Corps in Fiji, and then completed advanced training in biomedical sciences at Vanderbilt and Emory. He has performed research in bioinformatics, cyberinfrastructure, high-performance computing, network biology, tumor biology, agrigenomics, genome assembly, systems genetics, paleogenomics, and bioenergy feedstock genetics. Currently, Feltus is Professor of Clemson University’s Dept. of Genetics & Biochemistry and CEO of Allele Systems LLC. Feltus has published numerous scientific articles in peer-reviewed journals, released open source software, and taught undergrad and PhD students in bioinformatics, biochemistry, and genetics. He is funded by multiple NSF grants and is engaged in tethering together smart people from diverse technical backgrounds to propel genomics research from the Excel-scale into the Exascale.

 

 

 

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