Genetics and Biochemistry

Dr. Alex Feltus

Alex Feltus

Associate Professor

Ph.D. Cell Biology
2000, Vanderbilt University

Contact Information
Office: 302 Biosystems Research Complex
Phone: (864) 656-3231
Web: Gene Network Browser

Research Focus Areas
Plant Genetics and Biochemistry
Genomics, Bioinformatics, and Population Genetics


Research Activities

Systems Genetics.  Network biology techniques allow for the measurement, modeling and interpretation of gene expression relationships and gene product physical interactions on a global scale. The Feltus lab focuses on gene co-expression networks which have the power to reveal functionally-related genes based upon the simultaneous co-expression of gene pairs across hundreds to thousands of expression profiling experiments (microarray, RNAseq).  By combining high-throughput construction (Feltus et al. 2013; Gibson et al. 2013)  of co-expression networks with additional biological data (Ficklin et al. 2010; Ficklin and Feltus 2011; Spangler et al. 2012a; Feltus et al. 2013; Ficklin and Feltus 2013), we are discovering and validating genetic subsystems controlling specific biochemical pathways and quantitative phenotypes.  Our lab has been especially interested in advancing the field of systems genetics in grasses centered on sorghum, maize, and rice (Feltus 2014; Ficklin et al. 2010; Ficklin and Feltus 2013). We are also exploring how networks have been modified to meet the physiological requirements of a particular species through comparative network analysis (Ficklin and Feltus 2011).  For data mining purposes, we have created a Tripal-based System Genetics module explorer (Gene Net Engine) for the mining of biological networks:

Big Data Bioinformatics. On May 08, 2014, there were 2,447,763,407,541,605 DNA base pairs (~2.4 PetaBytes) deposited into the National Center for Biotechnology Information (NCBI) Next-Generation Sequencing (NGS) DNA sequence database.  This represents the exponential output of DNA sequencing of genes and organisms in the last 5.5 years alone.  There are 1,014,156 unique taxonomic tags (~species) in the database as well as 11,640 genomes.  These raw numbers increase on a daily basis and indicate that the study of living things is entering a new era of “Big Data” and is drifting away from the descriptive science of the last century.  Along with this wealth of biological information useful in medical, agricultural, and environmental contexts comes data management and analysis issues on the order of those experienced by particle physicists.    Through an NSF funded project, the Feltus lab is collaborating with Clemson’s Center for Excellence in Next Generation Computing to maximize transfer rates through a next-generation campus network linked to the Internet2 backbone.  A target data stream is from NCBI where we are trying to identify network bottlenecks.  We are also engaged in the development of OpenFlow-switch based software-defined networking (SDN) tools.  Furthermore, the Feltus lab is actively engaged in next-generation sequence analysis workflow development optimized for the Clemson Palmetto cluster and providing training to the Clemson community through regular Genetics & Biochemistry courses and CCIT workshops.

Paleogenomics.  The Feltus lab is studying conserved non-coding sequences (CNS), conserved DNA motifs retained after a whole genome duplication (WGD) event that occurred in the Arabidopsis lineage around 20 million years ago.  We have found evidence for a putative cis-regulatory function of the CNSs via expression analysis across nine curated tissue-enriched expression datasets: aerial tissue, flowers, leaves, roots, rosettes, seedlings, seeds, shoots, and whole plants (Spangler et al. 2012b).  We have constructed co-expression networks from these partitioned datasets and assigned CNSs to gene co-expression modules and gene regulatory networks (Spangler et al. 2012a).  Mechanistically, we suspect that some of regulatory control encoded in CNS elements is post0transcriptional at the level of mRNA decay (Spangler and Feltus 2013). These results provide evidence that CNS molecular footprints are real and have implications in understanding the functional effects of duplicating regulatory machinery after ancient polyploidy events. 

Bioenergy Feedstock Development. The Feltus lab has made significant strides in the analysis of sorghum as a bioenergy feedstock (Feltus and Vandenbrink 2012).  We have screened sorghum varieties for high hydrolysis yield potential (HYP), the maximal enzymatic conversion of biomass to sugar (Vandenbrink et al. 2010).  Using biomass conversion variability in these genetic backgrounds as a guide, we have identified secondary traits (e.g. composition, crystallinity; (Vandenbrink et al. 2011; Vandenbrink et al. 2013b).  Finally, we have begun to map genome positions controlling hydrolysis in sorghum (Vandenbrink et al. 2013a).  While our interests revolve around basic research objectives, we hope that our work will lead to new crop options for marginal lands in South Carolina and beyond. 

Plant Genomics. The Feltus lab has a long standing interest in the broad field of plant genomics. We have been an active participant in a T. cacao genome sequencing consortium in collaboration with the Clemson University Genomics Institute (CUGI;; (Feltus et al. 2011; Saski et al. 2011a; Motamayor et al. 2013).  Another plant genomics collaboration has focused on the sex determination region in the papaya and the full genome assembly (Yu et al. 2007; Ming et al. 2008; Yu et al. 2008; Na et al. 2012; Wang et al. 2012).  In addition, we heavily collaborate in grass genomics projects such as the sorghum genome assembly (Paterson et al. 2009) and switchgrass sequencing (Saski et al. 2011b).  We strive to make significant advances in plant and crop research to help improve agriculture worldwide.


Recent Publications

Clyde Felix and F. Alex Feltus Plant Stress Biomarkers from Biosimulations: The Transcriptome-To-Metabolometm (TTMtm) Technology. Effects of drought stress on rice. Plant Biology (In press), 2014.

Feltus FA Systems Genetics: A Paradigm to Improve Discovery of Candidate Genes and Mechanisms Underlying Complex Traits. Plant Science 223, 45-48, 2014.

Roger N. Hilten, Joshua P. Vandenbrink, Andrew H. Paterson, F. Alex Feltus, and Keshav C. Das. Linking isoconversional pyrolysis kinetics to compositional characteristics for multiple Sorghum bicolor genotypes. Thermochimica Acta 577, 46-52, 2014. TC#6176.

Yannick Pauchet, Christopher A. Saski, Frank A. Feltus, Isabelle Luyten, Hadi Quesneville, and David G. Heckel. Studying the organization of genes encoding plant cell wall degrading enzymes in Chrysomela tremulae provides insights into a leaf beetle genome. Insect Molecular Biology 23(3):286-300, 2014.

Nadia Shakoor, Ramesh Nair, Oswald Crasta, Geoffrey Morris, Alex Feltus and Stephen Kresovich A Sorghum bicolor expression atlas reveals dynamic genotype-specific expression profiles for vegetative tissues of grain, sweet and bioenergy sorghums. BMC Plant Biology 14(1):35, 2014.

Sanderson LA, Ficklin SP, Cheng CH, Jung S, Bett KE, Feltus FA, Main D Tripal 1.1: a Standards-based Platform for Construction of Online Genetic, Genomic and Biological Databases. Database (Oxford) doi: 10.1093/database/bat075, 2013.

Joshua P. Vandenbrink, Ryan E Hammonds, Andrew H. Paterson, KC Das, J Michael Henson, Roger N. Hilten, and F. Alex Feltus. Tissue specific analysis of hydrolysis related traits and pretreatment efficacy in the bioenergy grass Sorghum bicolor. Industrial Crops & Products 50:118-130, 2013. TC#6018.

Joshua P. Vandenbrink, Andrew H. Paterson, Lori Goff, Wenqian Kong, Huizhe Jin and F. Alex Feltus. Identification of Bioconversion Quantitative Trait Loci in the Interspecific Bioenergy Grass Cross Sorghum bicolor x Sorghum propinquum. Theoretical and Applied Genetics 126(9):2367-80, 2013. TC#6096.

Stephen P. Ficklin and F. Alex Feltus. A Systems-Genetics Approach and Data Mining Tool For the Discovery of Genes Underlying Complex Traits in Oryza Sativa. PloS ONE 8(7): e68551, 2013.

F. Alex Feltus, Stephen P. Ficklin, Scott M Gibson, and Melissa C. Smith. Maximizing Capture of Gene Co-expression Relationships Through Pre-Clustering of Input Expression Samples: An Arabidopsis Case Study. BMC Systems Biology 7:44, doi:10.1186/1752-0509-7-44, 2013