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Washington State University Molecular Plant Sciences

Stephen Ficklin

Assistant Professor, Department of Horticulture. Ph.D. 2013, Clemson University


Links:  Lab Website


Development of models for gene-product interactions is of major importance to agricultural research. A better understanding of how gene products interact within a plant system can have major impacts on our basic understanding of plant molecular biology as well as development of biotechnologies that can improve traits of importance in agriculture. Systems Genetics is an area of Systems Biology that employs statistics, computation, graph theory, information theory, and cyber-infrastructure development to analyze high throughput genomics, genetics and phenotype data in an effort to unravel genotype-phenotype relationships. This top-down approach attempts to create models (or graphs) of gene-product interactions which can be mined for groups of interconnected genes. Interconnected genes tend to be involved in similar biological functions. Associating groups of genes with genetic data hypothetically link these genes to expression of given traits. The models, therefore, are intended to support new hypothesis as to genes underlying complex traits. Additionally, with ever increasing quantities of data, and with the need for ever larger datasets to unravel the genotype-phenotype relationships, development of cyber-infrastructure is important for systems-genetics work. My research is, therefore, focused in three areas:

1) Improvement of the sensitivity and specificity of the models. This requires use of new statistical and computation methods to improve algorithms.
2) Through use of systems-genetics models, develop sets of gene targets of interest to breeders that can be used for crop improvement.
3) Creation of cyberinfrastructure to improve access and use of systems-genetics datasets. Specifically, to expand Tripal (, an the open-source toolkit for creation of online genomic and genetic databases (of which I serve as a lead developer), to support systems genetics data sets.

Selected Publications

1. Bassil NV, Davis TM, Zhang H, Ficklin S, Mittmann M, Webster T, et al. Development and preliminary evaluation of a 90 K Axiom® SNP array for the allo-octoploid cultivated strawberry Fragaria × ananassa. BMC Genomics. 2015;16:155.
2. Jung S, Ficklin SP, Lee T, Cheng CH, Blenda A, Zheng P, et al. The Genome Database for Rosaceae (GDR): year 10 update. Nucleic Acids Res. 2014;42(Database issue):D1237-44. doi: 10.1093/nar/gkt1012.
3. Yu J, Jung S, Cheng CH, Ficklin SP, Lee T, Zheng P, et al. CottonGen: a genomics, genetics and breeding database for cotton research. Nucleic Acids Res. 2014;42(Database issue):D1229-36. doi: 10.1093/nar/gkt1064.
4. Feltus FA, Ficklin SP, Gibson SM, Smith MC. Maximizing capture of gene co-expression relationships through pre-clustering of input expression samples: an Arabidopsis case study. BMC Syst Biol. 2013;7:44.
5. Ficklin SP, Feltus FA. A systems-genetics approach and data mining tool to assist in the discovery of genes underlying complex traits in Oryza sativa. PLoS One. 2013;8(7):e68551.
6. Gibson SM, Ficklin SP, Isaacson S, Luo F, Feltus FA, Smith MC. Massive-scale gene co-expression network construction and robustness testing using random matrix theory. PLoS One. 2013;8(2):e55871.
7. Sanderson LA, Ficklin SP, Cheng CH, Jung S, Feltus FA, Bett KE, et al. Tripal v1.1: a standards-based toolkit for construction of online genetic and genomic databases. Database (Oxford). 2013;2013:bat075.
8. Verde I, Abbott AG, Scalabrin S, Jung S, Shu S, Marroni F, et al. The high-quality draft genome of peach (Prunus persica) identifies unique patterns of genetic diversity, domestication and genome evolution. Nat Genet. 2013;45(5):487-94.
9. Barakat A, Staton M, Cheng C-H, Park J, Yassin NBM, Ficklin S, et al. Chestnut resistance to the blight disease: insights from transcriptome analysis. Bmc Plant Biology. 2012;12.
10. Peace C, Bassil N, Main D, Ficklin S, Rosyara UR, Stegmeir T, et al. Development and evaluation of a genome-wide 6K SNP array for diploid sweet cherry and tetraploid sour cherry. PLoS One. 2012;7(12):e48305.
11. Spangler JB, Ficklin SP, Luo F, Freeling M, Feltus FA. Conserved non-coding regulatory signatures in Arabidopsis co-expressed gene modules. PLoS One. 2012;7(9):e45041. doi: 10.1371/journal.pone.0045041.
12. Wegrzyn JL, Main D, Figueroa B, Choi M, Yu J, Neale DB, et al. Uniform standards for genome databases in forest and fruit trees (vol 5, pg 549, 2012). Tree Genetics & Genomes. 2012;8(4):941.
13. Feltus FA, Saski CA, Mockaitis K, Haiminen N, Parida L, Smith Z, et al. Sequencing of a QTL-rich region of the Theobroma cacao genome using pooled BACs and the identification of trait specific candidate genes. BMC Genomics. 2011;12:379.
14. Ficklin SP, Feltus FA. Gene coexpression network alignment and conservation of gene modules between two grass species: maize and rice. Plant Physiol. 2011;156(3):1244-56.
15. Ficklin SP, Sanderson LA, Cheng CH, Staton ME, Lee T, Cho IH, et al. Tripal: a construction toolkit for online genome databases. Database (Oxford). 2011; bar044.
16. Saski CA, Feltus FA, Staton ME, Blackmon BP, Ficklin SP, Kuhn DN, et al. A genetically anchored physical framework for Theobroma cacao cv. Matina 1-6. BMC Genomics. 2011;12:413.
17. Wang Y, Wang X, Tang H, Tan X, Ficklin SP, Feltus FA, et al. Modes of gene duplication contribute differently to genetic novelty and redundancy, but show parallels across divergent angiosperms. PLoS One. 2011;6(12):e28150.
18. Ficklin SP, Luo F, Feltus FA. The association of multiple interacting genes with specific phenotypes in rice using gene coexpression networks. Plant Physiol. 2010;154(1):13-24.