Funded by the USDA NIFA IDEAS program, this projects seeks to create a time-based summary measure of dairy cow health using transcriptomics, the microbiome and trait data that can be used to assess the comparative importance of diseases and injuries affecting animal wellbeing and economic losses across dairy populations.
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The Ficklin Research Program
About
The Ficklin research program in the Dept. of Horticulture at Washington State University is a computational dry lab dedicated to the creation of software tools, computational approaches and systems-level models that address basic and applied hypothesis at the molecular-level of agricultural systems.
Areas of Focus
- Biosignature discovery: identification of dynamic molecular markers (i.e., gene expression, metabolite abundance) for environmentally controlled traits in plants using machine learning.
- Machine-learning and image processing for automated physiological trait rating in horticultural crops.
- Systems genetics: using multiomic networks to link the molecular underpinnings to traits of interest.
- Community biological database development using the Tripal software.
- Whole genome assembly and annotation.
Stephen P. Ficklin, Ph.D.
Associate Professor, Dept of Horticulture, Washington State University

Contact Information
Office: Plant Science Building #403A
Phone: (509) 335-4295
Email: stephen.ficklin@wsu.edu
Department of Horticulture
Washington State University
PO Box 646414
Pullman, WA 99164-6414
Educational Background
- Ph.D. Plant and Environmental Sciences, Clemson University (2013)
- M.S. Computer Science, Clemson University (2003)
- B.S. Computer Science, Brigham Young University (2000)
Summary Measures of Health for Dairy Cattle
NRSP10: National Database Resources for Crop Genomics, Genetics and Breeding Research
NRSP10 (https://www.nrsp10.org/) is one of seven National Research Support Project (NRSP) funded by the State Agricultural Experiment Stations (SAES) from the Hatch Multistate Research Fund (MRF) provided by the National Institute for Food and Agriculture (NIFA). The mission is to establish a robust, dynamic, and widely available genomics, genetics and breeding online database platform as a resource for crops of national significance that are currently underserved (Citrus, Cool Season Food Legumes, Cotton, Rosaceae, and Vaccinium), that is flexible enough to be readily implemented for other crops and organisms valuable to U.S. agriculture. The role of our program is to provide core development support and outreach for Tripal (http://tripal.info)
Analysis of the Antagonistic and Mutualistic Interactions Within Potato, Protist & Virus
Awarded jointly by the NSF and USDA , this project seeks to explore the mutualistic relationship between the soil borne Spongospora subterranea f. sp. subterranea (a protist parasite), and the potato mop-top virus (PMTV) as they antagonistically interact with potato plants. A systems-level time-series analysis will be performed to identify candidate gene sets that underlie disease susceptibility, resistance and mutualism.
Assessment of smoke taint risk in vineyards exposed to smoke from wildfires
Funded by the Washington State Department of Agriculture Specialty Block Program, this project addresses the grape and wine industry's need for methods that assess the risk to grape and wine quality associated with vineyard exposure to smoke from wildfires.
Apple genomes for postharvest fruit quality biomarkers
This project funded by the Washington Tree Fruit Research Commission seeks to develop tools for identification of postharvest biomarkers in apple fruit that assess response to storage conditions and predict risk for disorders or loss of quality.
"Big Data" Tree Crop Cyberinfrastructure
Standards and Cyberinfrastructure that Enable "Big-Data" Driven Discovery for Tree Crop Research is a project funded by the US National Science Foundation (award #1444573) to develop standards and infrastucture for the integration of high quality, curated, phenotypic and genotypic data with geo-location and environmental data. This project will both leverage and coordinate funded efforts to enhance or update tree crop databases (Genome Database for Rosaceae, Citrus Genome Database, TreeGene and Hardwood Genomics Web) to Tripal that will support cross-site communication, adoption of existing standards, and "big data" integration and analysis.
Precision Dairying: Transcriptomics/Phenomics Pilot Project
Funded by a Livestock Health and Food Security internal grant by the College of Veterinary Medicine (CVM) and the College of Agricultural, Human and Natural Resources (CAHNRS) at WSU, the Precision Dairying project seeks to apply high-throughput data collection technologies in transcriptomics and phenomics to identify biomarkers predictive of animal health.
Scientific Data at Scale (SciDAS)
SciDAS is a multi-institutional project funded by the National Science Foundation (award #1659300). The goal for SciDAS is to provide advanced cyberinfrastructure to support the creation of a National-level distributed compute infrastructure for the efficient injection of data and workflows compute environments. The Ficklin Lab is responsible for working with the project team to develop a Systems-Biology use case for large-scale development of gene co-expression networks across the tree of life. The project also contains a Tripal component to integrate Tripal sites with the SciDAS infrastructure. See the official SciDAS home page for more information.
Lentil Transcriptomics/Phenomics Pilot Project
Funded by the College of Agricultural, Human and Natural Resources (CAHNRS) at WSU, the Lentil Emerging Issues Pilot project explores integration of transcriptomic, phenomics and phenotype data using a systems genetic approach to explore new protocols for identification of disease resistance biomarkers in Lentil.
Tripal Gateway
The Tripal Gateway Project is a US National Science Foundation (NSF) Funded (award #1443040) project designed to create infrastructure to support two important needs within the Tripal community: data exchange and big data analysis. Modern sequencing technologies have expanded the need for workflow-based analytics to meet the demands of community expectations. The ability to move data between the community database and the high performance computing cluster is critical for meeting performance expectations. The Tripal Gateway Project attempts to meet these needs through the addition of RESTful web services to Tripal, second, integration of Tripal with Galaxy such that Tripal sites can provide analytical workflows to their users, and third development and exploration of methods to improve data transfer between Tripal sites and computing centers where Galaxy jobs are executed.
People
The Ficklin Lab comprises full time technical and research staff, postdocs, graduate students and undergraduate students. Current members of the Ficklin Lab are listed below in alphabetical order.Active
P. Layton Ashmore

Application of data science to identify biomarkers from large untargeted masspec datasets for wildfire smoke-related compounds in wine grapes. Co-advised by Tom Collins.
Zach Hall

Data analytics, biomarker development
Abdur-Rahman Muhammad

Developer of Granny, a machine learning tools for pome fruit trait ratings.
Joel Alejandro Velasco

Identification of genes underlying root rot (Aphanomyces) resistance in lentils
Huiting Zhang

Use of functional genomics and bioinformatics to explore the genetic control of post-harvest fruit quality of pome fruits. Works in both the Ficklin and Honnas research programs.
Alumni
Tyler Biggs

Sean Buehler

Tripal v3 and v4 Core Development and Tripal Help Desk support.
Mitchell Greer

C++, CUDA, OpenCL Developer. Assisted in Development of KINC.
John Hadish

Exploration of improved computational methods towards development of biosignatures for post-harvest fruit quality in apples. Lead developer of GEMmaker.
Matt McGowan

Noise reduction strategies, Network & GWAS integration, condition-specific subnetworks. Works in both the Ficklin and Zhang research programs.
Nhan Nguyen

Lead Developer of Granny, a machine learning tools for pome fruit trait ratings.
Sai Oruganti Sai Prakash

Top-down metabolic networks construction for identification of condition-specific interactions and integration with gene expression data.
Risharde Ramnath

Tripal v3 and v4 Core Development and Tripal Help Desk support.
Yue Shang

Researches chemical composition changes in smoke tainted grape and wine using GC-MS and Q-TOF. Works in the Collins lab, co-advised in the Ficklin lab.
Brian Soto

Software developer (PHP, JavaScript, Python) working on the Tripal Galaxy Module and blend4php
Shawna Spoor

Works on the Tripal v3 and the Tripal Gateway Project, including development and outreach. Highly experienced Drupal developer.
Fabiola Ramirez Torres

Gene editing techniques
Connor Wytko

Software developer (PHP, JavaScript, Python) working on the Tripal Galaxy Module and blend4php
Publications
The Ficklin Lab in the Department of Horticulture at WSU began in July of 2015. The following is a list of peer-reviewed publications with lab members as primary or as co-author since 2015.
2024
- Hadish JA, Hargarten HL, Zhang H, Mattheis JP, Ficklin SP, Honaas LA. Transcriptomics of long-term, low oxygen storage coupled with ethylene signaling interference suggests neofunctionalization of hypoxia response pathways in apple (Malus domestica) (2024) Plant Direct. 8(12), e70025
- Yocca A, Akinyuwa M, Bailey N, Cliver B, Estes H, Guillemette A, Hasannin O, Hutchison J, Jenkins W, Kaur I, Khanna RR, Loftin M, Lopes L, Moore-Pollard E, Olofintila O, Oyebode GO, Patel J, Thapa P, Waldinger M, Zhang J, Zhang Q, Goertzen L, Carey SB, Hargarten H, Mattheis J, Zhang H, Jones T, Boston L, Grimwood J, Ficklin S, Honaas L, Harkess A. A chromosome-scale assembly for 'd'Anjou' pear (2024) (2024) G3: Genes, Genomes, Genetics. Jan 8:jkae003
- Hadish JA, Hargarten HL, Zhang H, Honaas LA, Ficklin SP. Towards Identification of Postharvest Fruit Quality Transcriptomic Markers in Malus domestica (2024) PLoS ONE. 19(3): e0297015
- Zhang H, Ko I, Eaker A, Haney S, Khuu N, Ryan K, Appleby AB, Hoffman B, Landis H, Pierro K, Wilsea N, Hargarten H, Yocca A, Harkess A, Honaas L, Ficklin SP. A Phased, Chromosome-scale Genome for Malus domestica ‘WA 38’ (2024) G3:Genes|Genomes|Genetic. Sept 17:jkae222
- Nguyen N, Michaud J, Mogollon R, Hargarten H, Leisso R, Torres C, Zhang H, Honaas LA, Ficklin SP. Rating Postharvest Pome Fruit Quality Using Deep Learning and Image Processing (2024) Plant Direct. e70005
2023
- Clark JL, Cooper L, Poelchau M, Berardini T, Elser J, Farmer A, Ficklin S, Kumari S, Laporte MA, Nelson RT, Sadohara R, Selby P, Thessen A, Whitehead B, Sen T. Data Sharing and Ontology use among Agricultural Genetics, Genomics, and Breeding Databases and Resources of the AgBioData Consortium (2023) Oxford Database. baad076
2022
- Marzougui A, Rajendran A, Mattinson DS, Ma Y, McGee RJ, Garcia-Perez M, Ficklin SP, Sankaran, S.. Evaluation of biogenic markers-based phenotyping for resistance to Aphanomyces root rot in field pea. (2022) Information Processing in Agriculture. 9(1), 1–10
- Hang Y, Burns J, Shealy, BT, Pauly R, Ficklin SP, Feltus FA. Identification of condition-specific regulatory mechanisms in normal and cancerous human lung tissue (2022) BMC Genomics. 23:350
- Hadish JA, Biggs, T D, Shealy, BT, Bender MR, McKnight CB, Wytko C, Smith MC, Feltus FA, Honaas L, Ficklin SP. GEMmaker: process massive RNA-seq datasets on heterogeneous computational infrastructure (2022) BMC Bioinformatics. 23:156
2021
- Burns JJR, Shealy BT, Greer MS, Hadish JA, McGowan MT, Biggs T, Smith MC, Feltus FA, Ficklin SP. . Addressing Bias in Multidimensional Gene Expression Datasets for Co-Expression Networks (2021) Briefings in Bioinformatics. bbab495
- Staton M, Cannon E, Sanderson LA, Wegrzyn J, Anderson T, Buehler S, Cobo I, Faaberg K, Grau E, Guignon V, Gunoskey J, Inderski B, Jung S, Lager K, Main D, Poelchau M, Ramnath R, Richter P, West J, Ficklin S. Tripal, a community update after 10 years of supporting open source, standards-based genetic, genomic, and breeding databases (2021) (2021) Briefings in Bioinformatics. bbab238
- Honaas L, Hargarten H, Hadish J, Ficklin SP, Serra S, Musacchi S, Wafula E, Mattheis J, DePamphilis CW and Rudell D. Transcriptomics of differential ripening in ‘d’Anjou pear (Pyrus communis L.) (2021) Frontiers in Plant Science. 12
- McGowan MT, Zhang Z, Ficklin SP. Chromosomal Characteristics of Salt Stress Heritable Gene Expression in the Rice Genome (2021) BMC Genomic Data. 22:17
- Marzougui A, Rajendran A, Mattison S, Ma Y, McGee R, Garcia-Perez M, Ficklin S, Sankaran S. Evaluation of biogenic markers-based phenotyping for resistance to Aphanomyces root rot in field pea (2021) Information Processing in Agriculture. 2021
- Ogle C, Reddick D, McKnight C, Biggs T, Ficklin SP, Pauly R, Shannigrahi1 S, Feltus FA. Named data networking for genomics data management and integrated workflows (2021) Frontiers in Big Data. 4
2020
- McConnel C, Crisp S, Biggs T, Parrish LM, Sischo WM, Adams-Progar A, Ficklin SP. Transcriptomic analysis of peripheral leukocytes in dairy cows with and without evidence of early postpartum disease (2020) Applied Animal Science. 36:6, pps 784-798
- McConnel C, Sierra C, Biggs T, Ficklin S, Lindsay P, Trombetta S, William S, Adams-Progar A. A fixed cohort field study of gene expression in circulating leukocytes from dairy cows with and without mastitis (2020) Frontiers in Veterinary Science. 7:559279
- Spoor S, Wytko C, Soto S, Chen M, Almasaeed A, Condon B, Herndon N, Hough H, Sook J, Staton M, Wegrzyn J, Main D, Feltus F, Ficklin SP. Execution of analytical workflows on community databases: integration of Tripal and Galaxy (2020) Oxford Database. baaa032
- Staton M, Addo-Quaye C, Cannon N, Yu J, Zhebentyayeva T, Huff M, Islam-Faridi N, Fan S, Georgi L, Nelson CD, Bellis E, Fitzsimmons S, Henry N, Drautz-Moses D, Noorai R, Ficklin S, Saski C, Mandal M, Wagnar T, Zembower N, Bodenes C, Holliday J, Westbrook J, Lasky J, Hebard F, Schuster S, Abbot A, Carlson JE. A reference genome assembly and adaptive trait analysis of Castanea mollissima 'Vanuxem', a source of resistance to chestnut blight in restoration breeding (2020) Tree Genetics & Genomes. 16:57
- Ma Y, Marzougui A, Coyne CJ, Sankaran S, Main D, Porter LD, Mugabe E, Smitchger J, Zhang C, Amin MN, Rasheed N, Ficklin S, McGee RJ. Dissecting the genetic architecture of Aphanomyces root rot resistance in lentil by QTL mapping and genome-wide association stud (2020) International Journal of Molecular Sciences. 21:6
- Condon B, Almsaeed A, Buehler S, Childers S, Ficklin S, Staton, M, Poelchau, M. Tripal Eutils - A Tripal module to increase exchange and reuse of genome assembly metadata (2020) Oxford Database. baz143
2019
- Buble K, Jung S, Humann JL, Yu J, Cheng CH, Lee T, Ficklin SP, Hough H, Condon B, Staton ME, Wegrzyn JL, Main D. A tool for interactive visualization and comparison of genetic maps (2019) Oxford Database. baz100
- Shealey BT, Burns J, Feltus FA, Smith M, Ficklin SP. GPU Implementation of Pairwise Gaussian Mixture Models for Multi-Modal Gene Co-Expression Networks (2019) IEEE Access. 7, 160845-160857
- Humann JL, Lee T, Ficklin S, Main D. Structural and Functional Annotation of Eukaryotic Genomes with GenSAS (2019) Gene Prediction - Methods in Molecular Biology. vol1962. Humana, New York, NYKollmar M. (eds)
- Spoor S, Cheng CH, Sanderson LA, Condon B, Almasaeed A, Chen M, Bretaudeau A, Rache H, Jung S, Main D, Bett K, Staton M, Wegrzyn JL, Feltus FA, Ficklin SP. Tripal v3: an ontology-based toolkit for construction of FAIR biological community databases (2019) Oxford Database. baz077
- Wegrzyn JL, Staton M, Street N, Main D, Grau E, Herndon N, Buehler S, Falk T, Zaman S, Ramnath R, Richter P, Sun L, Condon B, Almasaeed A, Chen M, Mannapperuma C, Jung S, Ficklin S. . Cyberinfrastructure to improve forest health and productivity: the role of tree databases in connecting genomes, phenomes, and the environment (2019) Frontiers in Plant Science, section Plant Biotechnologyc. 10:813
- Honaas LA, Hargarten HL, Ficklin SP, Hadish JA, Wafula E, Mattheis JP, Rudell DR. Co-expression networks provide insights into molecular mechanisms of postharvest temperature modulation of apple fruit to reduce superficial scald (2019) Postharvest Biology and Technology. 149, 27-41
2018
- Zhang X, Khanal U, Zhao X, Ficklin S. Making sense of performance in in-memory computing frameworks for scientific data analysis: A case study of the spark system (2018) Journal of Parallel and Distributed Computing. 120, 369-382
- Linge CS, Antanaviciute L, Abdelghafar A, Arús P, Bassi D, Rossini L, Ficklin S, Gasic K. High-density multi-population consensus genetic linkage map for peach (2018) PLoS One. 13, e0207724.
- Jung S, Lee T, Cheng CH, Buble K, Zheng P, Yu J, Humann J, Ficklin SP, Gasic K, Scott K, et. al.. 15 years of GDR: New data and functionality in the Genome Database for Rosaceae (2018) Nucleic Acids Research. 47, D1137-D1145
- Harper L, Campbell J, Cannon EKS, Jung S, Poelchau M, Walls R, Andorf C, Arnaud E, Berardini TZ, Birkett C, Cannon S, Carson J, Condon B, Cooper L, Dunn N, Elsik CG, Farmer A, Ficklin SP, et. al. . AgBioData consortium recommendations for sustainable genomics and genetics databases for agriculture (2018) Database. bay088
- Falk T, Herndon N, Grau E, Buehler S, Richter P, Zaman S, Baker EM, Ramnath R, Ficklin S, Staton M, Feltus FA, Jung S, Main D, Wegrzyn JL. Growing and cultivating the forest genomics database, TreeGenes (2018) Database. 2018, 1-11
- Dunwoodie LJ, Poehlman WL, Ficklin SP, Feltus FA. Discovery and validation of a glioblastoma co-expressed gene module (2018) Oncotarget. 9, 10995-11008
2017
- Jung S, Taein L, Cheng CH, Ficklin SP, Yu J, Humann J, Main D. Extension modules for storage, visualization and querying of genomic, genetic and breeding data in Tripal databases (2017) Database . 2017:bax092
- Wytko C, Soto B, Ficklin SP . blend4php: a PHP API for galaxy (2017) Database. baw154
- Chen M, Henry N, Almsaeed A, Zhou X, Wegrzyn J, Ficklin S, Staton M. New extension software modules to enhance searching and display of transcriptome data in Tripal databases. (2017) Database. bax052
- Zhang X, Khanal K, Zhao X, Ficklin SP. Making sense of performance in in-memory computing frameworks for scientific data analysis: A case study of the spark system (2017) Journal of Parallel and Distributed Computing. 2017
- Ficklin SP, Dunwoodie LJ, Poehlman WL, Watson C, Roche KE, Feltus FA. Discovering Condition-Specific Gene Co-Expression Patterns Using Gaussian Mixture Models: A Cancer Case Study (2017) Scientific Reports. 7:8617
2016
- Zhang X, Khanal U, Zhao X, Ficklin SP. Understanding software platforms for in-memory scientific data analysis: a case study of the spark system (2016) Proceedings of the IEEE 22nd International Conference on Parallel and Distributed Systems (ICPADS). Wuhan, China. Dec 13-16 2016
- Jung S, Lee T, Ficklin S, Yu J, Cheng CH, Main D. Chado Use Case: Storing Genomic, Genetic and Breeding Data of Rosaceae and Gossypum Crops in Chado (2016) Database. 2016:baw010
- Wang Y, Ficklin SP, Wang X, Feltus FA, Paterson AH. Large-scale gene relocations following an ancient genome triplication associated with the diversification of core eudicots (2016) PLoS One. 11(5): e0155637
- Zhang X, Khanal U, Zhao X, Ficklin SP. Understanding Software Platforms for In-memory Scientific Data Analysis: A Case Study of the Spark System (2016) IEEE 22nd International Conference on Parallel and Distributed Systems (ICPADS). Wuhan, China. Dec 13-16 2016
2015
- 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 (2015) BMC Genomics. 16:155
Software
The Ficklin lab actively develops software that implements new approaches for Systems Genetics and the Tripal database platform. A list of these software packages is provided below.ACE
The Accelerated Computational Engine (ACE) is a C++ library that provides a generic interface for construction of analytical tools. It provides a common interface for GPU utilization, visualization using the Qt package, and multi-node execution using OpenMPI. ACE provides an open file format for all output files that supports meta-data and provenance. ACE was created as the base for KINC, but can be used for any scientific application.
blend4php

blend4php is a PHP library that interacts directly with the Galaxy Project API. This tools was developed for use by the Tripal Galaxy Module, but was designed to be independent to allow anyone with a PHP-based site to directly interact with workflows housed in Galaxy. The blend4php package will allow a site to add, modify and launch workflows, view and download histories, create datasets and more.
FUNC-E
FUNC-E provides a DAVID-style command-line tool for functional enrichment of gene sets. It performs Fisher's test, multiple-testing correction, and KAPPA statistics for term clustering. FUNC-E allows a user to provide their own genome background and annotation sets.
GEMmaker

GEMmaker is a Nextflow workflow for large-scale gene expression sample processing, expression-level quantification and Gene Expression Matrix (GEM) construction. Results from GEMmaker are useful for differential gene expression (DGE) and gene co-expression network (GCN) analyses. The GEMmaker workflow currently supports Illumina RNA-seq datasets.
GSForge
GSForge is a Python software package that assists researchers through use of data management, visualization and machine learning approaches in the selection of gene sets with potential association to an experimental condition or phenotypic trait, which offers new potential hypotheses for gene-trait causality.
KINC

The Knowledge Independent Network Construction (KINC) package generates gene co-expression networks using Pearson and Spearman and Mutual Information, employs Random Matrix Theory (RMT) for automated network thresholding and optionally employs Gaussian Mixture Models (GMMs) to identify potential condition-specific gene expression. KINC v3.0 is built off of the Accelerated Computing Engine (ACE)--another Ficklin Lab software product.
Pynome

Pynome is a product of the NSF-funded SciDAS project It is used to automate retrieval and preparation of whole genome sequences for a variety of Eukaryotic species. Pynome integrates with iRODs to prepare large-scale genomic analyticsl workflows.
Tripal

Tripal is a toolkit for construction of online biological (genetics, genomics, breeding, etc), community database, and is a member of the GMOD family of tools. Tripal v3 provides by default integration with the GMOD Chado database. Tripal is used by species and clade genome databases all over the world and boasts an active distributed community of open-source developers.
Tripal Galaxy Module

The Tripal Galaxy Module is an extension module for Tripal that integrates a Tripal-based site with the Galaxy Workflow tool. It allows a site to provide workflows to end-users and for site developers to use Galaxy workflows to power computation of complex analytical tools.
Tripal Network Module

The Tripal Network Module serves as an extension to Tripal and provides data management and visualization for biological networks stored in Tripal.
AFS 505: Topics in Computing and Analytical Methods for Scientists
Formerly a Horticulture 503 (Special Topics) course, this course offers:
- Applied computational methods for researchers processing, managing, and analyzing data in scientific and engineering fields.
- Variable-credit (1-6) course with 5-weeks per module and 1 credit per module.
- Select from non-sequential modules to meet program needs.
- General prerequisite is graduate standing in an agricultural, life environmental or economic science, or engineering. Other recommended preparation specific to individual modules.
Modules offered in the Fall
- Data Structures in R
- Data Visualization in R
- Data Wrangling in R
Modules offered in the Spring
- Programming in Python
- Data Analysis with Python
- Computing for Big Data
- AFS 505 Units 1-3 Spring 2020
- Hort 503 (Advanced Topics), Section 1 Spring 2019
- Hort 503 (Advanced Topics), Section 1 Spring 2018
Data Analysis in Systems Biology
This course offers an introduction to approaches for modeling and analysis for systems biology. Topics include
- Review of gene, protein, metabolic, and signaling systems
- Methods for modeling biological systems
- UNIX Basics
- High Performance Computing (HPC) introduction
- Graph theory for network modeling
- Network visualization
Throughout the course students work towards the generation of gene co-expression networks from RNA-seq data they select for organisms and biological functions of their own interest. These networks are constructed using HPC and existing bioinformatics tools.
- Hort 503 (Advanced Topics), Section 2 Fall 2019
- Hort 503 (Advanced Topics), Section 2 Fall 2017
- Hort 503 (Advanced Topics), Section 2 Fall 2016
Join the Lab
Graduate Studies
Graduate degrees with an emphasis on Systems Genetics and Computational Biology are available with the Ficklin lab through the Department of Horticulture and the Molecular Plant Sciences (MPS) program. Both programs offer world-class graduate-level education. Dr. Ficklin is currently looking for students interested in graduate research both at the M.S. (Horticulture) and Ph.D. levels (Horticulture and MPS). Please contact Dr. Ficklin directly to express interest.
Undergraduate Research
Undergraduate research opportunities are available for motivated students with background in computer programming. If interested, please contact Dr. Ficklin directly.
Research Staff / Postdoctoral Researchers
The Ficklin lab offers full time employment as needed by funded projects for data scientists and software developers. At times, positions are available for Research Associates (with a B.S. or M.S. degree and relevant experience) and Postoctoral Researchers. When available, these positions are posted online at WSU's career website. If you are looking to apply for an existing opening please use that site to apply. If you would like to inquire about potential employment, please contact Dr. Ficklin directly.