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 hypotheses at the molecular-level of agricultural systems.
Associate Professor, Dept of Horticulture, Washington State University
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
Ph.D. Plant and Environmental Sciences, Clemson University (2013) M.S. Computer Science, Clemson University (2003)
Funded by the USDA NIFA IDEAS program, this project 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.
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.
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.
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.
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.
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 infrastructure 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.
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.
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.
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.
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.
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.
Undergraduate Researcher
Data analytics, biomarker development
Laboratory Technician
Molecualr biology laboratory and postharvest physiology
Undergraduate Researcher
RNA-seq library preparation in support of the smoke-tained wine project.
Horticulture PhD Student
Identification of genes underlying root rot (Aphanomyces) resistance in lentils
Research Assistant Professor
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 Honaas research programs.
Postdoctoral Researcher
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.
Postdoctoral Researcher
Undergraduate Researcher
Image analysis using Python and machine learning.
Scientific Application Web Developer
Tripal v3 and v4 Core Development and Tripal Help Desk support.
Research Associate
MPS Ph.D. Graduate 8/2023
Exploration of improved computational methods towards development of biosignatures for post-harvest fruit quality in apples. Lead developer of GEMmaker.
Undergraduate Researcher
Developer of Granny, a machine learning tool for pome fruit trait ratings.
MPS Ph.D. Graduate 5/2022
Noise reduction strategies, Network & GWAS integration, condition-specific subnetworks. Works in both the Ficklin and Zhang research programs.
Machine Learning Software Development
Lead Developer of Granny, a machine learning tool for pome fruit trait ratings.
Former Hort Ph.D. Student
Top-down metabolic networks construction for identification of condition-specific interactions and integration with gene expression data.
Scientific Application Web Developer
Tripal v3 and v4 Core Development and Tripal Help Desk support.
Hort MS Graduate 5/2023
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.
Undergraduate in EECS
Software developer (PHP, JavaScript, Python) working on the Tripal Galaxy Module and blend4php
Research Associate
Works on Tripal v3 and the Tripal Gateway Project, including development and outreach. Highly experienced Drupal developer.
Horticulture PhD Student
Gene editing techniques
Undergraduate in EECS
Software developer (PHP, JavaScript, Python) working on the Tripal Galaxy Module and blend4php
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.
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.
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 is a PHP library that interacts directly with the Galaxy Project API. This tool 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 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 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 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.
The Knowledge Independent Network Construction (KINC) package generates gene co-expression networks using Pearson, 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 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 analytics workflows.
Tripal is a toolkit for construction of online biological (genetics, genomics, breeding, etc.) community databases, 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.
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.
The Tripal Network Module serves as an extension to Tripal and provides data management and visualization for biological networks stored in Tripal.
The following courses are offered to graduate-level students by the Ficklin Lab.
Formerly a Horticulture 503 (Special Topics) course, this course offers:
Modules offered in the Fall (Instructor: David Brown, Ph.D.)
Modules offered in the Spring (Instructor: Stephen Ficklin, Ph.D.)
This course offers an introduction to approaches for modeling and analysis for systems biology. Topics include:
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.
