Research Project: Integrative Applied Agricultural Genomics and Bioinformatics Research

Location: Genomics and Bioinformatics Research

2025 Annual Report

Objectives
1. Advance and accelerate translational research for ARS and its collaborators that address the agricultural needs of the Southeast region and ARS, through data generation, data integration and analysis, with an emphasis on ‘omics and machine learning approaches in crops, animals, insects, and microbiomes; support germplasm analysis for breeding and for trait genetic and molecular analyses; and support gene expression analysis and gene discovery. 1.A. Supplying bridge services in genomics and bioinformatics. 1.B. Translating standard genomic tools to outlier and non-model genetic systems. 2. Accelerate the integration of bioinformatics and advanced technologies in research, for the Southeast region and ARS, through direct project collaboration; develop and evaluate new tools, workflows, and systems that enable ARS and its collaborators to more efficiently manage, integrate, analyze, and share diverse streams of biological data and knowledge, including high throughput genotyping and phenotyping, thereby enhancing crop and animal genetic improvement, health, and nutrition. 2.A. Developing bioinformatic capacity that supports universal resource utility. 2.B. Pangenomic and phenomic data integration.

Approach
Genomic technologies are powerful tools for germplasm improvement using marker assisted selection (MAS), biotechnology, or synthetic biology, and for analyzing associated biological processes (genetics, physiology, cell and molecular biology, biochemistry, and evolutionary biology). Thus, many ARS scientists, e.g., crop and animal breeders, have a direct need for genomic tools in their research. Others, e.g., soil scientists, can enhance their research dramatically using genomic tools to analyze the microbiome, if the technologies and appropriate expertise are available. The Genomics and Bioinformatics Research Unit’s (GBRU) primary function is conducting research in the areas of bioinformatics and genomics on a wide array of species and topics. GBRU also provides collaborative assistance with various ARS project that are constrained by routine genomics or bioinformatics hurdles. Not all ARS locations have sufficient resources to support core genomic technologies. Thus, some specific roles of GBRU are to: (1) coordinate, facilitate, collaborate and conduct genomics and bioinformatics research emphasizing the Southeast region; (2) serve as a research and training resource for genomic technologies and bioinformatic analyses in support of ARS scientists and their collaborations; and (3) serve as a technical resource for ARS research programs that have not typically utilized these technologies, and aid in their development of genomic resources. Within the GBRU, this research project will conduct and collaborate on genome sequencing, sequence assembly and analysis, diversity analysis, marker development, haplotyping, physical and genetic map production, and transcription profiling research. To provide sequencing and analysis for polyploids, clonally-propagated cultivars, historically resource limited systems, and other edge cases for which standard bioinformatic protocols are problematic. In part this will be possible through exploring and advocating universal and reproducible bioinformatic approaches for researchers engaged in genome-wide or high-throughput experiments, and by connecting phenotypic information with genotypic and genomic data in a coherent way that supports germplasm utilization and gene discovery. Thus, essential product development includes new and improved reference genomes for plants, animals, insects, fish, and microbes that enable genomics assisted breeding; new physical and genetic maps; improved cultivars, germplasm, or breeding lines; and new information on key agricultural problems such as disease resistance and drought tolerance.

Progress Report
This report is the results of completing the second year of the “Integrative Applied Agricultural 'Omics and Bioinformatics Research” project. The project supports large scale genomics data generation, development and application of bioinformatics tools as well as training support across USDA-ARS. In Objective 1 – The Stoneville, MS genomics facility has continued to provided genomics services for numerous commodities including arthropods, wheat, barley, snails, cranberry, einkorn wheat, maize, okra, cotton, sugarcane, citrus, prunus, insect and fungi pests, avocado, sorghum, vanilla, sugar beet, and more. Informaticians in the unit have supported multiple programs including the following: Breeding Insight OnRamp (BI OnRamp): Provided continued support four commodity breeding programs: cotton, sugarcane, soybean, and citrus. For each commodity, support for high-throughput phenotyping, digital data management, ontology or trait design, and advancement of genotyping capabilities continues. Sugarcane work specifically has become long-term with the addition of specific funds into the in-house project starting in FY2023 that led to the development of a cross USDA initiative, the Sugarcane Integrated Breeding System. In FY25, an additional four databases were initiated and are being maintained by the unit - including ones for industry stakeholders and capacity to support germplasm collection activities. Grass Annotation: Finalized a pipeline for producing high-quality grass genome annotations that is translatable across grass species. Previous annotation work with available software produced incomplete annotations. This pipeline enables reproducible publication-quality annotation for multiple grass species. Training: The unit provided dedicated training support as requested by collaborators. Additionally, the unit supported Data Carpentry, Statistics, Virtual Research Support Core and other organized training events throughout the year as lead and supporting trainers. Multiple internal training events were organized for Field Book digital data collection application and BreedBase database software. Spearheading UTA/USDA summer internship: In FY25, twenty-one projects using advanced statistics, visual recognition, and/or advanced computer programming were developed across the Southeast Area with students and university mentors at the University of Texas Arlington. Under this program, undergraduate and graduate students are exposed to agriculture problems and then use their computational abilities to help ARS scientists to address research problems where such computational skillsets are not available in-house. The program represents a unique way to enhance ARS research while also enhancing the education of students. Objective 2 – Researchers developed new breeding software tools and maintained previously developed tools. Breeding support tools: A breeding application programming interface (BrAPI)-based Decision Support Tool was developed in the programing language R/Shiny to effectively target and optimize sugarcane breeding crosses. USDA-ARS sugarcane breeders now use this tool to optimize sugarcane crossing decisions in real time. This tool significantly improves breeding efficiency in multiple ways. For example, it synthesizes daily plant inventories, crossing records, trial data and pedigrees to recommend novel, genetically diverse crosses. The unit also designed documentation and training materials for routinely used tools. In FY25, 46 detailed protocols on BreedBase and Field Book usage were developed, and they have been viewed over 300 times by collaborators. High-throughput genotyping tool for cotton: Upon confirmation of stakeholder support to develop a public replacement for the population CottonSNP63K array, unit researchers in Raleigh, NC coordinated the development of a new high-throughput genotyping tool for. We coordinated national and international sample submission for an open-access 27K single nucleotide polymorphism (SNP) genotyping array. The project has been advanced to the final step for automation and has been moved into the cluster file finalization phase. Standardized digital phenotyping: To support phenomic data integration, the unit researchers have supported advancing standardized digital phenotyping. Researchers worked to establish and harmonize trait measurement protocols across crops to support reproducibility, interoperability, and data integration. This included defining trait ontologies, that is the definition of collected traits, using community standards (e.g., Crop Ontology, Planteome), designing consistent field scoring scales for traits like disease resistance or fruit quality, and aligning sampling methods and environmental metadata collection across institutions. The unit researchers supported the implementation of cutting-edge phenotyping technologies to improve trait accuracy and scalability. For example, it utilizes high-throughput image-based phenotyping (unmanned ariel systems (UAS) imagery, multispectral and thermal imaging), machine learning-assisted trait scoring, and sensor-based data collection (e.g., soil moisture, canopy temperature) to capture dynamic and complex trait variation across breeding populations. The unit is working to support a multi-crop, species-agnostic framework designed to empower USDA breeders and researchers to make faster, more informed decisions, ultimately leading to the development of resilient, high-performing cultivars that address the dynamic challenges of modern agriculture. This effort has culminated in the initiation of the Digital Agricultural Systems Hub (DASH).

Accomplishments
1. A standardized blueberry trait catalog for a USDA-ARS breeding effort was developed and is now publicly available. An ontology, or a catalog of traits, was developed for blueberry that represented all US public breeding efforts. This effort highlights USDA-ARS leadership in the blueberry breeding community and establishes a standard for sharing data across blueberry breeding. Fundamental tools like trait ontologies improve data integrity and shareability, which are critical for publicly-funded research efforts. This effort supports a transition to working in the digital agriculture space moving into the future.

2. An improved version of analysis software, ITSxpress, is now available. An updated second version of the highly-cited ITSxpress software has been developed and made available to the public. This software supports automated analysis of metagenomics data, that is the process of taking a snapshot of all DNA present in an environment at a point in time, to enable researchers to know what organisms are present. The updated version integrated a new algorithm that provides more accurate results on what organisms are present in the snapshot. Additionally, there is now a streamlined installation process, broader input file support and support looking at a broader range of organisms. This version enhances the performance, flexibility, and user-experience of a popular metagenomics tool.

3. The first high-quality genome for the highest quality coffee type, Geisha, was developed. There are many types of coffee, including Geisha coffee which is recognized for its unique aromas and flavors and accordingly, has achieved the highest prices in the specialty coffee markets. Growers and consumers alike would love to know what in its genetics makes Geisha coffee so unique and valuable. The first step to answering this question is to develop a blueprint of the entire genetic code of Geisha and the genes that this blueprint encodes. In this study the first genetic blueprint for Geisha was developed and used to identify just over 47,000 unique genes. This is an important step to now be able to compare Geisha genes with genes of other coffee types to see which genes are unique and generating highest quality coffee.

4. High-throughput digital method for problematic leaf spot in cultivated peanut was developed. Breeding for disease resistance is very tricky, as it relies on the disease to be present across materials to be evaluated. It also requires accurately assessing the level of resistance an individual line is experiencing. The work for breeders is further complicated when multiple visual symptoms may be occurring that are hard to decipher. There is a need to develop tools that will help the breeder obtain more accurate data faster to make better decisions to get better materials to growers. This study developed a new tool utilizing drones to help the breeder be able to evaluate disease resistance for leaf spot, an important disease in peanut that causes significant yield losses. This new tool potentially supports more sustainable peanut production, reducing the need for chemical controls.

5. Produced outline on how to support transition of public plant breeding programs. Researchers in the unit participated as experts in a US plant breeding community effort, where researchers came together to develop a publication to provide support across the community for transitioning of programs across generations. Across the US a large portion of the plant breeding programs will soon be undergoing transition as breeders reach retirement age, requiring a careful plan to retain critical germplasm and corresponding data/information about the germplasm materials. This effort attempts to provide documentation and support for how to enable this process. Successful transition is important to the maintenance of the production of successful varieties to provide feed, fuel and fiber and provide competitive materials continuously to growers and farmers.

6. Transfer learning is a successful path for calculating yield. Researchers have successfully transferred our highly accurate computer vision model for citrus yield estimation to apple yield estimation. On top of that, it was demonstrated and shown in the publication that few additional images from the new task can be utilized to implement a new model via transfer learning and learn to add additional tasks, such as counting the fruit on the ground which was not present in a prior model. This is incredibly important as it empowers efforts in agriculture that often are lacking in large data sets for the numerous tasks being performed in field efforts.

7. Two High-quality genomes for important oilseed crop, Camelina. Two high-quality assemblies for the emerging oilseed crop Camelina sativa were developed that were utilized to identify regions of interest for freezing tolerance. The genomes represented the parental lines of a hybrid mated from a winter and spring variety that have different tolerances to freezing conditions. Compared to previous genomic resources that were limited to short-read sequencing technologies, the long-read genomes offered less fragmented sequences and better representation of the repetitive elements that comprise roughly 29% of the genomes. These resources allowed for a better foundation to investigate the genomic regions associated with freezing tolerance to direct breeding for those traits as well as flowering time to achieve longer growing

Review Publications
Valles, S.M., Zhao, C., Rivers, A.R., Iwata, R.L., Oi, D.H., Cha, D.H., Collignon, R., Cox, N.A., Morton, G.J., Calcaterra, L.A. 2023. RNA virus discoveries in the electric ant, Wasmannia auropunctata. Virus Genes. 59:276–289. https://doi.org/10.1007/s11262-023-01969-1.
Hilsop, L.M., Luby, C.H., Loarca, J., Humann, J., Hummer, K.E., Bassil, N.V., Zhao, D., Sheehan, M., Casa, A.M., Billings, G.T., Echeverria, D., Ashrafi, H., Babiker, E.M., Edger, P., Ehlenfeldt, M.K., Hancock, J., Finn, C.E., Iorizzo, M., Mackey, T.A., Munoz, P.R., Olmstead, J., Rowland, L.J., Sandefur, P., Spencer, J., Stringer, S.J., Vorsa, N., Wagner, A., Hulse-Kemp, A.M. 2024. A blueberry (Vaccinium spp.) crop ontology to enable standardized phenotyping for blueberry breeding and research. Journal of the American Pomological Society. 59:1433-1442. https://doi.org/10.21273/HORTSCI17676-23.
Ali, A., Gao, G., Al-Tobasei, R., Youngblood, R., Waldbieser, G.C., Scheffler, B.E., Palti, Y., Salem, M. 2025. Chromosome-level genome assembly and annotation of the Swanson rainbow trout homozygous line. Scientific Data. 12. Article 345. https://doi.org/10.1038/s41597-025-04693-7.
Ott, B.D., Torrans, E.L., Griffins, M.J., Allen, P.J., Duke, M.V., Peterson, B.C., Scheffler, B.E., Hulse-Kemp, A.M. 2024. Hypothalamic Transcriptome Response To Simulated Diel Earthen Pond Hypoxia Cycles In Channel Catfish (Ictalurus punctatus). Physiological Genomics. https://doi.org/10.1152/physiolgenomics.00007.2024.
Einarsson, S.V., Rivers, A.R. 2024. ITSxpress Version 2: Software to rapidly trim internal transcribed spacer sequences with quality scores for amplicon sequencing. Microbiology Spectrum. https://doi.org/10.1128/spectrum.00601-24.
Medrano, J.F., Cantu, D., Minio, A., Dreischer, C., Gibbons, T., Chin, J., Chen, S., Van Deynze, A., Hulse-Kemp, A.M. 2024. De novo whole-genome assembly and annotation of a high-quality coffee variety from the primary origin of coffee, Coffea arabica var. Geisha. G3: Genes, genomics, genetics. https://doi.org/10.1093/g3journal/jkae262.
Ontano, A., Dobrin, B.H., Smith, T.P., Abernathy, B., Sthapit Kandel, J., Shaikh, T., Rahman, M., Anderson, J.V., Vaughn, J.N., Horvath, D.P. 2024. Assembly and analysis of sequence from a spring and winter type Camelina sativa by whole genome PacBio Hifi technologies. Industrial Crops and Products. 221. Article 119346. https://doi.org/10.1016/j.indcrop.2024.119346.
Conover, J.L., Grover, C.E., Sharbrough, J., Sloan, D.B., Peterson, D.G., Wendel, J.F. 2024. Little evidence for homoeologous gene conversion and homoeologous exchange events in Gossypium allopolyploids. American Journal of Botany. https://doi.org/10.1002/ajb2.16386.
Perez, L.M., Mauleon, R., Arick Ii, M.A., Magbanua, Z.V., Peterson, D.G., Dean, J.F., Tseng, T. 2022. Transcriptome analysis of the 2,4-dichlorophenoxyacetic acid (2,4-D)-tolerant cotton chromosome substitution line CS-B15sh and its susceptible parental lines G. hirsutum L. cv. Texas Marker-1 and G. barbadense L. cv. Pima 379. Frontiers in Plant Science. https://doi.org/10.3389/fpls.2022.910369.
Kayal, E., Arick Ii, M.A., Hsu, C., Thrash, A., Yorkston, M., Morden, C.W., Wendel, J.F., Peterson, D.G., Grover, C.E. 2024. Genomic diversity and evolution of the Hawaiian Islands endemic Kokia (Malvaceae). G3: Genes, genomics, genetics. https://doi.org/10.1093/g3journal/jkae180.
Forsythe, E.S., Grover, C.E., Miller, E.R., Conover, J.L., Arick Ii, M.A., Chavarro, M.F., Leal-Bertioli, S.C., Peterson, D.G., Sharbrough, J., Wendel, J.F., Sloan, D.B. 2022. Organellar transcripts dominate the cellular mRNA pool across plants of varying ploidy levels. Proceedings of the National Academy of Sciences (PNAS). https://doi.org/10.1073/pnas.2204187119.
Grover, C.E., Arick II, M.A., Thrash, A., Sharbrough, J., Hu, G., Yuan, D., Miller, E.R., Ramaraj, T., Peterson, D.G., Udall, J.A., Wendell, J.F. 2022. Dual domestication, diversity, and differential introgression in Old World cotton diploids. Genome Biology and Evolution. 14(12). https://doi.org/10.1093/gbe/evac170.
Ramaraj, T., Grover, C.E., Azalea, M., Arick II, M.A., Jareczek, J., Leach, A., Peterson, D., Wendel, J.F., Udall, J.A. 2022. The Gossypium herbaceum L. Wagad genome as a resource for understanding cotton domestication. G3, Genes/Genomes/Genetics. 13(2). https://doi.org/10.1093/g3journal/jkac308.
Ning, W., Rogers, K., Hsu, C., Magbauna, Z.V., Pechanova, O., Arick Ii, M.A., Kayal, E., Hu, G., Peterson, D.G., Udall, J.A., Grover, C., Wendel, J.F. 2024. Origin and diversity of the wild cottons (Gossypium hirsutum) of mound key, Florida. Scientific Reports. 14. Article 14046. https://doi.org/10.1038/s41598-024-64887-8.
Power, I., Simpson, S.A., Ballard, L.L., Liu, X.F., Scheffler, B.E., Lamb, M.C., Arias De Ares, R.S. 2025. Genetic marker data for sweetpotato improvement. Data in Brief. https://doi.org/10.1016/j.dib.2025.111630.
Newman, C., Austin, R., Andres, R., Read, Q.D., Garrity, N., Fritz, K., Oakley, A., Hulse-Kemp, A.M., Dunne, J. 2025. Evaluating UAV-captured RGB and multispectral imagery as a proxy for visual rating of leaf spot in cultivated peanut . The Plant Phenome Journal. https://doi.org/10.1002/ppj2.70019.
Sojka, J., Takac, T., Hlavackova, K., Melicher, P., Ovecka, M., Pechan, T., Samaj, J. 2024. Overexpression of SIMK in menadione-treated alfalfa enhances antioxidant machinery and leads to oxidative stress resistance. Plant Stress. https://doi.org/10.1016/j.stress.2024.100608.
Takac, T., Kubenova, L., Samajova, O., Dvorak, P., Rehak, J., Haberland, J., Pechan, T., Ovecka, M., Samaj, J. 2024. Actin cytoskeleton and plasma membrane aquaporins are involved in different drought response of Arabidopsis rhd2 and der1 root hair mutants. Plant Physiology and Biochemistry. https://doi.org/10.1016/j.plaphy.2024.109137.
Shan, X., Williams, W.P., Peterson, D.G. 2023. Genome resequencing facilitates high-resolution exploration of a maize quantitative trait locus for resistance to aflatoxin accumulation. Euphytica. https://doi.org/10.1007/s10681-023-03232-y.
2024. Complete genome sequence of lima bean endornavirus 1: a putative new member of the genus Alphaendornavirus (family Endornaviridae). Archives of Virology. https://doi.org/10.1007/s00705-024-06135-y.
Gosselaar, M., Arick Ii, M.A., Hsu, C., Renninger, H., Siegert, C.M., Shafqat, W., Peterson, D.G., Himes, A. 2025. Comparative transcriptomic and phenotypic analysis of monoclonal and polyclonal Populus deltoides genotypes. Frontiers in Plant Science. https://doi.org/10.3389/fpls.2024.1498535.
Melicher, P., Dvorak, P., Rehak, J., Samajova, O., Pechan, T., Samaj, J., Takac, T. 2024. Methyl viologen-induced changes in the Arabidopsis proteome implicate PATELLIN 4 in oxidative stress responses. Journal of Experimental Botany. https://doi.org/10.1093/jxb/erad363.
Chang, S.K., Zhang, Y., Pechan, T. 2025. Structures, antioxidant, and angiotensin I-converting enzyme (ACE)-inhibitory activities of peptides derived from protein hydrosylates of three phenolics-rich legume genera. Journal of Food Science. https://doi.org/10.1111/1750-3841.70069.
Aboughanem-Sabanadzo, N., Sabanadzovic, S. 2024. Complete genome sequence of an umbravirus from white snakeroot (Ageratina altissima). Archives of Virology. https://doi.org/10.1007/s00705-024-06125-0.
Sah, S., Popescu, G.V., Reddy, K.R., Klink, V.P., Li, J. 2024. The Glycine max abscisic acid-activated protein kinase-like kinase 1 (GmAALK1) modulates drought stress response. Journal of Plant Growth Regulation. 44:1642-1663. https://doi.org/10.1007/s00344-024-11287-x.
Magbanua, Z.V., Hsu, C., Pechanova, O., Arick Ii, M., Grover, C.E., Peterson, D.G. 2022. Innovations in double digest restriction-site associated DNA sequencing (ddRAD-Seq) method for more efficient SNP identification. Analytical Biochemistry. https://doi.org/10.1016/j.ab.2022.115001.
Liu, R., Luo, D., Scheffler, B.E., Hulse-Kemp, A.M., Overlander-Chen, M., Nandety, R., Fiedler, J.D., Chu, C.N., Zhong, S., Yang, S. 2025. Genetic and physical localization of a leaf rust susceptibility gene in barley. Theoretical and Applied Genetics. 138. Article 127. https://doi.org/10.1007/s00122-025-04916-w.
Gorman, Z.J., Liu, H., Sorg, A.M., Grissett, K.S., Yactayo-Chang, J.P., Li, Q., Rivers, A.R., Basset, G.J., Rering, C.C., Beck, J.J., Hunter III, C.T., Block, A.K. 2025. Flood-induced insect resistance in maize involves flavonoid-dependent salicylic acid induction. Plant, Cell & Environment. 48,5169-5183. https://doi.org/10.1111/pce.15496.
Hale, I., Koebernick, J., Hershberger, J., Rife, T., Arbelaez, J.D., Anderson, N., Bekkerman, A., Bohn, M., Bourland, F., Burke, T., Chee, P., Evans, E., Fumia, N., Feldmann, M., Gasic, K., Hague, S., Heilman-Morales, A.M., Hulse-Kemp, A.M., Iglesias, C., Mueller, L., Luby, J., Pratt, R., Thompson, A., Vierling, R., Worthington, M., Smith, M., Volk, G.M., Wolfe, M., Kantar, M. 2025. Sustaining public plant breeding programs across generations. Crop Science. 65(3). Article e70094. https://doi.org/10.1002/csc2.70094.
Jordan, J.A., Manching, H.K., Hulse-Kemp, A.M., Beksi, W.J. 2024. Few-shot fruit segmentation via transfer learning. IEEE International Conference on Robotics and Automation. https://doi.org/10.1109/ICRA57147.2024.10610003.
Page, C.A., Simpson, S.A., D'Souza, C., Perez Diaz, I.M., Rivers, A.R. 2024. Whole-genome sequences of fermentative and spoilage-associated lactic acid bacteria, Lysinibacillus capsici and a Serratia marcescens isolated from commercial cucumber fermentations. Microbiology Resource Announcements. 13, Issue 12. https://doi.org/10.1128/mra.00910-24.
Jordan, J.A., Manching, H.K., Mattia, M.R., Bowman, K.D., Hulse-Kemp, A.M., Beksi, W.J. 2024. Citdet: a benchmark dataset for citrus fruit detection. International of Electrical and Electronics Engineers (IEEE) Robotics and Automation Letters. https://doi.org/10.1109/LRA.2024.3474473.
Souza, R., Mian, R.M., Vaughn, J.N., Li, Z. 2025. Introgression of a Danbaekkong high protein allele across different genetic backgrounds in soybean. Frontiers in Plant Science. https://doi.org/10.3389/fpls.2023.1308731.