Research in the Schnable Lab@UNL is an interdisciplinary endeavor. Here are our main focus areas:
Phenotyping
Developing and testing new approaches to measure plants, from greenhouses to fields to satellites. We collaborate closely with engineers and statisticians both here at UNL and at other universities around the world to develop and deploy new algorithms, tools, and datasets for high throughput plant phenotyping.
Recent Lab Publications on Phenotyping
- Shrestha N, Mangal H, Torres-Rodriguez JV, Tross MC, Lopez-Corona L, Linders K, Sun G, Mural RV, Schnable JC (2025) "Off-the-shelf image analysis models outperform human visual assessment in identifying genes controlling seed color variation in sorghum." The Plant Phenome Journal doi: 10.1002/ppj2.70013
- Istipliler D, Tross MC, Bouwens B, Jin H, Yufeng Ge, Yang J, Mural RV, Schnable JC (2025) "Heritability, heterosis, and hybrid/inbred classification ability of maize leaf hyperspectral signals under changing soil nitrogen." Crop Science doi: 10.1002/csc2.70073
- Shrestha N, Powadi A, Davis J, Ayanlade TT, Liu H, Tross MC, Mathivanan RK, Bares J, Lopez-Corona L, Turkus J, Coffey L, Tubery TZ, Ge Y, Sarkar S, Schnable JC, Ganapathysubramanian B, Schnable PS (2025) "Plot-level satellite imagery can substitute for UAVs in assessing maize phenotypes across multistate field trials." Plants Planet People doi: 10.1002/ppp3.10613
- Davis JM, Gaillard M, Tross MC, Shrestha N, Ostermann I, Grove RJ, Li B, Benes B, Schnable JC (2025) "3D reconstruction enables high-throughput phenotyping and quantitative genetic analysis of phyllotaxy." Plant Phenomics doi: 10.1016/j.plaphe.2025.100023
- Ji Zhongjie, Ge Y, Schnable JC (2025) "Scalable methods for quantifying the stay green ability of corn for yield prediction by using satellite images." agriRxiv doi: 10.31220/agriRxiv.2025.00339
- Tross MC, Grzybowski M, Jubery TZ, Grove RJ, Nishimwe AV, Torres-Rodriguez JV, Sun G, Ganapathysubramanian B, Ge Y, Schnable JC (2024) "Data driven discovery and quantification of hyperspectral leaf reflectance phenotypes across a maize diversity panel." The Plant Phenome Journal doi: 10.1002/ppj2.20106
- Miao C, Guo A, Thompson AM, Yang J, Ge Y, Schnable JC (2021) "Automation of leaf counting in maize and sorghum using deep learning." The Plant Phenome Journal doi: 10.1002/ppj2.20022
- Grzybowski M, Wijewardane NK, Atefi A, Ge Y, Schnable JC (2021) "Hyperspectral reflectance-based phenotyping for quantitative genetics in crops: progress and challenges." Plant Communications doi: 10.1016/j.xplc.2021.100209
Quantitative Genetics
Collecting genetic, molecular, and trait data from large populations to understand how genes and environment shape phenotypes. We develop and apply statistical approaches that leverage unique features of new phenotypic datasets, including time-series data from whole mapping populations collected using high throughput phenotyping technologies.
Recent Lab Publications on Quantitative Genetics
- Istipliler D, Tross MC, Bouwens B, Jin H, Yufeng Ge, Yang J, Mural RV, Schnable JC (2025) "Heritability, heterosis, and hybrid/inbred classification ability of maize leaf hyperspectral signals under changing soil nitrogen." Crop Science doi: 10.1002/csc2.70073
- Davis JM, Coffey LM, Turkus J, López-Corona L, Linders K, Ullagaddi C, Santra DK, Schnable PS, Schnable JC (2025) "Assessing the impact of yield plasticity on hybrid performance in maize." Physiologia Plantarum doi: 10.1111/ppl.70278
- Shrestha N, Mangal H, Torres-Rodriguez JV, Tross MC, Lopez-Corona L, Linders K, Sun G, Mural RV, Schnable JC (2025) "Off-the-shelf image analysis models outperform human visual assessment in identifying genes controlling seed color variation in sorghum." The Plant Phenome Journal doi: 10.1002/ppj2.70013
- Ali W, Grzybowski M, Torres-Rodriguez JV, Li F, Shrestha N, Mathivanan RK, de Bernardeaux G, Hoang K, Mural R, Roston RL, Schnable JC, Sahay S (2025) "Quantitative genetics of photosynthetic trait variation in maize." Journal of Experimental Botany doi: 10.1093/jxb/eraf198
- Mangal H, Linders K, Turkus J, Shrestha N, Long B, Kuang X, Cebert E, Torres-Rodriguez JV, Schnable JC (2025) "Genes and pathways determining flowering time variation in temperate adapted sorghum." The Plant Journal doi: 10.1111/tpj.70250
- Mathivanan RK, Pedersen C, Turkus J, Shrestha N, Ali W, Torres-Rodriguez JV, Mural RV, Obata T, Schnable JC (2025) "Transcripts and genomic intervals associated with variation in metabolite abundance in maize leaves under field conditions." BMC Genomics doi: 10.1186/s12864-025-11580-3
- Torres-Rodriguez JV, Li D, Schnable JC (2025) "Evolving best practices for transcriptome-wide association studies accelerate discovery of gene-phenotype links." Current Opinion in Plant Biology doi: 10.1016/j.pbi.2024.102670
- Tross MC, Grzybowski M, Jubery TZ, Grove RJ, Nishimwe AV, Torres-Rodriguez JV, Sun G, Ganapathysubramanian B, Ge Y, Schnable JC (2024) "Data driven discovery and quantification of hyperspectral leaf reflectance phenotypes across a maize diversity panel." The Plant Phenome Journal doi: 10.1002/ppj2.20106
Genomics
Using comparative genomics to engineer more stress-tolerant and resource-use-efficient plants. We utilize cross-species comparisons to separate functional from non-functional portions of plant genomes and predict the functions of conserved sequences.
Recent Lab Publications on Genomics
- Shrestha N, Ji Zhongjie, Dai X, Li P, Schnable JC (2025) "A sequence-based classifier distinguishes phenotype-associated genes from other gene models in plants" bioRxiv doi: 10.64898/2025.11.30.691407
- Mathivanan RK, Pedersen C, Turkus J, Shrestha N, Ali W, Torres-Rodriguez JV, Mural RV, Obata T, Schnable JC (2025) "Transcripts and genomic intervals associated with variation in metabolite abundance in maize leaves under field conditions." BMC Genomics doi: 10.1186/s12864-025-11580-3
- Moura Dias H, de Teledo NA, Mural RV, Schnable JC, Van Sluys MA (2024) "THI1 gene evolutionary trends: A comprehensive plant-focused Assessment via data Mining and large-scale analysis." Genome Biology and Evolution doi: 10.1093/gbe/evae212
- Korth N, Yang Q, Van Haute MJ, Tross MC, Peng B, Shrestha N, Zwiener M, Mural RV, Schnable JC, Benson AK (2024) "Genomic co-localization of variation affecting agronomic and human gut microbiome traits in a meta-analysis of diverse sorghum." doi: 10.1093/g3journal/jkae145
- Grzybowski M, Mural RV, Xu G, Turkus J, Yang J, Schnable JC (2023) "A common resequencing-based genetic marker dataset for global maize diversity." The Plant Journal doi: 10.1111/tpj.16123
- Sun G, Yu H, Wang P, Lopez-Guerrero MG, Mural RV, Mizero ON, Grzybowski M, Song B, van Dijk K, Schachtman DP, Zhang C, Schnable JC (2023) "A role for heritable transcriptomic variation in maize adaptation to temperate environments." Genome Biology doi: 10.1186/s13059-023-02891-3
- Sun G, Wase N, Su S, Jenkins J, Zhou B, Torres-Rodriguez JT, ..., Foltz A, ..., Sigmon B, Yu B, Obata T, Schmutz J, Schnable JC (2022) "Genome of Paspalum vaginatum and the role of trehalose mediated autophagy in increasing maize biomass." Nature Communications doi: 10.1038/s41467-022-35507-8
- Dai X, Xu Z, Liang Z, Tu X, Zhong S, Schnable JC, Li P (2020) "Non-homology-based prediction of gene functions." The Plant Genome doi: 10.1002/tpg2.20015
AI/ML
Applying artificial intelligence and machine learning approaches across our research areas. From deep learning models for image-based phenotyping to neural networks for genomic prediction, we leverage modern computational methods to extract insights from large biological datasets.
Recent Lab Publications on AI/ML
- Shrestha N, Ji Zhongjie, Dai X, Li P, Schnable JC (2025) "A sequence-based classifier distinguishes phenotype-associated genes from other gene models in plants" bioRxiv doi: 10.64898/2025.11.30.691407
- Powadi A, Jubery T, Tross M, Shrestha N, Coffey L, Schnable JC, Schnable PS, Ganapathysubramanian B (2025) "Enhancing yield prediction from plot-level satellite imagery through genotype and environment feature disentanglement." Frontiers in Plant Science doi: 10.3389/fpls.2025.1617831
- Zaremehrjerdi H, Coffey L, Jubery T, Liu H, Turkus J, Linders K, Schnable JC, Schnable PS, Ganapathysubramanian B (2025) "MaizeEar-SAM: Zero-shot maize ear phenotyping." arXiv doi: 10.48550/arXiv.2502.13399
- Ji Zhongjie, Ge Y, Schnable JC (2025) "Scalable methods for quantifying the stay green ability of corn for yield prediction by using satellite images." agriRxiv doi: 10.31220/agriRxiv.2025.00339
- Zarei A, Li B, Schnable JC, Lyons E, Pauli D, Benes B, Barnard K (2024) "PlantSegNet: 3D point cloud instance segmentation of nearby plant organs with identical semantics." Computers and Electronics in Agriculture doi: 10.1016/j.compag.2024.108922
- Powadi A, Jubery TZ, Tross M, Schnable JC, Ganapathysubramanian B (2024) "Disentangling genotype and environment specific latent features for improved trait prediction using a compositional autoencoder." Frontiers in Plant Science doi: 10.3389/fpls.2024.1476070
- Ostermann I, Benes B, Gaillard M, Li B, Davis J, Grove RJ, Shrestha N, Tross MC, Schnable JC (2024) "Sorghum segmentation and leaf counting using an in silico trained deep neural model." The Plant Phenome Journal doi: 10.1002/ppj2.70002
- Tross MC, Duggan G, Shrestha N, Schnable JC (2024) "Models trained to predict differential expression across plant organs identify distal and proximal regulatory regions." doi: 10.1101/2024.06.04.597477