Keywords: paralog processing, spatial transcriptomics, deep learning, drought tolerance

Latest 39 Papers

Unraveling the cis-regulatory code controlling abscisic acid-dependent gene expression in Arabidopsis using deep learning

Authors: Opdebeeck, H., Smet, D., Thierens, S., Minne, M., De Beukelaer, H., Zuallaert, J., Van Bel, M., Van Montagu, M., Degroeve, S., De Rybel, B., Vandepoele, K.

Date: 2025-06-06 · Version: 1

DOI: 10.1101/2025.06.03.657584

Model Organism: Arabidopsis thaliana

Abscisic acid Arabidopsis thaliana deep learning promoter motifs ABF binding sites

Imputation integrates single-cell and spatial gene expression data to resolve transcriptional networks in barley shoot meristem development

Authors: Demesa-Arevalo, E., Dorpholz, H., Vardanega, I., Maika, J. E., Pineda-Valentino, I., Eggels, S., Lautwein, T., Kohrer, K., Schnurbusch, T., von Korff, M., Usadel, B., Simon, R.

Date: 2025-05-11 · Version: 1

DOI: 10.1101/2025.05.09.653223

Model Organism: Hordeum vulgare

Barley (Hordeum vulgare)inflorescence meristems single-cell RNA sequencing spatial transcriptomics developmental gene regulation

Deciphering the Sequence Basis and Application of Transcriptional Initiation Regulation in Plant Genomes Through Deep Learning

Authors: Gao, P., Lian, L., Feng, W., Ma, Y., Lin, J., Qin, L., Hao, S., Zhao, H., Liu, X., Yuan, J., Lin, Z., Li, X., Guan, Y., Wang, X.

Date: 2025-04-24 · Version: 1

DOI: 10.1101/2025.04.22.649999

Model Organism: Glycine max

transcription start site promoter motifs deep learning soybean domestication plant gene regulation

Loss-of-function of the drought-induced genes GASA3 and AFP1 confers enhanced drought tolerance in Arabidopsis thaliana

Authors: Bhattacharyya, S., Turysbek, B., Lorenz, S. D., Rosales, D. C., Shoaib, Y., Gutbrod, K., Doermann, P., Chigri, F., Vothknecht, U. C.

Date: 2025-04-06 · Version: 1

DOI: 10.1101/2025.04.03.647048

Model Organism: Arabidopsis thaliana

drought tolerance GASA3 AFP1 abscisic acid (ABA)stomatal aperture

Developing the Grapevine Hydric Stress Atlas: A Meta-Analysis Resource for Exploring transcriptome Responses to Drought

Authors: VALENZUELA, A. V., Navarro-Paya, D., Santiago, A., Sonego, P., Gainza-Cortes, F., Malnoy, M., Matus, J. T.

Date: 2025-04-01 · Version: 1

DOI: 10.1101/2025.03.31.646369

Model Organism: Vitis vinifera

drought tolerance grapevine (Vitis vinifera)transcriptomic meta-analysis gene co‑expression network molecular breeding

The Global Wheat Full Semantic Organ Segmentation (GWFSS) dataset

Authors: Wang, Z., Zenkl, R., Greche, L., De Solan, B., Bernigaud Samatan, L., Ouahid, S., Visioni, A., Robles-Zazueta, C. A., Pinto, F., Perez-Olivera, I., Reynolds, M. P., Zhu, C., Liu, S., D'argaignon, M.-P., Lopez-Lozano, R., Weiss, M., Marzougui, A., Roth, L., Dandrifosse, S., Carlier, A., Dumont, B., Mercatoris, B., Fernandez, J., Chapman, S., Najafian, K., Stavness, I., Wang, H., Guo, W., Virlet, N., Hawkesford, M., Chen, Z., David, E., Gillet, J., Irfan, K., Comar, A., Hund, A.

Date: 2025-03-19 · Version: 1

DOI: 10.1101/2025.03.18.642594

Model Organism: Triticum aestivum

wheat semantic segmentation computer vision deep learning phenotyping

Priming of retrograde signaling in wheat across multiple natural environments reveal how responses to dynamic stimuli can be integrated to alter yield, yield stability and water productivity

Authors: Bowerman, A. F., Moore, M., Yadav, A., Zhang, J., Mortimer, M. D., Plskova, Z., Tee, E. E., Au, E. K., Collinge, D. P., Estavillo, G. M., Howitt, C. A., Chan, K. X., Rebetzke, G. J., Pogson, B. J.

Date: 2025-03-13 · Version: 1

DOI: 10.1101/2025.03.10.642515

Model Organism: Triticum aestivum

retrograde signaling SAL gene deletions wheat (Triticum aestivum)drought tolerance field trial validation

Fiber Bragg grating based sensing system for non-destructive root phenotyping using ResNet prediction

Authors: Binder, S., Hossain, K., Bucksch, A., Fok, M.

Date: 2025-02-28 · Version: 2

DOI: 10.1101/2024.09.17.613457

Model Organism: Zea mays

root architecture fiber Bragg grating real-time phenotyping deep learning Zea mays

Robustness of high-throughput prediction of leaf ecophysiological traits using near infra-red spectroscopy and poro-fluorometry

Authors: Coindre, E., Boulord, R., Chir, L., Freitas, V., Ryckewaert, M., Laisne, T., Bouckenooghe, V., Lis, M., Cabrera-Bosquet, L., Doligez, A., Simonneau, T., Pallas, B., Coupel-Ledru, A., Segura, V.

Date: 2025-02-07 · Version: 1

DOI: 10.1101/2025.02.07.636194

Model Organism: Vitis vinifera

drought tolerance high-throughput phenotyping spectroscopy poro-fluorometry leaf mass per area

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