Keywords: paralog processing, ascorbate, codon usage, deep learning, root architecture

Latest 23 Papers

Early root-root interactions weaken foliar defense responses against Septoria tritici blotch in a durum wheat varietal mixture

Authors: MATHIEU, L., CHLOUP, A., MARTY, S., SAVAJOLS, J., ROUVEYROL, C., VALLS, J., PETRIACQ, P. G. J., MOREL, J.-B., METEIGNIER, L.-V., BALLINI, E.

Date: 2025-08-02 · Version: 1

DOI: 10.1101/2025.07.30.667709

Model Organism: Triticum durum

durum wheat Septoria tritici blotch root architecture defense response timing untargeted metabolomics

Phenotypic scoring of Canola Blackleg severity using machine learning image analysis

Authors: Hu, Q., Anderson, S. N., Gardner, S., Ernst, T. W., Koscielny, C. B., Bahia, N. S., Johnson, C. G., Jarvis, A. C., Hynek, J., Coles, N., Falak, I., Charne, D. R., Ruidiaz, M. E., Linares, J. N., Mazis, A., Stanton, D. J.

Date: 2025-07-22 · Version: 1

DOI: 10.1101/2025.07.17.665372

Model Organism: Multi-species

blackleg disease canola (Brassica spp.)deep learning image-based phenotyping disease severity scoring

RegenPGC View: Semantic Segmentation of Perennial Groundcover Cropping Systems to Restore American Croplands

Authors: Meyering, B., Schlautman, B.

Date: 2025-07-06 · Version: 1

DOI: 10.1101/2025.07.03.663038

Model Organism: General

perennial groundcovers cover cropping deep learning convolutional neural network image‑based phenotyping

Exogenous Hormone Treatments Reveal Species-Specific Regulation of Individual Components of Root Architecture and Salt Ion Accumulation in Cultivated and Wild Tomatoes

Authors: Rahmati-Ishka, M., Craft, E., Pineros, M., Julkowska, M. M.

Date: 2025-06-20 · Version: 1

DOI: 10.1101/2025.06.17.660158

Model Organism: Solanum lycopersicum

hormonal signaling root architecture salt stress Na/K ratio tomato

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

Arabidopsis lines with modified ascorbate concentrations reveal a link between ascorbate and auxin biosynthesis

Authors: Fenech, M., Zulian, V., Moya-Cuevas, J., Arnaud, D., Morilla, I., Smirnoff, N., Botella, M. A., Stepanova, A. N., Alonso, J. M., Martin-Pizarro, C., Amorim-Silva, V.

Date: 2025-05-16 · Version: 1

DOI: 10.1101/2025.05.15.654287

Model Organism: Arabidopsis thaliana

ascorbate Arabidopsis thaliana auxin biosynthesis redox homeostasis transcriptomics

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

Revisiting the Central Dogma: the distinct roles of genome, methylation, transcription, and translation on protein expression in Arabidopsis thaliana

Authors: Zhong, Z., Bailey, M., Kim, Y.-I., Pesaran-Afsharyan, N., Parker, B., Arathoon, L., Li, X., Rundle, C. A., Behrens, A., Nedialkova, D. D., Slavov, G., Hassani-Pak, K., Lilley, K. S., Theodoulou, F. L., Mott, R.

Date: 2025-03-31 · Version: 2

DOI: 10.1101/2025.01.08.631880

Model Organism: Arabidopsis thaliana

Arabidopsis thaliana codon usage gene expression DNA methylation ribosome profiling

Root exudation facilitate water infiltration and rewetting of dry soil

Authors: Gomez, E., Mair, A., Martin, I., de las Heras, G., Ptashnyk, M., Dupuy, L. X.

Date: 2025-03-27 · Version: 1

DOI: 10.1101/2025.03.26.645447

Model Organism: General

root exudates water infiltration dry soil root architecture drought resistance

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

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