Keywords: paralog processing, transcriptome, deep learning, VPZ construct, ascorbate, phylogenomic discordance

Latest 17 Papers

Organellar data sets confirm overall angiosperm relationships if problematic RNA-edit sites are accounted for in mitochondrial genomes

Authors: Gerelle, W. K., Jost, M., Marques, I., Les, D., Vallejos, R., Little, S., Sinn, B. T., Sokoloff, D. D., Macfarlane, T. D., Iles, W., Feild, T., Mathews, S., Moore, M., Couvreur, T. L. P., Sauquet, H., Wanke, S., Graham, S. W.

Date: 2025-06-12 · Version: 1

DOI: 10.1101/2025.06.09.658674

Model Organism: Multi-species

organellar genomes angiosperm phylogeny mitochondrial RNA editing plastid phylogenomics phylogenomic discordance

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

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

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

Transcriptome responses of two Halophila stipulacea seagrass populations from pristine and impacted habitats, to single and combined thermal and excess nutrient stressors, reveal local adaptive features and core stress-response genes

Authors: Nguyen, H. M., Yaakov, B., Beca-Carretero, P., Procaccini, G., Wang, G., Dassanayake, M., Winters, G., Barak, S.

Date: 2025-01-27 · Version: 2

DOI: 10.1101/2025.01.21.634158

Model Organism: Halophila stipulacea

Halophila stipulacea thermal stress nutrient stress combined stressors transcriptome

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