Keywords: paralog processing, transcriptome, deep learning, local adaptation

Latest 21 Papers

Tomato leaf transcriptomic changes promoted by long-term water scarcity stress can be largely prevented by a fungal-based biostimulant

Authors: Lopez-Serrano, L., Ferez-Gomez, A., Romero-Aranda, R., Jaime Fernandez, E., Leal Lopez, J., Fernandez Baroja, E., Almagro, G., Dolezal, K., Novak, O., Diaz, L., Bautista, R., Leon Morcillo, R. J., Pozueta Romero, J.

Date: 2025-06-28 · Version: 1

DOI: 10.1101/2025.06.26.661737

Model Organism: Solanum lycopersicum

water deficit Trichoderma harzianum culture filtrate tomato drought tolerance transcriptome

Contrasting patterns of local adaptation and climate resilience across forest management regimes in Norway spruce (Picea abies): implications for reforestation practices under climate change

Authors: Eklöf, H., Bernhardsson, C., Ingvarsson, P. K.

Date: 2025-06-17 · Version: 1

DOI: 10.1101/2025.06.11.659044

Model Organism: Picea abies

genetic diversity local adaptation Picea abies forest management climate change

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

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

Extensive photophysiological variation in wild barley is linked to environmental origin

Authors: Breil-Aubert, M., Shaw, K., Royles, J., Rodrigues Gabriel Sales, C., Walter, J., Taylor, G., Vath, R., Bdolach, E., Dev Tiwari, L., Mathan, J., Lawson, T., Fridman, E., Kromdijk, J., Ferguson, J. N.

Date: 2025-03-31 · Version: 1

DOI: 10.1101/2025.03.28.645825

Model Organism: Hordeum vulgare

crop wild relatives photophysiology genome-wide association study local adaptation stomatal conductance

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

Phytochrome A is required for light-inhibited germination of Aethionema arabicum seed

Authors: Merai, Z., Xu, F., Hajdu, A., Kozma-Bognar, L., Dolan, L.

Date: 2025-02-28 · Version: 1

DOI: 10.1101/2025.02.26.640300

Model Organism: Aethionema arabicum

negative photoblastic germination phytochrome A Aethionema arabicum high‑irradiance response local adaptation

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 differentiation in Cryptomeria japonica trees with different origins growing in the north and south of Japan

Authors: Ujino-Ihara, T., Uchiyama, K., Kanetani, S., Suyama, Y., Tsumura, Y.

Date: 2025-02-25 · Version: 1

DOI: 10.1101/2025.02.22.639673

Model Organism: Cryptomeria japonica

local adaptation transcriptome analysis weighted gene co‑expression network analysis (WGCNA)SNP differentiation defense response genes

Remote sensing reveals inter- and intraspecific variation in riparian cottonwood (Populus spp) response to drought

Authors: Seeley, M., Wiebe, B. C., Gehring, C. A., Hultine, K., Posch, B. C., Cooper, H. F., Schaefer, E. A., Bock, B. M., Abraham, A. J., Moran, M. E., Keith, A., Allan, G. J., Scull, M., Whitham, T. G., Martin, R. E., Asner, G. P., Doughty, C.

Date: 2025-02-19 · Version: 1

DOI: 10.1101/2025.02.17.638728

Model Organism: Populus fremontii

drought response leaf-level VSWIR spectroscopy UAV thermal imaging genotype-by-environment interaction local adaptation

PreviousPage 2 of 3Next