Keywords: transcriptomics, decoding modules, machine learning, transcriptome analysis, DDM1, drought tolerance

Latest 74 Papers

Welcome pathogens: transient heat dampens the responses to acibenzolar-S-methyl beyond defenses in apple plants

Authors: Chavonet, E., Nguyen, B.-h., Heintz, C., Cournol, R., Siroka, J., Novak, O., Larbat, R., Gaucher, M., Brisset, M.-N., Pantin, F., Degrave, A.

Date: 2025-04-06 · Version: 1

DOI: 10.1101/2025.04.03.647103

Model Organism: Malus domestica

heat stress acibenzolar‑S‑methyl (ASM)induced immunity apple (Malus domestica)transcriptomics

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

High Density Phenotypic Map of Natural Variation for Intermediate Phenotypes Associated with Stalk Lodging Resistance in Maize

Authors: Kunduru, B., Bokros, N. T., Tabaracci, K., Kumar, R., Brar, M. S., Stubbs, C. J., Oduntan, Y., DeKold, J., Bishop, R. H., Woomer, J., Verges, V. L., McDonald, A., McMahan, C. S., DeBolt, S., Robertson, D. J., Sekhon, R.

Date: 2025-04-06 · Version: 1

DOI: 10.1101/2025.04.03.647088

Model Organism: Zea mays

stalk lodging resistance maize phenomics genetic architecture intermediate phenotypes machine learning

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

Omics-driven Identification of Candidate Genes and SNP markers in a Major QTL Controlling Early Heading in Rice

Authors: Rao, D., T, N. S., CG, G., Gaur, N., Jamaloddin, M., Maganti, S. M., Raman, M. S., Patel, H. K., Tiwari, S., Sonti, R. V.

Date: 2025-03-27 · Version: 1

DOI: 10.1101/2025.03.25.645207

Model Organism: Oryza sativa

heading date rice (Oryza sativa)QTL-seq SNP markers transcriptomics

Multilevel analysis of response to plant growth promoting and pathogenic bacteria in Arabidopsis roots and the role of CYP71A27 in this response

Authors: Koprivova, A., Ristova, D., Berka, M., Berkova, V., Türksoy, G. M., Andersen, T. G., Westhoff, P., Cerny, M., Kopriva, S.

Date: 2025-03-27 · Version: 1

DOI: 10.1101/2025.03.26.645393

Model Organism: Arabidopsis thaliana

CYP71A27 plant‑microbe interaction Pseudomonas fluorescens CH267 Burkholderia glumeae PG1 transcriptomics

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

MYB93 regulates responses to environmental sulphur in Arabidopsis and tomato.

Authors: Cao, X., Wilkinson, H. B., Hutton, B., McMulkin, N., Graham, N. S., Etherington, R., Oliver, A. G., Clayton, C. M., Kaur, H., Coates, J. C.

Date: 2025-03-07 · Version: 1

DOI: 10.1101/2025.03.04.641362

Model Organism: Arabidopsis thaliana

sulfur nutrition AtMYB93 root development transcriptome analysis elemental analysis

The genome of the vining fern Lygodium microphyllum highlights genomic and functional differences between life phases of an invasive plant

Authors: Pelosi, J., Davenport, R., Kuo, L.-Y., Gray, L. N., Dant, A. J., Kim, E. H., Li, F.-W., Dlugosch, K. M., Krabbenhoft, T. J., Barbazuk, W. B., Sessa, E. B.

Date: 2025-03-07 · Version: 1

DOI: 10.1101/2025.03.06.640867

Model Organism: Lygodium microphyllum

Lygodium microphyllum alternation of generations transcriptomics methylome invasive species

Sunflower yield modeling with XAI: Historical weather impacts and forecasting

Authors: Majumder, S., Mason, C.

Date: 2025-02-28 · Version: 1

DOI: 10.1101/2025.02.27.640573

Model Organism: Helianthus annuus

explainable AI sunflower yield climate variability machine learning Shared Socioeconomic Pathways

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