Keywords: paralog processing, transcriptomic analysis, machine learning, quantitative resistance - Genetius Papers

Latest 21 Papers

Molecular basis of delayed leaf senescence induced by short-term treatment with low phosphate in rice

Authors: Martin-Cardoso, H., Bundo, M., Garcia-Molina, A., San Segundo, B.

Date: 2026-01-24 · Version: 1

DOI: 10.64898/2026.01.23.701354

Model Organism: Oryza sativa

phosphate deficiency leaf senescence Oryza sativa CRISPR/Cas9 transcriptomic analysis

Successful Predictive Modeling of Pollen Fitness Phenotypes Is Enabled by Measures of Expression Specificity

Authors: Mueller, S. A. F., Vejlupkova, Z., Megraw, M., Fowler, J. E.

Date: 2026-01-13 · Version: 2

DOI: 10.64898/2025.12.05.692487

Model Organism: Zea mays

maize pollen phenotype prediction machine learning Ds-GFP insertional mutants expression specificity

A novel pathosystem between Aeschynomene evenia and Aphanomyces euteiches reveals new immune components in quantitative legume root-rot resistance.

Authors: Baker, M., Martinez, Y., Keller, J., Sarrette, B., Pervent, M., Libourel, C., Le Ru, A., Bonhomme, M., Gough, C., Castel, B., ARRIGHI, J.-F., Jacquet, C.

Date: 2026-01-11 · Version: 1

DOI: 10.64898/2026.01.11.698850

Model Organism: Aeschynomene evenia

legume immunity Aphanomyces euteiches quantitative resistance transcriptomics Nod factor-independent symbiosis

Key molecular and cellular events of table olive fruit abscission zone formation during natural maturation and after ethephon treatment

Authors: Wang, M., Santos, E., Eagle, S., Chernikova, A., Zhang, S., Tran, P., Marino, G., Jernstedt, J., Niederholzer, F., Wheeler-Dykes, B., Wilkop, T., Ferguson, L., Drakakaki, G.

Date: 2025-11-12 · Version: 1

DOI: 10.1101/2025.11.10.687704

Model Organism: Olea europaea

abscission zone Olea europaea transcriptomic analysis cell wall remodeling alkalinization

Evaluation of computational tools for predicting CRISPR gRNA on-target efficiency in plants.

Authors: Gong, Z., Chen, M., Zhang, H., Mortimer, J., Botella, J. R.

Date: 2025-10-15 · Version: 1

DOI: 10.1101/2025.10.15.679654

Model Organism: Multi-species

CRISPR guide RNA efficiency plant genome editing in silico prediction tools machine learning

Phylogenomic challenges in polyploid-rich lineages: Insights from paralog processing and reticulation methods using the complex genus Packera (Asteraceae: Senecioneae)

Authors: Moore-Pollard, E. R., Ellestad, P., Mandel, J.

Date: 2025-10-13 · Version: 2

DOI: 10.1101/2025.09.14.676143

Model Organism: Multi-species

phylogenomic discordance polyploidy paralog processing Packera historical biogeography

Antifungal activity and mechanism of limonene against Fusarium oxysporum, a pathogen of potato dry rot

Authors: Xia, Q., Dong, P.

Date: 2025-10-06 · Version: 1

DOI: 10.1101/2025.10.06.675995

Model Organism: Fusarium oxysporum

limonene Fusarium oxysporum potato dry rot bio‑based fungicide transcriptomic analysis

Usefulness of scRNA-seq data in predicting plant metabolic pathway genes

Authors: Ma, J., Wang, Z., Zou, L., Wang, X., Zuo, X., Wang, F., Wang, Z., Li, Z., Li, L., Wang, P.

Date: 2025-10-02 · Version: 2

DOI: 10.1101/2024.10.07.617125

Model Organism: Arabidopsis thaliana

metabolic pathway genes single-cell RNA sequencing machine learning co-expression networks prediction accuracy

Conserved 'late' effector genes from Leptosphaeria maculans inducing gene-for-gene quantitative resistance in Brassica napus semi-winter genotypes

Authors: Rabeau, C., Wagner, A., Lapalu, N., Jiquel, A., Faure, S., Fudal, I.

Date: 2025-09-23 · Version: 1

DOI: 10.1101/2025.09.22.677120

Model Organism: Brassica napus

Leptosphaeria maculans Brassica napus late effector genes quantitative resistance semi‑winter genotypes

Predicting flowering time using integrated morphophysiological and genomic data with machine learning models

Authors: Babaei, M., Nemati, H., Arouiee, H., Torkamaneh, D.

Date: 2025-08-13 · Version: 1

DOI: 10.1101/2025.08.11.669697

Model Organism: Cannabis sativa

flowering time Cannabis sativa genotyping-by-sequencing machine learning SNP markers

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