Keywords: paralog processing, machine learning, transcriptomics, high‑throughput phenotyping

Latest 53 Papers

Phylogenetic Analysis and Machine Learning Identify Signatures of Selection and Predict Deleterious Mutations in Common Bean

Authors: Cordoba-Novoa, H. A., Buckler, E. S., Romay, C., Berthel, A., Johnson, L., Balasubramanian, P., Hoyos Villegas, V.

Date: 2025-05-06 · Version: 1

DOI: 10.1101/2025.05.05.652309

Model Organism: Phaseolus vulgaris

deleterious mutations genetic load common bean MAGIC population machine learning

S2-PepAnalyst: A Web Tool for Predicting Plant Small Signalling Peptides

Authors: Vomo Donfack, K. L., Abaach, M., Luna, A. M., Ginot, G., Doblas, V. G., Morilla, I.

Date: 2025-05-06 · Version: 2

DOI: 10.1101/2024.08.02.606319

Model Organism: General

small signalling peptides machine learning protein language models geometric-topological analysis reinforcement learning

Comparative multi-omics profiling of Gossypium hirsutum and Gossypium barbadense fibers at high temporal resolution reveals key differences in polysaccharide composition and associated glycosyltransferases

Authors: Swaminathan, S., Lee, Y., Grover, C. E., DeTemple, M. F., Mugisha, A. S., Sichterman, L. E., Yang, P., Xie, J., Wendel, J. F., Szymanski, D. B., Zabotina, O. A.

Date: 2025-04-30 · Version: 1

DOI: 10.1101/2025.04.26.650795

Model Organism: Multi-species

cotton fiber development polysaccharide composition glycome profiling transcriptomics glycosyltransferases

ERF transcription factor regulons underpin growth-defence trade-off under acute heat stress in rice seedlings

Authors: Nair, A. U., Vishwakarma, S., Guha, T., Kadumuri, R. V., Fritschi, F. B., Chavali, S., Allu, A. D.

Date: 2025-04-22 · Version: 1

DOI: 10.1101/2025.04.21.649784

Model Organism: Oryza sativa

heat stress Oryza sativa seedling transcriptomics ethylene responsive factors phytohormone treatment

A transcriptional dissection of the petty spurge (Euphorbia peplus L.) reproductive structure

Authors: Johnson, A. R., Bao, A., Frank, M. H.

Date: 2025-04-17 · Version: 2

DOI: 10.1101/2024.05.06.592779

Model Organism: Euphorbia peplus

cyathium transcriptomics floral development genes SEP1 paralogs Euphorbia peplus

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

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

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

The FIP 1.0 Data Set: Highly Resolved Annotated Image Time Series of 4,000 Wheat Plots Grown in Six Years

Authors: Roth, L., Boss, M., Kirchgessner, N., Aasen, H., Aguirre-Cuellar, B. P., Akiina, P. P. A., Anderegg, J., Gajardo Castillo, J., Chen, X., Corrado, S., Cybulski, K., Keller, B., Kortstee, S. G., Kronenberg, L., Liebisch, F., Nousi, P., Oppliger, C., Perich, G., Pfeifer, J., Yu, K., Storni, N., Tschurr, F., Volpi, M., Treier, S., Zellweger, H., Zumsteg, O., Hund, A., Walter, A.

Date: 2025-03-11 · Version: 3

DOI: 10.1101/2024.10.04.616624

Model Organism: Triticum aestivum

genotype‑environment interaction winter wheat high‑throughput phenotyping genomic prediction crop modeling

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