Keywords: decoding modules, sequence polymorphism, SWI3, machine learning, CRISPR/Cas9

Latest 33 Papers

SWI3 regulates male sex determination in Marchantia polymorpha

Authors: Quiles, M. L., Raval, P. K., Rensing, S. A., Gould, S. B.

Date: 2025-10-30 · Version: 1

DOI: 10.1101/2025.10.29.685095

SWI3 chromatin regulation Marchantia polymorpha gametangiophore development transcriptomics

Engineering compact Physalis peruviana (goldenberry) to promote its potential as a global crop

Authors: Santo Domingo, M., Fitzgerald, B., Robitaille, G. M., Ramakrishnan, S., Swartwood, K., Karavolias, N., Schatz, M., Van Eck, J., Lippman, Z.

Date: 2025-10-24 · Version: 2

DOI: 10.1101/2025.08.15.670557

Model Organism: Physalis peruviana

Physalis peruviana Goldenberry CRISPR/Cas9 plant architecture compact ideotype

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

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

Cis-regulatory architecture downstream of FLOWERING LOCUS T underlies quantitative control of flowering

Authors: Zhou, H.-R., Doan, D. T. H., Hartwig, T., Turck, F.

Date: 2025-09-25 · Version: 1

DOI: 10.1101/2025.09.23.678055

Model Organism: Arabidopsis thaliana

FLOWERING LOCUS T enhancer architecture cis‑regulatory logic CRISPR/Cas9 chromatin accessibility

Choosing the Best Route: Comparative Optimization of Wheat Transformation Methods for Improving Yield by Targeting TaARE1-D with CRISPR/Cas9

Authors: Tek, M. I., Budak Tek, K., Sarikaya, P., Ahmed, A. R., Fidan, H.

Date: 2025-09-12 · Version: 1

DOI: 10.1101/2025.09.11.675438

Model Organism: Triticum aestivum

Triticum aestivum CRISPR/Cas9 Agrobacterium-mediated transformation TaARE1-D yield improvement

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

tRNA-Based Polycistronic CRISPR/Cas9 System Boosts Efficiency of Multi-Gene Deletion in the Moss Physcomitrella.

Authors: Kozgunova, E.

Date: 2025-08-01 · Version: 1

DOI: 10.1101/2025.07.29.667574

Model Organism: Physcomitrium patens

CRISPR/Cas9 polycistronic tRNA‑gRNA array large gene deletion multiplex editing Physcomitrium patens

Integrative transcriptomic and phosphoproteomic analysis reveals key components of SnRK1 signaling network in rice

Authors: Faria-Bates, M. C., Maurya, C., Jamsheer K, M., Srivastava, V.

Date: 2025-07-23 · Version: 1

DOI: 10.1101/2025.07.22.666209

Model Organism: Oryza sativa

SnRK1 rice CRISPR/Cas9 phosphoproteomics stress response

Integrating Load-Cell Lysimetry and Machine Learning for Prediction of Daily Plant Transpiration

Authors: Friedman, S., Averbuch, N., Nevo, T., Moshelion, M.

Date: 2025-06-30 · Version: 2

DOI: 10.1101/2024.11.24.625038

Model Organism: Multi-species

daily transpiration machine learning gravimetric load cells Random Forest XGBoost

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