class UtsabGhimire(PlantScientist):
role = "Scientist @ Soilcea"
mission = "Transgene-free, CRISPR-edited citrus vs. Huanglongbing (citrus greening)"
education = "PhD, Horticultural Sciences — University of Florida"
at_the_bench = ["CRISPR/Cas9", "base editing", "Agrobacterium transformation",
"tissue culture & regeneration", "molecular cloning"]
at_the_keyboard = ["bulk & single-cell RNA-seq", "WGS / variant calling",
"proteomics", "metabolomics", "reproducible HPC pipelines"]
for_prediction = ["XGBoost", "CatBoost", "neural networks"]
def philosophy(self):
return "Build more resilient crops — one edited gene, then a thousand analyzed at once."flowchart LR
A["🧬 Design<br/>gRNA & edit"] --> B["🦠 Deliver<br/>Agrobacterium"]
B --> C["🌱 Regenerate<br/>edited plant"]
C --> D["🔎 Verify<br/>WGS · Nanopore"]
D --> E["📊 Profile<br/>RNA · protein · metabolites"]
E --> F["🤖 Predict<br/>traits with ML"]
classDef bench fill:#1D5B44,stroke:#0c3327,color:#fff;
classDef data fill:#0c3327,stroke:#3C9D6B,color:#fff;
class A,B,C bench;
class D,E,F data;
- 🍊 Transgene-free editing workflows for citrus at Soilcea
- High quality citrus genome
-1D5B44?style=flat-square){kind=icon}
Single-cell atlas of broccoli inflorescences — senescence & developmental trajectories.
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TMT proteomics pipeline — preprocessing, PCA, volcano plots, differential testing.
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Cover-crop biomass & C:N prediction with CatBoost, XGBoost and neural nets + weather features.
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QC → trimming → alignment on HPC for postharvest RNA-seq.
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Notebook + Streamlit app: raw reads to differential expression, clickable end to end.
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Peer-reviewed work in postharvest biology, senescence genomics & ML for agriculture (click to expand)
Published in:
- 📘 Postharvest Biology and Technology
- 📗 Vegetable Research
- 📙 Agricultural & Environmental Letters
If our interests overlap — crop biotech, omics, or ML for plants — let's talk. 🌿