Prem P. SinghPlant scientist · Data scientist

Prem Pratap Singh

Plant Scientist · Data Scientist · Molecular Diagnostics

Translating noisy ag-bio data into actionable R&D insights.

I study crop disease from the field sample to the final model. My work brings molecular diagnostics, multi-omics, and reproducible data analysis together to find signals that can improve crop decisions.

  • UC Davis
  • Plant pathology
  • Computational biologist

Open to Research Scientist and Data Scientist roles in ag-bio R&D, molecular diagnostics, NGS workflows, and trait discovery.

Explore researchCV
Prem Pratap Singh
Now
Postdoctoral Scholar, University of California, Davis
Study
California field crop diseases, including Grapevine Red Blotch Virus
Methods
RNA sequencing, Snakemake, DESeq2, GC-MS, R, Python
Published
38 peer-reviewed articles, 22 book chapters

How I work

Crop improvement is the destination. Data is how I get there.

I do not begin with a model. I begin with what changed in the plant.

Davis, California

Design the project

Start from the question

Evidence carried forward
Questions, sampling plans and controls set before the first sample
What it changes
Define the plant response and the trait worth improving.
See the research projects
Design
Questions, sampling plans and controls
Field
Samples across sites, seasons and growth stages
Bench
dPCR, RNA-seq and metabolites
Analysis
Mechanisms, markers and trait leads

Capabilities

Crop biology is my domain. Data science is how I investigate it.

I connect plant pathology and molecular measurement with computation, statistics, and reproducible research delivery.

  • Crop biology

    Read disease in the plant and design sampling around the biological question.

    • Plant pathology
    • Field sampling
    • Infection assays

  • Molecular diagnostics

    Build quantitative assays that move from laboratory validation to field use.

    • RT-qPCR
    • Digital PCR
    • Assay validation

  • Omics & analytical

    Measure gene activity and chemistry across the same biological system.

    • RNA-seq
    • GC-MS
    • LC-MS/MS

  • Data science

    Turn complex measurements into reproducible models and interpretable evidence.

    • Python & R
    • Machine learning
    • Statistical modeling

  • Research delivery

    Make the work reusable, reviewable, and clear to collaborators and decision-makers.

    • Reproducible pipelines
    • SOPs
    • Scientific writing
Eight technical domains, 96 methods and research practices. Open the full capability index

  • Molecular Diagnostics & Assay Development

    13 items
    • RT-qPCR
    • Digital PCR (dPCR)
    • Assay design
    • Assay validation
    • Primer/probe design
    • Standard-curve calibration
    • Limit of detection
    • Limit of quantification
    • Replicate-variance characterization
    • Nucleic-acid extraction
    • RNA / DNA handling
    • Low-input sample handling
    • MIC determination

  • Molecular Biology

    5 items
    • SDS-PAGE
    • Protein purification
    • Western blotting
    • Northern / Southern blotting
    • Gel electrophoresis

  • NGS Workflow & Library Preparation

    11 items
    • Illumina TAG-seq
    • RNA-Seq
    • Library preparation
    • Library QC
    • RNA isolation
    • Sequencing-run setup
    • UMI-aware quantification
    • Low-input plant-tissue protocols
    • Sample QC
    • Bioanalyzer
    • Qubit

  • Computational Biology & Software Engineering

    16 items
    • Python
    • NumPy
    • pandas
    • scikit-learn
    • R
    • Bioconductor
    • Bash
    • Snakemake
    • SLURM
    • Linux / HPC
    • Pipeline development
    • Git / GitHub
    • conda / mamba
    • Singularity containers
    • Environment management
    • Version control

  • Bioinformatics & Statistical Analysis

    23 items
    • Differential expression (DESeq2, limma-voom, edgeR)
    • Repeated-measures voom
    • Pathway enrichment (GSEA, GO, KEGG, MapMan)
    • Co-expression networks (WGCNA, GENIE3, DGCA)
    • Multi-omics integration (MOFA)
    • Machine-learning classification
    • Biomarker discovery
    • LASSO / random forest / SVM / gradient boosting
    • Stability selection
    • Cross-validation
    • Bayesian inference (brms, Stan)
    • Causal mediation (HIMA)
    • Design of experiments
    • Response-surface methodology
    • Simplex-centroid mixture design
    • Polynomial regression
    • Model-adequacy testing
    • JMP
    • Cross-genome synteny (JCVI MCScan)
    • Protein structure prediction (AlphaFold, Rosetta)
    • Molecular docking (AutoDock)
    • Molecular dynamics (Amber, MM-PBSA)
    • Binding-energy validation

  • Analytical Chemistry & Metabolomics

    10 items
    • GC-MS
    • LC-MS/MS
    • Volatile profiling
    • Targeted metabolomics
    • HPLC
    • FT-IR
    • UV-Vis
    • Fluorescence spectrophotometry
    • In-vitro release profiling
    • Sample preparation

  • Plant Pathology & Disease Biology

    11 items
    • Plant-virus biology
    • Plant-fungal pathogen biology
    • Vineyard / field sampling design
    • Pathogen culture
    • Infection assays
    • Postharvest disease modeling
    • Stored-grain matrices
    • Biopesticide formulation chemistry
    • Mixture-design optimization
    • Chitosan nano-encapsulation
    • Mechanism-of-action characterization (biochemical, structural-computational, transcriptomic)

  • Documentation, Communication & Cross-Functional Work

    7 items
    • SOP authoring
    • Technical-report writing
    • Reproducible-research documentation
    • Manuscript writing
    • Peer review
    • Invited commentary
    • Cross-functional collaboration

Where I have done the work

Roles spanning crop research, data science, and the evaluation of scientific AI.

  • Sep 2023–Present, Davis, CA, USA

    Field + data

    University of California, Davis

    Postdoctoral Scholar

    I lead the field, laboratory, and computational work on California field crop diseases, including Grapevine Red Blotch Virus, from field sampling and dPCR through RNA sequencing and biological interpretation.

    • 300+ crop samples
    • 1.3B+ sequencing reads
    • Reusable analysis pipelines

  • Sep 2025–Present, Remote

    Scientific AI

    Handshake AI

    MOVE Fellow, AI Trainer (part-time)

    I evaluate scientific reasoning in biology for frontier AI systems and document the failure patterns that matter to accuracy and trust.

    • Biology-domain evaluation
    • Reference answers
    • Reasoning failure analysis

  • 2017–2023, India

    Mechanism

    Banaras Hindu University

    Ph.D. Research Scholar

    I developed plant-based treatments for food-borne molds by combining formulation chemistry, wet-lab assays, transcriptomics, and computational modeling.

    • 85% less mold growth
    • Toxin eliminated
    • 6 first-author papers
Full career details

Evidence that has passed peer review.

Journal articles and book chapters spanning plant disease, diagnostics, multi-omics, and food safety.

60published works

Journal articles
38
Book chapters
22
Browse the publication archiveFilter by format and method

Open-source tools

Pipelines and analysis code from my research, published on GitHub.

All repositories on GitHub
  • New release
  • RNASeq pipeline v2.1.1, October 2026

Your study design. Your reads. A traceable analysis.

Turns raw bulk RNA-seq reads into quality reports, gene counts and differential-expression results. Describe the study in an Excel intake, review the configuration, then run it with Snakemake on Linux or through SLURM.

strict release checks passed
35
packages in a locked runtime
580
DE backends: limma-voom, edgeR, DESeq2
3
  • Excel intake
  • Salmon or STAR
  • Preflight design check
  • Snakemake + SLURM
  • GO and WGCNA
View on GitHubRelease notes

  • GRBV transcriptomics analysis

    A 327-sample RNA-seq study of how Grapevine Red Blotch Virus rewires the Merlot transcriptome, and how temperature modulates it.

    • Snakemake + R
    • Pre-submission
    View repository: GRBV transcriptomics analysis

  • RGraphMaker

    Publication-ready R graphs from a plain-language description: 50 graph types across ggplot2, base R and plotly.

    • R
    View repository: RGraphMaker

  • AcademicWriter

    A paper writing and revision system with 14 workflows, from a first draft to a response to reviewers.

    • Python
    View repository: AcademicWriter

Research projects

Field studies, diagnostic methods, and mechanism work across crop and food systems.

Vineyard systems

Why does red blotch become more damaging in hot seasons?

Methods

  • RNA sequencing
  • Snakemake
  • DESeq2

2.6×outcome

Diagnostics

Can infection be measured before symptoms become visible?

Methods

  • RT-qPCR
  • Digital PCR

Before symptomsoutcome

Food safety

Can a food-safe plant treatment suppress aflatoxin without synthetic preservatives?

Methods

  • Nano-encapsulation
  • Chitosan
  • GC-MS

>90%outcome

Field notes

Ideas moving between crop and data

Open research notebook

Where the work can grow next

Better crop decisions, built on stronger biological data.

  • Research Scientist and Data Scientist roles

    Ag-biotech, diagnostics and trait discovery

  • Research collaborations

    Field biology, multi-omics and reproducible analysis

  • Scientific consulting

    From experimental design to decision-ready evidence

Start a conversation

pr0982@gmail.com

Occasional field notes

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