probe-tester

probe-tester is a modular Python toolkit for evaluating primers and probes against large collections of genomes. It supports automated download of NCBI genomes, in silico PCR (IPCRESS) matching, summary/statistics reporting, and modern, user-friendly CLI progress indicators.

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Features

• Automated download of GenBank genomes by species, genus, or custom set (NCBI Datasets CLI required)
• Flexible in silico PCR (IPCRESS): test any forward/reverse/probe set on downloaded genomes
• Multiprocessing support: scale up to thousands of genomes
• Rich progress bars (if available) or clean fallback output
• Summary and specificity panelization: separates “target” and “non-target” organisms for true molecular diagnostics benchmarking
• CSV/Markdown export, table summaries, and metadata in results
• Graceful error handling, dependency checks, and quality-of-life CLI features

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Quick Start

Requirements

• python >= 3.8
• ncbi-datasets-cli
• Python packages: (optionally) rich,

Easiest, install with conda

mamba env create -f environment.yaml

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1. Download Genomes

Download a specific taxonomic level of genomes:

Ex. Download 5 genomes for the species "Mycoplasmopsis bovis:

python main.py download --taxon "Mycoplasmopsis bovis" --mode species --max-genomes 5

Or knowing the target species, download genomes from the upper taxonomic level.

Ex. Download 5 genomes for all members of the genus that "Mycoplasmopsis bovis" belongs to.

python main.py download --taxon "Mycoplasmopsis bovis" --mode parent --max-genomes 5

Add --dry-run to preview downloads, or --force to skip confirmation for large jobs.

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2. Run the Assay

Test your primers and probe against all genomes:

python main.py assay \
  --forward TCTAATTTTTTCATCATCGCTAATGC \
  --reverse TCAGGCCTTTGCTACAATGAAC \
  --probe AACTGCATCATATCACATACT \
  --run-name Parker-2017 \
  --threads 4

• Uses multiprocessing (--threads) if desired for speed.
• IPCRESS is called under the hood.
• Results are written to a JSON file (named by run or timestamp).

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3. Summarize Results

Get a sensitivity/specificity table, with targets vs. non-targets, in pretty, CSV, or markdown format:

python main.py summarize --input results-Parker-2017.json \
  --target "Mycoplasmopsis-bovis" \
  --format text

Or for all Mycoplasmopsis- species:

python main.py summarize --input results-Parker-2017.json \
  --target "Mycoplasmopsis-*"

Export to CSV/markdown for figures or publication:

python main.py summarize --input results.json --format csv --target "Mycoplasmopsis-bovis"

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Advanced Usage

• List available species/genomes:

  python main.py list --taxon "Mycoplasmopsis" --mode parent

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Best Practices

• Use --dry-run to preview large downloads before running them.
• Use --run-name for descriptive result files.
• Use --threads N on modern CPUs for much faster assay runs.
• Regularly update the NCBI Datasets CLI for the latest assembly info.

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Example Full Workflow

# Download up to 10 genomes for all organisms belonging to Mycoplasmopsis bovis's genus.
python main.py download --taxon "Mycoplasmopsis bovis" --max-genomes 10

# Run probe set against all genomes, using 8 CPU threads
python main.py assay --forward ... --reverse ... --probe ... --threads 8 --run-name my-panel

# Summarize, showing only Mycoplasmopsis-bovis as the target
python main.py summarize --input results-my-panel.json --target "Mycoplasmopsis-bovis" --format csv

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Happy analyzing!