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pathotypr

Lineage classification and marker-driven genotyping — from assemblies or raw reads.

pathotypr is a Rust toolkit that classifies microbial genomes into lineages and genotypes them against user-defined marker panels. It works with both assembled genomes (FASTA) and raw sequencing reads (FASTQ), runs on a single laptop, and ships with a native desktop GUI.

pathotypr workflow pathotypr workflow

Validated for M. tuberculosis only

Nothing is hard-coded to one organism, so any species works in principle. In practice pathotypr has only been validated on the MTBC: treat other organisms as exploratory and check them against a truth set you trust.

Five commands, one binary

Command What it does Input
train Build a Random Forest classifier from labeled genomes FASTA
predict Assign lineages using a trained model FASTA + model
classify Call known SNP markers in assemblies FASTA + markers
split-fastq Alignment-free genotyping from reads FASTQ + markers
match Find the closest reference genome FASTQ + references

Highlights

  •   Organism-agnostic by design

    Bring your own markers for any pathogen — nothing is hard-coded to a single organism. Validated on MTBC only; other species are untested.

  •   Fast

    Rust + SIMD gzip + parallel k-mers process a sample in roughly 1–2 seconds.

  •   Desktop GUI

    A native app built with Tauri — drag-and-drop files, no server required.

  •   Excel + TSV output

    Tabular results everywhere, with interactive visualizations in the GUI.

Quick start

# Call known markers in an assembled genome
pathotypr classify \
  -m markers.tsv -r reference.fasta \
  -i sample.fasta -o results --excel

# Or predict a lineage with a trained model
pathotypr predict -i query.fasta -m model.pathotypr.zst -o predictions.tsv

# Alignment-free genotyping straight from reads
pathotypr split-fastq \
  -m markers.tsv -r reference.fasta \
  -i reads_R1.fastq.gz -i reads_R2.fastq.gz --paired \
  -o genotype

# Find the closest reference genome
pathotypr match -i reads_R1.fastq.gz reads_R2.fastq.gz \
  -r references.fasta -o match.tsv

# Train a Random Forest lineage classifier from labeled genomes
pathotypr train -i labeled_genomes.fasta -o model.pathotypr.zst

Excel output

Add --excel to classify, predict, split-fastq or match to also generate .xlsx files alongside the TSVs (train writes its reports as TSV only).

Where to next

  •   Installation

    Pre-built desktop apps, Bioconda, or build the CLI/GUI from source.

  •   Commands

    Detailed usage, options, and output formats for each subcommand.

  •   Input formats

    Specifications for training FASTA, marker TSVs, and input lists.

  •   Algorithms

    The design and data structures behind each module.

Citation

If you use pathotypr, please cite:

Ruiz-Rodriguez P, Coscollá M. Pathotypr: harmonised MTBC lineage assignment and resistance-associated variant detection for genomic surveillance. bioRxiv (2026). doi: 10.64898/2026.03.24.714002

See Citation for BibTeX and RIS entries, the software and marker DOI, and the licence.