mhc-tp 2.0.1

Cluster immunopeptidomics peptides by HLA/MHC binding motif, with an interactive HTML report

MHC-TP

Cluster immunopeptidomics peptides by their HLA/MHC binding motif and get a ranked table plus a standalone interactive HTML report.

mhc-tp takes a GibbsCluster output folder, correlates each cluster's position-specific scoring matrix against a reference of HLA/MHC class I + II binding motifs (human & mouse), and writes the best allele match per cluster.

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For users

Requirements: Python 3.9–3.11.

1. Install

Clone the repo and install it (editable, so git pull updates the tool):

git clone https://github.com/PurcellLab/MHC-TP.git
cd MHC-TP
pip install -e .

Prefer a one-liner without cloning? pip install git+https://github.com/PurcellLab/MHC-TP.git A virtual environment (python -m venv .venv && source .venv/bin/activate) is recommended.

2. Download the reference data (once)

The reference motifs are fetched from the GitHub release, not bundled:

mhc-tp fetch -s human     # or:  mouse  |  all

3. Run a search

mhc-tp search <gibbscluster_output_dir> -s human -o results/

<gibbscluster_output_dir> is a GibbsCluster run folder (it must contain a matrices/ subdirectory).

Outputs land in results/clust_result/:

file	what it is
correlations.csv	every cluster→allele match above the threshold (hla = display name, formatted = raw key, correlation = PCC)
mhc-tp-result.html	standalone report — open it in any browser

Common options

flag	meaning	default
-s, --species	human or mouse	human
-r, --reference	path to a <species>.parquet (otherwise the fetched one is used)	auto
-t, --threshold	minimum Pearson correlation to report	0.70
-o, --output	output directory	output
--threads	max CPU threads (also $MHC_TP_THREADS)	4
--no-html	write only the CSV	off
-l, --log	also save the coloured session log	off

Run mhc-tp search --help for the full list.

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For contributors / developers

The project uses pixi for a reproducible dev environment (Python 3.11) and a src/ layout packaged with hatchling.

git clone https://github.com/PurcellLab/MHC-TP.git
cd MHC-TP
pixi install            # create the dev env from pixi.lock
pixi run dev-install    # editable-install the package into the env (run once)

pixi run test           # pytest
pixi run lint           # ruff
pixi run fmt            # black

Always run via pixi run … — a bare python may pick up a different interpreter without the pinned dependencies.

Rebuilding the reference data (dev only)

End users never do this. The per-species parquets are built once from the NetMHCpan / NetMHCIIpan packs and uploaded to the release. Embedding the Seq2Logo reference logos (--with-logos) needs a separate Python 2.7 env and is slow — run it on a cluster:

mhc-tp build-ref <species> <classI_pack> <classII_pack> <out.parquet> \
    --with-logos --workers 16
# Seq2Logo itself runs in its own env:  pixi run -e seq2logo ...

Layout

src/mhc_tp/
  cli.py            entry point (mhc-tp)
  engine/           numba correlation search
  refdata/          reference parquet read/write, fetch, schema
  report/           HTML report rendering (data, logos, templates)
  db/               DEV-ONLY reference-pack ingestion
  tui/              Rich console banner, logging, results table
tests/              pytest suite