JRIF

Peptide retention time (RT) and ion mobility (IM) prediction.

pip install jrif

Models

Weights are bundled with the package.

Features

• Calibrate a model to your experiment using your identified peptides
• Predict RT/IM for a full spectral library, output in your experiment's units
• New modifications in your data are handled automatically
• Runs on GPU when available (CUDA or Apple), otherwise CPU

Usage

Calibrate on your IDs, then predict your library:

import pandas as pd
from jrif import RTPredictor
from jrif.checkpoints import checkpoint_path

ids = pd.read_csv("psm_ids.tsv", sep="\t")      # columns: seq, rt
lib = pd.read_csv("library.tsv", sep="\t")      # column:  seq

p = RTPredictor.from_checkpoint(checkpoint_path("rt_psmtag"))
p.calibrate(ids)
lib["pred_rt"] = p.predict(lib)

Ion mobility (needs a charge column):

from jrif import IMPredictor

m = IMPredictor.from_checkpoint(checkpoint_path("im_psmtag"))
m.calibrate(ids)                                # columns: seq, charge, im
lib["pred_im"] = m.predict(lib)                 # columns: seq, charge

Sequence format: residues, with modifications as delta masses (S[+79.9663], C[+57.0215]) or UniMod (S(UniMod:21)). N-terminal modifications are a leading n[+mass], C-terminal ones a trailing c[+mass]. A bare C is unmodified cysteine. Masses match at the precision you write them, so S[80] uses the model's S[+79.9663]. The older spellings C[160], M[147], K[308] and .[308] are also accepted.

Modifications the model has never seen (e.g. S[80]) don't need any setup: calibrate() adds new tokens to the vocabulary and learns them from your IDs automatically.

Save and reload a calibrated model:

p.save("my_experiment.pt")
p = RTPredictor.from_checkpoint("my_experiment.pt")

Training new base models (maintainers)

uv run jrif-train-label-free --data-path <library.tsv>
uv run jrif-train-psmtag    --data-path <library.tsv | report-lib.parquet>