Usage

Web application

A hosted web application is available at iomics.ugent.be/deeplc — no installation required.

Graphical interface

Windows: download the one-click installer from the releases page.

Other platforms: install with GUI dependencies:

pip install deeplc[gui]

Then launch as a browser app or native desktop window:

deeplc gui            # opens in browser
deeplc gui --native   # opens as desktop window

Command line interface

Install DeepLC:

pip install deeplc
# or
conda install -c bioconda -c conda-forge deeplc

Predict retention times for a PSM file:

deeplc predict <psm_file>

The input file format is inferred from the extension. All formats supported by psm_utils are accepted, including Sage, MaxQuant msms.txt, mzTab, and others. A tab-separated file with at least peptidoform and spectrum_id columns is also accepted directly.

For calibration, pass a reference file with observed retention times using the --psm-file-reference option. If no reference file is provided, DeepLC attempts automatic calibration from high-confidence PSMs in the input file.

For a full list of options:

deeplc predict --help

Python API

The public API consists of a small set of functions in deeplc.core.

Direct prediction

Predict retention times without calibration:

from psm_utils.io import read_file
from deeplc import predict

psm_list = read_file("results.sage.tsv")
rt_predictions = predict(psm_list)  # numpy array, shape (n,)

Prediction with calibration

Calibrate to observed retention times in a reference set, then predict:

from psm_utils.io import read_file
from deeplc import predict_and_calibrate

psm_list = read_file("results.sage.tsv")

# Auto-calibration: selects reference PSMs from psm_list automatically
calibrated_rt = predict_and_calibrate(psm_list)

# Or provide an explicit reference set
psm_list_reference = read_file("reference.tsv")
calibrated_rt = predict_and_calibrate(psm_list, psm_list_reference=psm_list_reference)

Fine-tuning

Fine-tune the model to a specific dataset, then predict:

from psm_utils.io import read_file
from deeplc import finetune_and_predict

psm_list = read_file("results.sage.tsv")
calibrated_rt = finetune_and_predict(psm_list)

For lower-level control, deeplc.calibrate() and deeplc.finetune() return a fitted Calibration instance and a fine-tuned model respectively, which can be reused across multiple prediction calls.

See the API reference for all parameters and return types.

Input file format

DeepLC accepts any PSM file format supported by psm_utils. The format is inferred from the file extension, or can be set explicitly with --psm-filetype.

A tab-separated file with the following columns is also accepted:

spectrum_id  peptidoform     retention_time
  AAGPSLSHTSGGTQSK/2      12.16
  AAINQK[Acetyl]LIETGER/2 34.10
  AANDAGYFNDEM[Oxidation]APIEVK/2 37.38

peptidoform: Peptide sequence in ProForma 2.0 notation (see Modifications below).
spectrum_id: Unique identifier for each PSM.
retention_time: Observed retention time, required for calibration and fine-tuning.

See example datasets for additional input file examples.

Modifications

Modifications are specified as bracketed labels in ProForma 2.0 notation. Labels must be resolvable to a known chemical formula:

A name or accession from a controlled vocabulary: Unimod or PSI-MOD (e.g. Oxidation, U:21, MOD:00046)
An elemental formula (e.g. Formula:C2H2O)

All modifications — including fixed modifications such as carbamidomethylation — must be present in the peptidoform string. Labels that cannot be resolved to a chemical formula (e.g. mass shifts) are ignored; predictions fall back to the unmodified peptide.

DeepLC can predict retention times for any modification, but accuracy depends on whether similar modifications were seen during training. For modifications involving elements or structural changes not well represented in the training data, prediction accuracy may be lower. In such cases, `fine-tuning <Fine-tuning_>`_ the model on a dataset containing the modification of interest is recommended.

Custom modifications not in Unimod or PSI-MOD can be encoded with an elemental formula directly:

PEPTI[Formula:C12H20O2]DE