deeplc 4.0.0a2

DeepLC: Retention time prediction for (modified) peptides using Deep Learning.

DeepLC: Retention time prediction for peptides carrying any modification.

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• Introduction
• Citation
• Usage
  • Web application
  • Graphical user interface
  • Python package
    • Installation
    • Command line interface
    • Python module
  • Input files
  • Prediction models

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Introduction

DeepLC is a retention time predictor for peptides. Its strength lies in the fact that it can accurately predict retention times for modified peptides, even if hasn't seen said modification during training.

DeepLC can be used through the web application or as a Python package. In the latter case, DeepLC can be used from the command line, or as a Python module.

Citation

If you use DeepLC for your research, please use the following citation:

DeepLC can predict retention times for peptides that carry as-yet unseen modifications
Robbin Bouwmeester, Ralf Gabriels, Niels Hulstaert, Lennart Martens & Sven Degroeve
Nature Methods 18, 1363–1369 (2021) doi: 10.1038/s41592-021-01301-5

Usage

Web application

Just go to iomics.ugent.be/deeplc and get started!

Python package

Installation

Install with conda, using the bioconda and conda-forge channels: conda install -c bioconda -c conda-forge deeplc

Or install with pip: pip install deeplc

Command line interface

To use the DeepLC CLI, run:

deeplc --file_pred <path/to/peptide_file.csv>

We highly recommend to add a peptide file with known retention times for calibration:

deeplc --file_pred  <path/to/peptide_file.csv> --file_cal <path/to/peptide_file_with_tr.csv>

For an overview of all CLI arguments, run deeplc --help.

Python module

Minimal example:

import pandas as pd
from deeplc import DeepLC

peptide_file = "datasets/test_pred.csv"
calibration_file = "datasets/test_train.csv"

pep_df = pd.read_csv(peptide_file, sep=",")
pep_df['modifications'] = pep_df['modifications'].fillna("")

cal_df = pd.read_csv(calibration_file, sep=",")
cal_df['modifications'] = cal_df['modifications'].fillna("")

dlc = DeepLC()
dlc.calibrate_preds(seq_df=cal_df)
preds = dlc.make_preds(seq_df=pep_df)

Minimal example with psm_utils:

import pandas as pd

from psm_utils.psm import PSM
from psm_utils.psm_list import PSMList
from psm_utils.io import write_file

from deeplc import DeepLC

infile = pd.read_csv("https://github.com/compomics/DeepLC/files/13298024/231108_DeepLC_input-peptides.csv")
psm_list = []

for idx,row in infile.iterrows():
    seq = row["modifications"].replace("(","[").replace(")","]")
    
    if seq.startswith("["):
        idx_nterm = seq.index("]")
        seq = seq[:idx_nterm+1]+"-"+seq[idx_nterm+1:]
        
    psm_list.append(PSM(peptidoform=seq,spectrum_id=idx))

psm_list = PSMList(psm_list=psm_list)

infile = pd.read_csv("https://github.com/compomics/DeepLC/files/13298022/231108_DeepLC_input-calibration-file.csv")
psm_list_calib = []

for idx,row in infile.iterrows():
    seq = row["seq"].replace("(","[").replace(")","]")
    
    if seq.startswith("["):
        idx_nterm = seq.index("]")
        seq = seq[:idx_nterm+1]+"-"+seq[idx_nterm+1:]
        
    psm_list_calib.append(PSM(peptidoform=seq,retention_time=row["tr"],spectrum_id=idx))

psm_list_calib = PSMList(psm_list=psm_list_calib)

dlc = DeepLC()
dlc.calibrate_preds(psm_list_calib)
preds = dlc.make_preds(seq_df=psm_list)

For a more elaborate example, see examples/deeplc_example.py .

Input files

DeepLC accepts any PSM file format supported by psm_utils, including MaxQuant msms.txt, Sage, MSAmanda, Percolator, and many more. The file format is automatically inferred from the file extension, or can be specified explicitly with the --psm-filetype option.

At a minimum, a tab-separated file with a peptidoform and spectrum_id column is accepted. Peptidoforms must be in ProForma 2.0 notation. For calibration or fine-tuning, a retention_time column is also required.

For example:

spectrum_id	peptidoform	retention_time
0	AAGPSLSHTSGGTQSK/2	12.16
1	AAINQK[Acetyl]LIETGER/2	34.10
2	AANDAGYFNDEM[Oxidation]APIEVK[Acetyl]TK/3	37.38

See examples/datasets for more examples.

Prediction models

DeepLC comes with multiple CNN models trained on data from various experimental settings. By default, DeepLC selects the best model based on the calibration dataset. If no calibration is performed, the first default model is selected. Always keep note of the used models and the DeepLC version. The current version comes with:

Model filename	Experimental settings	Publication
full_hc_PXD005573_mcp_8c22d89667368f2f02ad996469ba157e.hdf5	Reverse phase	Bruderer et al. 2017
full_hc_PXD005573_mcp_1fd8363d9af9dcad3be7553c39396960.hdf5	Reverse phase	Bruderer et al. 2017
full_hc_PXD005573_mcp_cb975cfdd4105f97efa0b3afffe075cc.hdf5	Reverse phase	Bruderer et al. 2017

For all the full models that can be used in DeepLC (including some TMT models!) please see:

https://github.com/RobbinBouwmeester/DeepLCModels

Naming convention for the models is as follows:

[full_hc]_[dataset]_[fixed_mods]_[hash].hdf5

The different parts refer to:

full_hc - flag to indicated a finished, trained, and fully optimized model

dataset - name of the dataset used to fit the model (see the original publication, supplementary table 2)

fixed mods - flag to indicate fixed modifications were added to peptides without explicit indication (e.g., carbamidomethyl of cysteine)

hash - indicates different architectures, where "1fd8363d9af9dcad3be7553c39396960" indicates CNN filter lengths of 8, "cb975cfdd4105f97efa0b3afffe075cc" indicates CNN filter lengths of 4, and "8c22d89667368f2f02ad996469ba157e" indicates filter lengths of 2

Q&A

See the FAQ in the documentation.