DeepLC: Retention time prediction for (modified) peptides using Deep Learning.
Project description
DeepLC: Retention time prediction for (modified) peptides using Deep Learning.
Introduction
DeepLC is a retention time predictor for (modified) peptides that employs Deep Learning. It's 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 run with a graphical user interface (GUI) or as a Python package. In the latter case, DeepLC can be used from the command line, or as a python module.
Graphical user interface
Installation
- Download
deeplc_gui.zipfrom the latest release and unzip. - Install DeepLC GUI with
install_gui_windows.batorinstall_gui_linux.sh, depending on your operating system. - Run DeepLC GUI by running the
deeplc_gui.jar.
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)
For a more elaborate example, see examples/deeplc_example.py .
Input files
DeepLC expects comma-separated values (CSV) with the following columns:
seq: unmodified peptide sequencesmodifications: MS2PIP-style formatted modifications: Every modification is listed aslocation|name, separated by a pipe (|) between the location, the name, and other modifications.locationis an integer counted starting at 1 for the first AA. 0 is reserved for N-terminal modifications, -1 for C-terminal modifications.namehas to correspond to a Unimod (PSI-MS) name.tr: retention time (only required for calibration)
For example:
seq,modifications,tr
AAGPSLSHTSGGTQSK,,12.1645
AAINQKLIETGER,6|Acetyl,34.095
AANDAGYFNDEMAPIEVKTK,12|Oxidation|18|Acetyl,37.3765
See examples/datasets for more examples.
Prediction models
DeepLC comes with multiple CNN models trained on data from various experimental settings:
| Model filename | Experimental settings | Publication |
|---|---|---|
| full_hc_dia_fixed_mods.hdf5 | Reverse phase | Rosenberger et al. 2014 |
| full_hc_LUNA_HILIC_fixed_mods.hdf5 | HILIC | Spicer et al. 2018 |
| full_hc_LUNA_SILICA_fixed_mods.hdf5 | HILIC | Spicer et al. 2018 |
| full_hc_PXD000954_fixed_mods.hdf5 | Reverse phase | Rosenberger et al. 2014 |
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.
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