ideeplc 1.1.0

iDeepLC: A deep Learning-based retention time predictor for unseen modified peptides with a novel encoding system

iDeepLC: A deep Learning-based retention time predictor for unseen modified peptides with a novel encoding system

Overview

iDeepLC is a deep learning-based tool for retention time prediction in proteomics.

Features

• Retention Time Prediction: Predict retention times for peptides, including modified ones.
• Fine-Tuning: Fine-tune the pre-trained model for specific datasets.
• Visualization: Generate scatter plots and other figures for analysis.

installation

Intall the package using pip:

pip install iDeepLC

Usage

The iDeepLC package provides a CLI for easy usage. Below are some examples:

Prediction

ideeplc --input <path/to/peptide_file.csv> --save

Fine-tuning

ideeplc --input <path/to/peptide_file.csv> --save --finetune

Calibration

ideeplc --input <path/to/peptide_file.csv> --save --calibrate

Example

ideeplc --input ./data/example_input/Hela_deeprt --save --finetune --calibrate

For more detailed CLI usage, you can run:

ideeplc --help

Input file format

The input file should be a CSV file with the following columns:

• seq: The amino acid sequence of the peptide. (e.g., ACDEFGHIKLMNPQRSTVWY)
• modifications: A string representing modifications in the sequence. (e.g., 11|Oxidation|16|Phospho)
• tr: The retention time of the peptide in seconds. (e.g., 1285.63)

For example:

NQDLISENK,,2705.724
LGSPPPHK,3|Phospho,2029.974
RMQSLQLDCVAVPSSR,2|Oxidation|4|Phospho,4499.832

Citation

If you use iDeepLC in your research, please cite our paper:

📄 iDeepLC: A deep Learning-based retention time predictor for unseen modified peptides with a novel encoding system
🖊 Alireza Nameni, Arthur Declercq, Ralf Gabriels, Robbe Devreese, Lennart Martens, Sven Degroeve , and Robbin Bouwmeester
📅 2025
🔗 DOI