Metadata-Version: 2.1
Name: deeplc
Version: 0.1.5
Summary: DeepLC: Retention time prediction for (modified) peptides using Deep Learning.
Home-page: http://compomics.github.io/projects/DeepLC
Author: Robbin Bouwmeester, Niels Hulstaert, Ralf Gabriels, Prof. Lennart Martens, Prof. Sven Degroeve
Author-email: Robbin.Bouwmeester@UGent.be
License: apache-2.0
Project-URL: Documentation, http://compomics.github.io/projects/DeepLC
Project-URL: Source, https://github.com/compomics/DeepLC
Project-URL: Tracker, https://github.com/compomics/DeepLC/issues
Description: <img src="https://github.com/compomics/DeepLC/raw/master/img/deeplc_logo.png"
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        DeepLC: Retention time prediction for (modified) peptides using Deep Learning.
        
        ---
        
        - [Introduction](#introduction)
        - [Graphical user interface](#graphical-user-interface)
        - [Python package](#python-package)
          - [Installation](#installation)
          - [Command line interface](#command-line-interface)
          - [Python module](#python-module)
        - [Input files](#input-files)
        - [Prediction models](#prediction-models)
        
        ---
        
        ## 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 GUI](https://img.shields.io/badge/download-GUI-green?style=flat-square)](https://github.com/compomics/DeepLC/releases/latest/)
        
        1. Download `deeplc_gui.zip` from the
        [latest release](https://github.com/compomics/DeepLC/releases/latest/) and
        unzip.
        2. Install DeepLC GUI with `install_gui_windows.bat` or `install_gui_linux.sh`,
        depending on your operating system.
        3. Run DeepLC GUI by running the `deeplc_gui.jar`.
        
        
        ## Python package
        
        ### Installation
        
        [![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat-square)](http://bioconda.github.io/recipes/deeplc/README.html)
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        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:
        
        ```sh
        deeplc --file_pred <path/to/peptide_file.csv>
        ```
        
        We highly recommend to add a peptide file with known retention times for
        calibration:
        
        ```sh
        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:
        
        ```python
        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](https://github.com/compomics/DeepLC/blob/master/examples/deeplc_example.py)
        .
        
        ## Input files
        
        DeepLC expects comma-separated values (CSV) with the following columns:
        
        - `seq`: unmodified peptide sequences
        - `modifications`: MS2PIP-style formatted modifications: Every modification is
          listed as `location|name`, separated by a pipe (`|`) between the location, the
          name, and other modifications. `location` is an integer counted starting at 1
          for the first AA. 0 is reserved for N-terminal modifications, -1 for
          C-terminal modifications. `name` has to correspond to a Unimod (PSI-MS) name.
        - `tr`: retention time (only required for calibration)
        
        For example:
        
        ```csv
        seq,modifications,tr
        AAGPSLSHTSGGTQSK,,12.1645
        AAINQKLIETGER,6|Acetyl,34.095
        AANDAGYFNDEMAPIEVKTK,12|Oxidation|18|Acetyl,37.3765
        ```
        
        See
        [examples/datasets](https://github.com/compomics/DeepLC/tree/master/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](https://doi.org/10.1038/sdata.2014.31) |
        | full_hc_LUNA_HILIC_fixed_mods.hdf5 | HILIC | [Spicer et al. 2018](https://doi.org/10.1016/j.chroma.2017.12.046) |
        | full_hc_LUNA_SILICA_fixed_mods.hdf5 | HILIC | [Spicer et al. 2018](https://doi.org/10.1016/j.chroma.2017.12.046) |
        | full_hc_PXD000954_fixed_mods.hdf5 | Reverse phase | [Rosenberger et al. 2014](https://doi.org/10.1038/sdata.2014.31) |
        
        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.
        
Keywords: DeepLC,Proteomics,deep learning,peptides,retention time,prediction
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Development Status :: 4 - Beta
Requires-Python: >=3.6,<3.8
Description-Content-Type: text/markdown
