Metadata-Version: 2.1
Name: CPred
Version: 0.0.3
Summary: CPred: A deep learning framework for predicting the charge state distribution in modified and unmodified peptides in ESI
Author-email: Frédérique Vilenne <frederique.vilenne@uhasselt.be>
License: Apache License
                                   Version 2.0, January 2004
                                http://www.apache.org/licenses/
        
           TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
        
           1. Definitions.
        
              "License" shall mean the terms and conditions for use, reproduction,
              and distribution as defined by Sections 1 through 9 of this document.
        
              "Licensor" shall mean the copyright owner or entity authorized by
              the copyright owner that is granting the License.
        
              "Legal Entity" shall mean the union of the acting entity and all
              other entities that control, are controlled by, or are under common
              control with that entity. For the purposes of this definition,
              "control" means (i) the power, direct or indirect, to cause the
              direction or management of such entity, whether by contract or
              otherwise, or (ii) ownership of fifty percent (50%) or more of the
              outstanding shares, or (iii) beneficial ownership of such entity.
        
              "You" (or "Your") shall mean an individual or Legal Entity
              exercising permissions granted by this License.
        
              "Source" form shall mean the preferred form for making modifications,
              including but not limited to software source code, documentation
              source, and configuration files.
        
              "Object" form shall mean any form resulting from mechanical
              transformation or translation of a Source form, including but
              not limited to compiled object code, generated documentation,
              and conversions to other media types.
        
              "Work" shall mean the work of authorship, whether in Source or
              Object form, made available under the License, as indicated by a
              copyright notice that is included in or attached to the work
              (an example is provided in the Appendix below).
        
              "Derivative Works" shall mean any work, whether in Source or Object
              form, that is based on (or derived from) the Work and for which the
              editorial revisions, annotations, elaborations, or other modifications
              represent, as a whole, an original work of authorship. For the purposes
              of this License, Derivative Works shall not include works that remain
              separable from, or merely link (or bind by name) to the interfaces of,
              the Work and Derivative Works thereof.
        
              "Contribution" shall mean any work of authorship, including
              the original version of the Work and any modifications or additions
              to that Work or Derivative Works thereof, that is intentionally
              submitted to Licensor for inclusion in the Work by the copyright owner
              or by an individual or Legal Entity authorized to submit on behalf of
              the copyright owner. For the purposes of this definition, "submitted"
              means any form of electronic, verbal, or written communication sent
              to the Licensor or its representatives, including but not limited to
              communication on electronic mailing lists, source code control systems,
              and issue tracking systems that are managed by, or on behalf of, the
              Licensor for the purpose of discussing and improving the Work, but
              excluding communication that is conspicuously marked or otherwise
              designated in writing by the copyright owner as "Not a Contribution."
        
              "Contributor" shall mean Licensor and any individual or Legal Entity
              on behalf of whom a Contribution has been received by Licensor and
              subsequently incorporated within the Work.
        
           2. Grant of Copyright License. Subject to the terms and conditions of
              this License, each Contributor hereby grants to You a perpetual,
              worldwide, non-exclusive, no-charge, royalty-free, irrevocable
              copyright license to reproduce, prepare Derivative Works of,
              publicly display, publicly perform, sublicense, and distribute the
              Work and such Derivative Works in Source or Object form.
        
           3. Grant of Patent License. Subject to the terms and conditions of
              this License, each Contributor hereby grants to You a perpetual,
              worldwide, non-exclusive, no-charge, royalty-free, irrevocable
              (except as stated in this section) patent license to make, have made,
              use, offer to sell, sell, import, and otherwise transfer the Work,
              where such license applies only to those patent claims licensable
              by such Contributor that are necessarily infringed by their
              Contribution(s) alone or by combination of their Contribution(s)
              with the Work to which such Contribution(s) was submitted. If You
              institute patent litigation against any entity (including a
              cross-claim or counterclaim in a lawsuit) alleging that the Work
              or a Contribution incorporated within the Work constitutes direct
              or contributory patent infringement, then any patent licenses
              granted to You under this License for that Work shall terminate
              as of the date such litigation is filed.
        
           4. Redistribution. You may reproduce and distribute copies of the
              Work or Derivative Works thereof in any medium, with or without
              modifications, and in Source or Object form, provided that You
              meet the following conditions:
        
              (a) You must give any other recipients of the Work or
                  Derivative Works a copy of this License; and
        
              (b) You must cause any modified files to carry prominent notices
                  stating that You changed the files; and
        
              (c) You must retain, in the Source form of any Derivative Works
                  that You distribute, all copyright, patent, trademark, and
                  attribution notices from the Source form of the Work,
                  excluding those notices that do not pertain to any part of
                  the Derivative Works; and
        
              (d) If the Work includes a "NOTICE" text file as part of its
                  distribution, then any Derivative Works that You distribute must
                  include a readable copy of the attribution notices contained
                  within such NOTICE file, excluding those notices that do not
                  pertain to any part of the Derivative Works, in at least one
                  of the following places: within a NOTICE text file distributed
                  as part of the Derivative Works; within the Source form or
                  documentation, if provided along with the Derivative Works; or,
                  within a display generated by the Derivative Works, if and
                  wherever such third-party notices normally appear. The contents
                  of the NOTICE file are for informational purposes only and
                  do not modify the License. You may add Your own attribution
                  notices within Derivative Works that You distribute, alongside
                  or as an addendum to the NOTICE text from the Work, provided
                  that such additional attribution notices cannot be construed
                  as modifying the License.
        
              You may add Your own copyright statement to Your modifications and
              may provide additional or different license terms and conditions
              for use, reproduction, or distribution of Your modifications, or
              for any such Derivative Works as a whole, provided Your use,
              reproduction, and distribution of the Work otherwise complies with
              the conditions stated in this License.
        
           5. Submission of Contributions. Unless You explicitly state otherwise,
              any Contribution intentionally submitted for inclusion in the Work
              by You to the Licensor shall be under the terms and conditions of
              this License, without any additional terms or conditions.
              Notwithstanding the above, nothing herein shall supersede or modify
              the terms of any separate license agreement you may have executed
              with Licensor regarding such Contributions.
        
           6. Trademarks. This License does not grant permission to use the trade
              names, trademarks, service marks, or product names of the Licensor,
              except as required for reasonable and customary use in describing the
              origin of the Work and reproducing the content of the NOTICE file.
        
           7. Disclaimer of Warranty. Unless required by applicable law or
              agreed to in writing, Licensor provides the Work (and each
              Contributor provides its Contributions) on an "AS IS" BASIS,
              WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
              implied, including, without limitation, any warranties or conditions
              of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
              PARTICULAR PURPOSE. You are solely responsible for determining the
              appropriateness of using or redistributing the Work and assume any
              risks associated with Your exercise of permissions under this License.
        
           8. Limitation of Liability. In no event and under no legal theory,
              whether in tort (including negligence), contract, or otherwise,
              unless required by applicable law (such as deliberate and grossly
              negligent acts) or agreed to in writing, shall any Contributor be
              liable to You for damages, including any direct, indirect, special,
              incidental, or consequential damages of any character arising as a
              result of this License or out of the use or inability to use the
              Work (including but not limited to damages for loss of goodwill,
              work stoppage, computer failure or malfunction, or any and all
              other commercial damages or losses), even if such Contributor
              has been advised of the possibility of such damages.
        
           9. Accepting Warranty or Additional Liability. While redistributing
              the Work or Derivative Works thereof, You may choose to offer,
              and charge a fee for, acceptance of support, warranty, indemnity,
              or other liability obligations and/or rights consistent with this
              License. However, in accepting such obligations, You may act only
              on Your own behalf and on Your sole responsibility, not on behalf
              of any other Contributor, and only if You agree to indemnify,
              defend, and hold each Contributor harmless for any liability
              incurred by, or claims asserted against, such Contributor by reason
              of your accepting any such warranty or additional liability.
        
           END OF TERMS AND CONDITIONS
        
           APPENDIX: How to apply the Apache License to your work.
        
              To apply the Apache License to your work, attach the following
              boilerplate notice, with the fields enclosed by brackets "[]"
              replaced with your own identifying information. (Don't include
              the brackets!)  The text should be enclosed in the appropriate
              comment syntax for the file format. We also recommend that a
              file or class name and description of purpose be included on the
              same "printed page" as the copyright notice for easier
              identification within third-party archives.
        
           Copyright [yyyy] [name of copyright owner]
        
           Licensed under the Apache License, Version 2.0 (the "License");
           you may not use this file except in compliance with the License.
           You may obtain a copy of the License at
        
               http://www.apache.org/licenses/LICENSE-2.0
        
           Unless required by applicable law or agreed to in writing, software
           distributed under the License is distributed on an "AS IS" BASIS,
           WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
           See the License for the specific language governing permissions and
           limitations under the License.
Project-URL: Homepage, https://github.com/VilenneFrederique/CPred
Keywords: Deep learning,proteomics,Charge state
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: Apache Software License
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE.txt
Requires-Dist: docutils
Requires-Dist: pandas >=2.2
Requires-Dist: numpy >=1.26
Requires-Dist: regex >=2023.12.25
Requires-Dist: openpyxl >=3.1.2
Requires-Dist: tensorflow >=2.15
Requires-Dist: scikit-learn >=1.4
Requires-Dist: keras-tuner >=1.4.6

<img src="https://github.com/VilenneFrederique/CPred/blob/main/img/CPred_logo.png"
width="550" height="300" /> <br/><br/>


[![GitHub release](https://flat.badgen.net/github/release/VilenneFrederique/CPred)](https://github.com/VilenneFredericonque/CPred/releases/latest/)
[![PyPI](https://flat.badgen.net/pypi/v/cpred)](https://pypi.org/project/cpred/)
[![Conda](https://img.shields.io/conda/vn/bioconda/deeplc?style=flat-square)](https://bioconda.github.io/recipes/deeplc/README.html)
[![GitHub Workflow Status](https://flat.badgen.net/github/checks/compomics/deeplc/)](https://github.com/compomics/deeplc/actions/)
[![License](https://flat.badgen.net/github/license/VilenneFrederique/cpred)](https://www.apache.org/licenses/LICENSE-2.0)


CPred: Charge State Prediction for Modified and Unmodified Peptides in Electrospray Ionization

---

- [Introduction](#introduction)
- [Usage](#usage)
  - [Python package](#python-package)
    - [Installation](#installation)
    - [Command line interface](#command-line-interface)
    - [Python module](#python-module)
  - [Input files](#input-files)
  - [Prediction models](#prediction-models)
- [Q&A](#qa)
- [Citation](#citation)

---

## Introduction

CPred is a neural network capable of predicting the charge state distribution for
modified and unmodified peptides in electrospray ionisation. By summarising the 
modifications as measures of mass and atomic compositions, the model is capable of
generalising unseen modifications during training. 

The model is available as a Python package, installable through Pypi and conda.
This also makes it possible to use from the command-line-interface.

## Usage


### Python package

#### Installation

[![install with bioconda](https://flat.badgen.net/badge/install%20with/bioconda/green)](http://bioconda.github.io/recipes/deeplc/README.html)
[![install with pip](https://flat.badgen.net/badge/install%20with/pip/green)](http://bioconda.github.io/recipes/deeplc/README.html)


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

Or install with pip:
`pip install CPred`


#### Python module

Minimal example:

```python
import pandas as pd
from CPred import CPred

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)
```

#### 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`.


For a more elaborate example, see
[examples/deeplc_example.py](https://github.com/compomics/DeepLC/blob/master/examples/deeplc_example.py)

## Citation

When using CPred, please use the cite the following article:
>**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](http://dx.doi.org/10.1038/s41592-021-01301-5)
.

### 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.

The table above is for an old version of DeepLC, the current version comes with:

| Model filename | Experimental settings | Publication |
| - | - | - |
| full_hc_hela_hf_psms_aligned_1fd8363d9af9dcad3be7553c39396960.hdf5 | Reverse phase | [Kelstrup et al. 2018](https://doi.org/10.1021/acs.jproteome.7b006021) |
| full_hc_hela_hf_psms_aligned_8c22d89667368f2f02ad996469ba157e.hdf5 | Reverse phase | [Kelstrup et al. 2018](https://doi.org/10.1021/acs.jproteome.7b00602) |
| full_hc_hela_hf_psms_aligned_cb975cfdd4105f97efa0b3afffe075cc.hdf5 | Reverse phase | [Kelstrup et al. 2018](https://doi.org/10.1021/acs.jproteome.7b00602) |
| full_hc_PXD005573_mcp_cb975cfdd4105f97efa0b3afffe075cc.hdf5 | Reverse phase | [Bruderer et al. 2017](https://pubmed.ncbi.nlm.nih.gov/29070702/) |

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

[https://github.com/RobbinBouwmeester/DeepLCModels](https://github.com/RobbinBouwmeester/DeepLCModels)


## Q&A

**__Q: Is it required to indicate fixed modifications in the input file?__**

Yes, even modifications like carbamidomethyl should be in the input file.

**__Q: So DeepLC is able to predict the retention time for any modification?__**

Yes, DeepLC can predict the retention time of any modification. However, if the
modification is **very** different from the peptides the model has seen during
training the accuracy might not be satisfactory for you. For example, if the model
has never seen a phosphor atom before, the accuracy of the prediction is going to
be low.

**__Q: Installation fails. Why?__**

Please make sure to install DeepLC in a path that does not contain spaces. Run
the latest LTS version of Ubuntu or Windows 10. Make sure you have enough disk
space available, surprisingly TensorFlow needs quite a bit of disk space. If
you are still not able to install DeepLC, please feel free to contact us:

Robbin.Bouwmeester@ugent.be and Ralf.Gabriels@ugent.be

**__Q: I have a special usecase that is not supported. Can you help?__**

Ofcourse, please feel free to contact us:

Robbin.Bouwmeester@ugent.be and Ralf.Gabriels@ugent.be

**__Q: DeepLC runs out of memory. What can I do?__**

You can try to reduce the batch size. DeepLC should be able to run if the batch size is low
enough, even on machines with only 4 GB of RAM.

**__Q: I have a graphics card, but DeepLC is not using the GPU. Why?__**

For now DeepLC defaults to the CPU instead of the GPU. Clearly, because you want
to use the GPU, you are a power user :-). If you want to make the most of that expensive
GPU, you need to change or remove the following line (at the top) in __deeplc.py__:

```
# Set to force CPU calculations
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
```

Also change the same line in the function __reset_keras()__:

```
# Set to force CPU calculations
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
```

Either remove the line or change to (where the number indicates the number of GPUs):

```
# Set to force CPU calculations
os.environ['CUDA_VISIBLE_DEVICES'] = '1'
```

**__Q: What modification name should I use?__**

The names from unimod are used. The PSI-MS name is used by default, but the Interim name
is used as a fall-back if the PSI-MS name is not available. Please also see __unimod_to_formula.csv__
in the folder __unimod/__ for the naming of specific modifications.

**__Q: I have a modification that is not in unimod. How can I add the modification?__**

In the folder __unimod/__ there is the file __unimod_to_formula.csv__ that can be used to
add modifications. In the CSV file add a name (**that is unique and not present yet**) and
the change in atomic composition. For example:

```
Met->Hse,O,H(-2) C(-1) S(-1)
```

Make sure to use negative signs for the atoms subtracted.

**__Q: Help, all my predictions are between [0,10]. Why?__**

It is likely you did not use calibration. No problem, but the retention times for training
purposes were normalized between [0,10]. This means that you probably need to adjust the
retention time yourselve after analysis or use a calibration set as the input.


**__Q: What does the option `dict_divider` do?__**

This parameter defines the precision to use for fast-lookup of retention times
for calibration. A value of 10 means a precision of 0.1 (and 100 a precision of
0.01) between the calibration anchor points. This parameter does not influence
the precision of the calibration, but setting it too high might mean that there
is bad selection of the models between anchor points. A safe value is usually
higher than 10.


**__Q: What does the option `split_cal` do?__**

The option `split_cal`, or split calibration, sets number of divisions of the
chromatogram for piecewise linear calibration. If the value is set to 10 the
chromatogram is split up into 10 equidistant parts. For each part the median
value of the calibration peptides is selected. These are the anchor points.
Between each anchor point a linear fit is made. This option has no effect when
the pyGAM generalized additive models are used for calibration.


**__Q: How does the ensemble part of DeepLC work?__**

Models within the same directory are grouped if they overlap in their name. The overlap
has to be in their full name, except for the last part of the name after a "_"-character.

The following models will be grouped:

```
full_hc_dia_fixed_mods_a.hdf5
full_hc_dia_fixed_mods_b.hdf5
```

None of the following models will not be grouped:

```
full_hc_dia_fixed_mods2_a.hdf5
full_hc_dia_fixed_mods_b.hdf5
full_hc_dia_fixed_mods_2_b.hdf5
```

**__Q: I would like to take the ensemble average of multiple models, even if they are trained on different datasets. How can I do this?__**

Feel free to experiment! Models within the same directory are grouped if they overlap in
their name. The overlap has to be in their full name, except for the last part of the
name after a "_"-character.

The following models will be grouped:

```
model_dataset1.hdf5
model_dataset2.hdf5
```

So you just need to rename your models.
