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DeepLC: Retention time prediction for (modified) peptides using Deep Learning.
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DeepLC: Retention time prediction for peptides carrying any modification.
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@@ -22,21 +22,13 @@ DeepLC: Retention time prediction for (modified) peptides using Deep Learning.
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-[Python module](#python-module)
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-[Input files](#input-files)
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-[Prediction models](#prediction-models)
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-[Q&A](#qa)
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---
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## Introduction
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DeepLC is a retention time predictor for (modified) peptides that employs Deep
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Learning. Its strength lies in the fact that it can accurately predict
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retention times for modified peptides, even if hasn't seen said modification
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during training.
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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.
locally with a graphical user interface (GUI), or as a Python package. In the
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latter case, DeepLC can be used from the command line, or as a Python module.
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DeepLC can be used through the [web application](https://iomics.ugent.be/deeplc/) or as a Python package. In the latter case, DeepLC can be used from the command line, or as a Python module.
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## Citation
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Just go to [iomics.ugent.be/deeplc](https://iomics.ugent.be/deeplc/) and get started!
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### Graphical user interface
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#### In an existing Python environment (cross-platform)
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1. In your terminal with Python (>=3.7) installed, run `pip install deeplc[gui]`
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2. Start the GUI with the command `deeplc-gui` or `python -m deeplc.gui`
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## Q&A
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**__Q: Is it required to indicate fixed modifications in the input file?__**
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Yes, even modifications like carbamidomethyl should be in the input file.
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**__Q: So DeepLC is able to predict the retention time for any modification?__**
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Yes, DeepLC can predict the retention time of any modification. However, if the
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modification is **very** different from the peptides the model has seen during
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training the accuracy might not be satisfactory for you. For example, if the model
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has never seen a phosphor atom before, the accuracy of the prediction is going to
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be low.
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**__Q: Installation fails. Why?__**
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Please make sure to install DeepLC in a path that does not contain spaces. Run
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the latest LTS version of Ubuntu or Windows 10. Make sure you have enough disk
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space available, surprisingly TensorFlow needs quite a bit of disk space. If
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you are still not able to install DeepLC, please feel free to contact us:
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Robbin.Bouwmeester@ugent.be and Ralf.Gabriels@ugent.be
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**__Q: I have a special usecase that is not supported. Can you help?__**
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Ofcourse, please feel free to contact us:
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Robbin.Bouwmeester@ugent.be and Ralf.Gabriels@ugent.be
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**__Q: DeepLC runs out of memory. What can I do?__**
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You can try to reduce the batch size. DeepLC should be able to run if the batch size is low
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enough, even on machines with only 4 GB of RAM.
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**__Q: I have a graphics card, but DeepLC is not using the GPU. Why?__**
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For now DeepLC defaults to the CPU instead of the GPU. Clearly, because you want
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to use the GPU, you are a power user :-). If you want to make the most of that expensive
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GPU, you need to change or remove the following line (at the top) in __deeplc.py__:
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```
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# Set to force CPU calculations
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os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
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```
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Also change the same line in the function __reset_keras()__:
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```
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# Set to force CPU calculations
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os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
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```
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Either remove the line or change to (where the number indicates the number of GPUs):
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```
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# Set to force CPU calculations
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os.environ['CUDA_VISIBLE_DEVICES'] = '1'
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```
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**__Q: What modification name should I use?__**
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The names from unimod are used. The PSI-MS name is used by default, but the Interim name
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is used as a fall-back if the PSI-MS name is not available. It should be fine as long as it is support by [proforma](https://pubs.acs.org/doi/10.1021/acs.jproteome.1c00771) and [psm_utils](https://github.com/compomics/psm_utils).
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**__Q: I have a modification that is not in unimod. How can I add the modification?__**
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Unfortunately since the V3.0 this is not possible any more via the GUI or commandline. You will need to use [psm_utils](https://github.com/compomics/psm_utils), above a minimal example is shown where we convert an identification file into a psm_list which is accepted by DeepLC. Here the sequence can for example include just the composition in proforma format (e.g., SEQUEN[Formula:C12H20O2]CE).
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**__Q: Help, all my predictions are between [0,10]. Why?__**
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It is likely you did not use calibration. No problem, but the retention times for training
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purposes were normalized between [0,10]. This means that you probably need to adjust the
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retention time yourselve after analysis or use a calibration set as the input.
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**__Q: What does the option `dict_divider` do?__**
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This parameter defines the precision to use for fast-lookup of retention times
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for calibration. A value of 10 means a precision of 0.1 (and 100 a precision of
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0.01) between the calibration anchor points. This parameter does not influence
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the precision of the calibration, but setting it too high might mean that there
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is bad selection of the models between anchor points. A safe value is usually
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higher than 10.
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**__Q: What does the option `split_cal` do?__**
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The option `split_cal`, or split calibration, sets number of divisions of the
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chromatogram for piecewise linear calibration. If the value is set to 10 the
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chromatogram is split up into 10 equidistant parts. For each part the median
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value of the calibration peptides is selected. These are the anchor points.
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Between each anchor point a linear fit is made. This option has no effect when
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the pyGAM generalized additive models are used for calibration.
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**__Q: How does the ensemble part of DeepLC work?__**
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Models within the same directory are grouped if they overlap in their name. The overlap
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has to be in their full name, except for the last part of the name after a "_"-character.
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The following models will be grouped:
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```
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full_hc_dia_fixed_mods_a.hdf5
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full_hc_dia_fixed_mods_b.hdf5
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```
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None of the following models will not be grouped:
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```
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full_hc_dia_fixed_mods2_a.hdf5
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full_hc_dia_fixed_mods_b.hdf5
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full_hc_dia_fixed_mods_2_b.hdf5
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```
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**__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?__**
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Feel free to experiment! Models within the same directory are grouped if they overlap in
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their name. The overlap has to be in their full name, except for the last part of the
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name after a "_"-character.
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The following models will be grouped:
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```
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model_dataset1.hdf5
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model_dataset2.hdf5
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```
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So you just need to rename your models.
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See the [FAQ](https://deeplc.readthedocs.io/en/latest/faq.html) in the documentation.
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