Metadata-Version: 2.1
Name: deepcoil
Version: 2.0
Summary: Fast and accurate prediction of coiled coil domains in protein sequences
Home-page: https://github.com/labstructbioinf/deepcoil
Author: Jan Ludwiczak
Author-email: j.ludwiczak@cent.uw.edu.pl
License: UNKNOWN
Description: # **DeepCoil** #
        [![DOI:10.1093/bioinformatics/bty1062](https://zenodo.org/badge/DOI/10.1093/bioinformatics/bty1062.svg)](https://doi.org/10.1093/bioinformatics/bty1062 )
        ![build](https://github.com/labstructbioinf/DeepCoil/workflows/deepcoil/badge.svg) 
        
        ## **Fast and accurate prediction of coiled coil domains in protein sequences**
        ### **New in version 2.0** ###
        - Retrained with the updated dataset based on *[SamCC-Turbo](https://github.com/labstructbioinf/samcc_turbo)* labels.
        - Faster inference time by applying *[SeqVec](https://github.com/rostlab/SeqVec)* embeddings instead of *psiblast* profiles.
        - Heptad register prediction (*a* and *d* core positions).
        - No maximum sequence length limit.
        - Convenient interface for using *DeepCoil* within python scripts.
        - Automated peak detection for improved output readability.
        - Simplified installation with *pip*.
        
        **Older DeepCoil versions are available [here](https://github.com/labstructbioinf/DeepCoil/releases).**
        
        ### **Requirements and installation** ###
        DeepCoil requires `python>=3.6.1` and `pip>=19.0`. Other requirements are specified in the `requirements.txt` file.
        
        The most convenient way to install **DeepCoil** is to use pip:
        ```bash
        $ pip3 install deepcoil
        ```
        
        ### **Usage** ###
        
        #### Running DeepCoil standalone version:
        
        ```bash
        deepcoil [-h] -i FILE [-out_path DIR] [-n_cpu NCPU] [--gpu] [--plot]
                        [--dpi DPI]
        ```
        | Argument        | Description |
        |:-------------:|-------------|
        | **`-i`** | Input file in FASTA format. Can contain multiple entries. |
        | **`-out_path`** | Directory where the predictions are saved. For each entry in the input file one file will be saved. Defaults to the current directory if not specified.|
        | **`-n_cpu`** | Number of CPUs to use in the prediction. By the default all cores will be used.|
        | **`--gpu`** | Flag for turning on the GPU usage. Allows faster inference on large datasets. Overrides **`-n_cpu`** option.|
        | **`--plot`** | Turns on the additional visual output of the predictions for each entry in the input. Plot files are saved in the **`-out_path`** directory.|
        | **`--dpi`** | DPI of the saved plots, active only with **`--plot`** option.|
        
        In a rare case of `deepcoil` being not available in your `PATH` after installation please look in the `$HOME/.local/bin/` or other system specific `pip` directory.
        
        Description of columns in output file:
        - **`aa`** - amino acid in the input protein sequence
        - **`cc`** - sharpened coiled coil propensity
        - **`raw_cc`** - raw coiled coil propensity
        - **`prob_a`** - probability of *a* core position
        - **`prob_d`** - probability of *d* core position
        
        #### Running DeepCoil within script:
        
        ```python
        from deepcoil import DeepCoil
        from deepcoil.utils import plot_preds
        from Bio import SeqIO
        
        dc = DeepCoil(use_gpu=True)
        
        inp = {str(entry.id): str(entry.seq) for entry in SeqIO.parse('example/example.fas', 'fasta')}
        
        results = dc.predict(inp)
        
        plot_preds(results['3WPA_1'], out_file='example/example.png')
        ```
        `results[entry]`  for an entry of sequence length `N` contains two keys:
        - `['cc']` - per residue coiled coil propensity (`[N, 1]` shape)
        - `['hept']` - per residue core positions (`[N, 3]` shape, order in the second axis is: no/other position, *a* position, *d* position)
        
        Peak detection can be performed with the `deepcoil.utils.sharpen_preds` helper function.
        #### Example graphical output:
        ![Example](example/example.png)
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Requires-Python: >=3.6.1
Description-Content-Type: text/markdown
