Metadata-Version: 1.1
Name: prodmx
Version: 0.1.0
Summary: Protein Functional Domain Analysis based on Compressed Sparse Matrix
Home-page: https://github.com/visanuwan/prodmx
Author: Visanu Wanchai
Author-email: visanuw86@gmail.com
License: MIT
Description: ## ProdMX : Protein Functional Domain based on Compressed Sparse Matrices
        
        ProdMX is a tool with user-friendly utilities developed to facilitate high-throughput analysis of protein functional domains and domain architectures. The ProdMX employs a compressed sparse matrix algorithm to reduce computational resources and time used to perform the matrix manipulation during functional domain analysis.
        
        ### Dependencies
        
        * Python 3.5 or newer and the following packages:
            * [pandas](https://github.com/pandas-dev/pandas)
            * [h5py](https://github.com/h5py/h5py)
            * [numpy](https://github.com/numpy/numpy)
            * [tqdm](https://github.com/tqdm/tqdm)
            * [scipy](https://github.com/scipy/scipy)
        
        ### Installation from source
        
        ```
        git clone https://github.com/visanuwan/prodmx
        python -m pip install prodmx
        ```
        ### Usage
        
        Generally, the use of the ProdMX tool starts with constructing the compressed sparse matrix of either protein functional domains or domain architectures in a command-line environment. The input of ProdMX is a tab-delimited file containing two columns of genome labels and the path to their HMMER results.
        
        **Protein functional domain**
        
        ```
        prodmx-buildDomain [-h] [-v] [-i INPUT] [-o OUTPUT] [-k]
        ```
        
        **Domain architecture**
        
        ```
        prodmx-buildArchitecture [-h] [-v] [-i INPUT] [-o OUTPUT] [-k]
        ```
        For the detail of commands and examples, see the example of analyses using ProdMX in [Jupyter Notebook.](test/prodmx_example.ipynb)
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
