Metadata-Version: 2.1
Name: motevowrapper
Version: 0.0.3
Summary: Simple Python MotEvo wrapper.
Home-page: https://github.com/brlauuu/motevowrapper
Author: Đorđe Relić
Author-email: dorde.relic@protonmail.com
License: MIT
Download-URL: https://github.com/brlauuu/motevowrapper/archive/v0.0.3.tar.gz
Description: ![Tests](https://github.com/brlauuu/motevowrapper/workflows/Tests/badge.svg) ![Upload to PyPi](https://github.com/brlauuu/motevowrapper/workflows/Upload%20to%20PyPi/badge.svg)
        
        # motevowrapper
        
        Simple Python parser for MotEvo files.
        
        To install, run:
        
        ```bash
        pip install motevowrapper
        ```
        
        ## MotEvo
        
        [MotEvo](https://pubmed.ncbi.nlm.nih.gov/22334039/) (Arnold et al. 2012) is a Bayesian probabilistic model for prediction of transcription factor binding sites (TFBSs) for a given set of position weight matrices (PWMs) and DNA sequences. It was developed by van Nimwegen lab at the Biozentrum (University of Basel, Switzerland) and it can be acquired [here](https://swissregulon.unibas.ch/sr/software).
        
        This repository contains the source code for a simple Python package that allows you to:
        
        1. Run MotEvo with given parameters
        2. Parse MotEvo output files
        3. Visualize visualize site density per motif
        
        ## Running MotEvo from MotevoWrapper
        
        TODO
        
        ## Parsing MotEvo files from MotevoWrapper
        
        MotEvo produces 2 files: `sites` and `priors` file. Usage of the package is simple. For a given MotEvo sites file stored at `/path/to/sites_MOTIF.wm` by calling:
        
        ```python
        import motevowrapper as mw
        
        df_sites = mw.parse_sites('/path/to/sites_MOTIF.wm') # Motif binding sites
        df_priors = mw.parse_sites('/path/to/priors_MOTIF.wm') # Final file with priors
        
        ```
        
        you get a Pandas data frame containing parsed data from the MotEvo run. Further manipulation with the dataframe allows getting motif binding density on all sequences, number of binding sites, number of different species from alignment used, etc.
        
        ## Visualizing site density per motif using MotevoWrapper
        
        TODO
        
        ## References
        
        1. Arnold, Phil, et al. "MotEvo: integrated Bayesian probabilistic methods for inferring regulatory sites and motifs on multiple alignments of DNA sequences." Bioinformatics 28.4 (2012): 487-494.
        
Keywords: MotEvo,wrapper,binding,sites,tfbs-discovery
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Description-Content-Type: text/markdown
