Metadata-Version: 2.1
Name: pyproteininference
Version: 1.0.0
Summary: Python Package for running custom protein inference algorithms on tab-formatted tandem MS/MS search results.
Home-page: https://github.com/thinkle12/pyproteininference
Author: Trent Hinkle
Author-email: hinklet@gene.com
License: Apache-2
Description: ﻿# Py Protein Inference
        
        # Requirements
        
         1. __Python 3.7__ or greater. This package was created using __Python 3.7__
         2. __Python Packages__:
        	__numpy__, __pyteomics__, __pulp__, __PyYAML__, __matplotlib__. These should be installed automatically during installation.
        		
        # Quick Start Guide
        1. Install the package using pip:
        ```shell
        pip install pyproteininference
        ```
           
        2. Run the Heuristic method with tab delimited results directly from percolator to generate results for peptide centric, parsimony, inclusion, and exclusion:
        ```shell
        protein_inference_heuristic_cli.py \
        -t /path/to/target/file1.txt \
        -d /path/to/decoy/file1.txt \
        -db /path/to/database/file.fasta 
        ```
        
        3. Run the standard commandline tool with tab delimited results directly from percolator to run a particular inference method. By default, peptide centric inference is selected if a parameter file is not specified:
        ```shell
        protein_inference_cli.py \
        -t /path/to/target/file.txt \
        -d /path/to/decoy/file.txt \
        -db /path/to/database/file.fasta 
        ```
        
        4. Specifying Parameters. 
        The two most common parameters to change are the inference type, and the decoy symbol (for identifying decoy proteins vs target proteins).
        The parameters can be quickly altered by creating a file called params.yaml as follows:
        ```yaml
        parameters:
          inference:
            inference_type: parsimony
          identifiers:
            decoy_symbol: "decoy_"
        ```
        The inference type can be one of: `parsimony`, `peptide_centric`, `inclusion`, `exclusion`, or `first_protein`.
        All parameters are optional, so you only need to define the ones you want to alter. Parameters that are not defined are set to default values.
        See the package documentation for the default parameters.
        
        5. Run the standard commandline tool again, this time specifying the parameters as above:
        ```shell
        protein_inference_cli.py \
        -t /path/to/target/file.txt \
        -d /path/to/decoy/file.txt \
        -db /path/to/database/file.fasta \
        -y /path/to/params.yaml
        ```
        
        6. Running with docker
        	- Either Pull the image from docker hub:
        		- `docker pull hinklet/pyproteininference:latest`
        	- Or Build the image with the following command (After having cloned the repository):
        	  	- `git clone REPOSITORY_URL`
        	  	- `cd pyproteininference`
        		- `docker build -t pyproteininference:latest .`
        	- Run the tool, making sure to volume mount in the directory with your input data and parameters. In the case below, that local directory would be `/path/to/local/directory` and the path in the container is `/data`
        	  ```shell
        			docker run -v /path/to/local/directory/:/data \
        			-it hinklet/pyproteininference:latest \
        			python /usr/local/bin/protein_inference_cli.py \
        			-f /data/input_file.txt \
        			-db /data/database.fasta \
        			-y /data/parameters.yaml \
        			-o /data/
        	  ```
        
        # Documentation
        For more information please see the full package documentation (https://thinkle12.github.io/pyproteininference/).
        
        
Keywords: proteininference
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Description-Content-Type: text/markdown
