Metadata-Version: 2.1
Name: Dellingr
Version: 0.9.5
Summary: Error supression and variant calling pipeline for Illumina sequencing data
Home-page: https://github.com/morinlab/Dellingr
Author: Christopher Rushton
Author-email: ckrushto@sfu.ca
License: UNKNOWN
Download-URL: https://github.com/morinlab/Dellingr/dist/Dellingr-0.9.5.tar.gz
Project-URL: Source, https://github.com/morinlab/Dellingr
Project-URL: Documentation, https://dellingr.readthedocs.io/en/latest/
Description: [![Build Status](https://travis-ci.org/morinlab/Dellingr.svg?branch=master)](https://travis-ci.org/morinlab/Dellingr)
        
        # Dellingr
        An error supression and variant calling pipeline for Second-Generation sequencing data
        
        ## Description
        
        See the full wiki page for more information: http://produse.readthedocs.io/en/latest/
        
        ## Installation 
        
        ### Dependencies
        
        You will need to install the following before installing Dellingr:
        
        * `python>=3.5`
        * `bwa>=0.7.0`
        * `samtools>=1.3.1`
        
        Dellingr will check these dependencies prior to running the pipeline
        
        To install the Dellingr package run the following command:
        
        ### Install using the Python Package Index (PyPI)
        ```bash
        pip install Dellingr
        ```
        
        ### Install from Github
        ```bash
        git clone https://github.com/morinlab/Dellingr.git
        cd Dellingr
        python setup.py install
        ```
        All required python dependencies will be installed during this step
        
        ## Running Dellingr
        
        ### The Analysis Pipeline: Very Quick Start
        
        You can view more detailed instructions on the [wiki](http://produse.readthedocs.io/en/latest/)
        
        All parameters required to run ProDuSe can be viewed using the following:
        ```bash
            dellingr run_dellingr -h
        ```
        
        Alternatively, if you wish to run Dellingr without installing it, you can run `DellingrPipeline.py` manually in a similar manner:
        ```bash
            /path/to/Dellingr/DellingrPipeline.py -h
        ```
        
        While these parameters can be specified individually, they can also be provided using a configuration file
        
        To run the analysis pipeline you simply need to run the following command:
        ```bash
            dellingr run_dellingr
            -c /path/to/github/clone/etc/dellingr_config.ini
        ```
        
        Alternatively:
        ```bash
            /path/to/Dellingr/DellingrPipeline.py 
            -c /path/to/github/clone/etc/dellingr_config.ini
        ```
        
        This will run the entire Dellingr pipeline on all samples specified in the sample_config.ini file, which can be found in 
        etc/sample_config.ini
        
        Results will be located in the following directory:
        
        ```bash
        ls ./dellingr_analysis_directory
        ```
        
        ### Helper Scripts
        
        The Dellingr package is comprised of several stages to aid in the analysis of duplex sequencing data.
        
        These stages can be be viewed by running the following:
        
        ```bash
        dellingr -h
        ```
        
        #### dellingr adapter_predict
        
        If you need to confirm the expected adapter sequence of a sample you should run the following command:
        
        ```bash
        dellingr adapter_predict -i input1.fastq input2.fastq
        ```
        
        This tool will print a predicted adapter sequence based off of ACGT abundances at each position. It uses these observed abundances and finds the closest expected abundance for an IUPAC unambiguous or ambiguous base.
        
        #### External links
        [The Morin Laboratory at SFU](https://morinlab.github.io/team/)
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: Unix
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: License :: OSI Approved :: GNU Affero General Public License v3
Requires-Python: >=3.5
Description-Content-Type: text/markdown
