Metadata-Version: 2.1
Name: iScanVCFMerge
Version: 0.1
Summary: Python tool to merge cross-species Illumina iScan genotype data with a reference set of data from a pre-existing source.
Home-page: https://github.com/baneslab/iScanVCFMerge
Author: Banes, G. L., Meyers, J. and Fountain, E. D.
Author-email: banes@wisc.edu
License: MIT
Project-URL: Bug Tracker, https://github.com/baneslab/iScanVCFMerge/issues
Description: # iScanVCFMerge
                
        ## Installation
        
        iScanVCFMerge has been tested with Python 3.9.2 on MacOS Big Sur 11.3 and on Ubuntu 21.04. 
        
        ### Option 1: Github clone and run with Python3
        
            git clone "https://github.com/baneslab/iScanVCFMerge.git"
            $ cd iScanVCFMerge
            $ python3 iScanVCFMerge.py
        
        If running the script directly with Python, you may also need to install the required packages, _e.g._:
        
            python3 -m pip install pandas
            
        ### Run Option 2: Install with pip
        
            pip install iScanVCFMerge
        
        or
        
            python3 -m pip install iScanVCFMerge
        
        ## Usage
        
            iScanVCFMerge [-h] -R <reference_vcf> -I <iScan_vcf> -O <output_directory>
        
        Optional arguments:
        
            -h, --help                  Show the help message
            -R, --reference_vcf         Reference VCF file against which iScan file will be merged (.vcf or .vcf.gz)
            -I, --iScan_vcf             iScan VCF file  (.vcf or .vcf.gz)
            -O, --output_directory      Name of the output directory (will be created if it doesn't exist)
        
        ## Citation
        
        Please cite the use of this software as follows:
        
        > Fountain, E. D., Zhou, L-C., Karklus, A., Liu, Q-X., Meyers, J., Fontanilla, I. K., Rafael, E. F., Yu, J-Y., Zhang, Q., Zhu, X-L., Pei, E-L., Yuan, Y-H. and Banes, G. L. (2021). Cross-species application of Illumina iScan microarrays for cost-effective, high-throughput SNP discovery. Frontiers in Ecology and Evolution, doi: 10.3389/fevo.2021.629252.
Platform: any
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3.9
Requires-Python: >=3.9
Description-Content-Type: text/markdown
