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        "author": "['Rahul Pisupati']",
        "author_email": "rahul.pisupati@gmi.oeaw.ac.at",
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        "classifiers": [
            "Development Status :: 5 - Production/Stable",
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            "Programming Language :: Python :: 3.4",
            "Topic :: Scientific/Engineering :: Bio-Informatics"
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        "description": "# SNPmatch\n\nSNPmatch is a Python toolkit which can be used to genotype a sample from as-low-as as 4000 markers from the database lines. SNPmatch can genotype samples efficiently and economically using a simple likelihood approach.\n\n## Installation & Usage\n\nThe below steps deal with running SNPmatch on a local machine. This package is only tested in Python 2.\n\n### Installation using pip\n\nSNPmatch can be easily installed with the help of pip. SNPmatch uses various python packages (NumPy, pandas, [PyGWAS](https://github.com/timeu/PyGWAS), [scikit-allel](https://github.com/cggh/scikit-allel)), which are automatically downloaded and installed while using pip. Follow the commands below for successful installation.\n\n```bash\n## installing SNPmatch from git hub repository\npip install git+https://github.com/Gregor-Mendel-Institute/SNPmatch.git\n## or PyPi\npip install SNPmatch\n```\nSNPmatch can be installed either from the git repo or through PyPi. In case of installation errors, please install these dependencies using the commands below (for a Debian based system).\n```bash\nsudo apt-get install python-dev libfreetype6-dev libxft-dev libblas-dev liblapack-dev libatlas-base-dev libhdf5-dev gfortran\nsudo pip install NumPy\n```\nMac users can install these packages using [Homebrew](https://brew.sh/). These packages should be enough to install SNPmatch correctly. Please raise an issue in the Github repo if you still have trouble installing.\n\n### Database files\n\nDatabase files containing the known genotype information for many strains have to be provided as HDF5 formatted file. These can be generated with given markers or variants present in a VCF file. The database files can be generated with the functions given in SNPmatch. They are generated using the commands given below.\n\nThe below commands require BCFtools executable in the path environment. The database files are read using PyGWAS package. So the VCF files need to have biallelic SNPs only for now.\n\n```bash\nsnpmatch makedb -i input_database.vcf -o db\n```\n\nThe above command generates three files,\n  * db.csv\n  * db.hdf5\n  * db.acc.hdf5\n\nThe two hdf5 files are the main database files used for further analysis. The files have the same information but are chunked for better efficiency. The files db.hdf5 and db.acc.hdf5 are given to the SNPmatch command under -d and -e options respectively.\n\nFor *Arabidopsis thaliana* users, we have made SNP database files for the `RegMap` and `1001Genomes` panel available and can be downloaded [here](https://gmioncloud-my.sharepoint.com/personal/uemit_seren_gmi_oeaw_ac_at/_layouts/15/guestaccess.aspx?folderid=0ca806e676c154094992a9e89e5341d43&authkey=AXJPl6GkD8vNPDZJwheb6uk).\n\n### Input file\n\nAs the input file, SNPmatch takes genotype information in two file formats (BED and VCF). Example input files are given in the folder [sample_files](https://github.com/Gregor-Mendel-Institute/SNPmatch/tree/master/sample_files). Briefly, BED files should be three tab-separated column with chromosome, position and genotype shown below.\n\n```\n1 125 0/0\n1 284 0/0\n1 336 0/0\n1 346 1/1\n1 353 0/0\n1 363 0/0\n1 465 0/0\n1 471 0/1\n1 540 0/0\n1 564 0/0\n1 597 0/0\n1 612 1/1\n1 617 0/1\n```\nVCF file in a default format in the [link](http://gatkforums.broadinstitute.org/gatk/discussion/1268/what-is-a-vcf-and-how-should-i-interpret-it). The main arguments required for SNPmatch are CHROM and POS in header and GT in the INFO column. PL (Normalized Phred-scaled likelihoods of the possible genotypes), if present improves the efficiency of SNPmatch.\n\n### Usage\n\nSNPmatch can be run as bash commands given below. A detailed manual for each command with -h.\n\n```bash\nsnpmatch inbred -i input_file -d db.hdf5 -e db.acc.hdf5 -o output_file\n# or\nsnpmatch parser -i input_file -o input_npz\nsnpmatch inbred -i input_npz -d db.hdf5 -e db.acc.hdf5 -o output_file\n```\n\n### AraGeno\n\nSNPmatch can be run directly for *A. thaliana* researchers as a web tool, [AraGeno](http://arageno.gmi.oeaw.ac.at)\n\n## Genotyping a hybrid\n\nSNPmatch can be used to identify hybrid individuals when parental strains are present in database. For such individuals, SNPmatch can be run in windows across the genome. The commands used to run are given below\n\n```bash\nsnpmatch cross -d db.hdf5 -e db.acc.hdf5 -i input_file -b window_size_in_bp -o output_file\n#to get a genetic map for the hybrid\nsnpmatch genotype_cross -e db.acc.hdf5 -p parent1xparent2 -i input_file -o output_file\n# or if parents have VCF files individually\nsnpmatch genotype_cross -p parent1.vcf -q parent2.vcf -i input_file -o output_file\n```\n\nThese scripts are implemented based on the *A. thaliana* genome sizes. But the global variable in csmatch [script](https://github.com/Gregor-Mendel-Institute/SNPmatch/blob/master/snpmatch/core/csmatch.py#L19) can be modified to the corresponding genome sizes.\n\n\n## Contributing\n1. Fork it!\n2. Create your feature branch: `git checkout -b my-new-feature`\n3. Commit your changes: `git commit -am 'Add some feature'`\n4. Push to the branch: `git push origin my-new-feature`\n5. Submit a pull request :D\n\n## History\n\n- 1.9.2: Stable version, 24-08-2017\n- 2.0.0: Stable version, 26-01-2018\n- 2.1.0: Stable version, 09-08-2018\n\n\n## Credits\n\n- Rahul Pisupati (rahul.pisupati[at]gmi.oeaw.ac.at)\n- \u00dcmit Seren (uemit.seren[at]gmi.oeaw.ac.at)\n\n## Citation\n\nPisupati, R. *et al.*. Verification of *Arabidopsis* stock collections using SNPmatch, a tool for genotyping high-plexed samples.  *Nature Scientific Data*  **4**, 170184 (2017).\n[doi:10.1038/sdata.2017.184](https://www.nature.com/articles/sdata2017184)\n\n\n",
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