A simple python library to identify the most likely strain given the SNPs for a sample
SNPmatch 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.
## Installation & Usage
The below steps deal with running SNPmatch on a local machine. This package is only tested in Python 2.
### Installation using pip
SNPmatch 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.
`bash ## installing SNPmatch from git hub repository pip install git+https://github.com/Gregor-Mendel-Institute/SNPmatch.git ## or PyPi pip install SNPmatch ` SNPmatch can be installed either from the git repo or through PyPi.
### Database files
Database 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.
The 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.
`bash snpmatch makedb -i input_database.vcf -o db `
- The above command generates three files,
The 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.
For 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).
### Input file
As 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.
` 1 125 0/0 1 284 0/0 1 336 0/0 1 346 1/1 1 353 0/0 1 363 0/0 1 465 0/0 1 471 0/1 1 540 0/0 1 564 0/0 1 597 0/0 1 612 1/1 1 617 0/1 ` VCF 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.
SNPmatch can be run as bash commands given below. A detailed manual for each command with -h.
`bash snpmatch inbred -i input_file -d db.hdf5 -e db.acc.hdf5 -o output_file # or snpmatch parser -i input_file -o input_npz snpmatch inbred -i input_npz -d db.hdf5 -e db.acc.hdf5 -o output_file `
SNPmatch can be run directly for A. thaliana researchers as a web tool, [AraGeno](http://arageno.gmi.oeaw.ac.at)
## Genotyping a hybrid
SNPmatch 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
`bash snpmatch cross -d db.hdf5 -e db.acc.hdf5 -i input_file -b window_size_in_bp -o output_file #to get a genetic map for the hybrid snpmatch genotype_cross -e db.acc.hdf5 -p parent1xparent2 -i input_file -o output_file # or if parents have VCF files individually snpmatch genotype_cross -p parent1.vcf -q parent2.vcf -i input_file -o output_file `
These 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.
## Contributing 1. Fork it! 2. Create your feature branch: git checkout -b my-new-feature 3. Commit your changes: git commit -am ‘Add some feature’ 4. Push to the branch: git push origin my-new-feature 5. Submit a pull request :D
- 1.7.2: Stable version, 15-12-2016
- 1.8.2: Stable version, 16-02-2017
- 1.9.2: Stable version, 24-08-2017
- Rahul Pisupati (rahul.pisupati[at]gmi.oeaw.ac.at)
- Ümit Seren (uemit.seren[at]gmi.oeaw.ac.at)
Pisupati et. al. Verification of Arabidopsis stock collection using SNPmatch - an algorithm to genotype high-plexed samples, manuscript under preparation.