{
    "info": {
        "author": "Colby Chiang (colbychiang@wustl.edu)",
        "author_email": "colbychiang@wustl.edu",
        "bugtrack_url": null,
        "classifiers": [
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 2.7",
            "Topic :: Scientific/Engineering :: Bio-Informatics"
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        "description": "SVTyper\n=======\n[![GitHub license](https://img.shields.io/badge/license-MIT-blue.svg)](https://raw.githubusercontent.com/hall-lab/svtyper/master/LICENSE)\n[![Build Status](https://travis-ci.org/hall-lab/svtyper.svg?branch=master)](https://travis-ci.org/hall-lab/svtyper)\n\nBayesian genotyper for structural variants\n\n## Overview\n\nSVTyper performs breakpoint genotyping of structural variants (SVs) using whole genome sequencing data. Users must supply a VCF file of sites to genotype (which may be generated by [LUMPY](https://github.com/arq5x/lumpy-sv)) as well as a BAM/CRAM file of Illumina paired-end reads aligned with [BWA-MEM](https://github.com/lh3/bwa). SVTyper assesses discordant and concordant reads from paired-end and split-read alignments to infer genotypes at each site. Algorithm details and benchmarking are described in [Chiang et al., 2015](http://www.nature.com/nmeth/journal/vaop/ncurrent/full/nmeth.3505.html).\n\n![NA12878 heterozygous deletion](etc/het.png?raw=true \"NA12878 heterozygous deletion\")\n\n## Installation\n\nRequirements:\n- Python 2.7.x\n\n### Install via `pip`\n\n    pip install git+https://github.com/hall-lab/svtyper.git\n\n`svtyper` depends on [pysam][0] _(version 0.15.0 or newer)_, [numpy][1], and [scipy][2]; `svtyper-sso` additionally depends on [cytoolz][7]. If the dependencies aren't already available on your system, `pip` will attempt to download and install them.\n\n## `svtyper` vs `svtyper-sso`\n\n`svtyper` is the original implementation of the genotyping algorithm, and works with multiple samples.  `svtyper-sso` is an alternative implementation of `svtyper` that is optimized for genotyping a single sample.  `svtyper-sso` is a parallelized implementation of `svtyper` that takes advantage of multiple CPU cores via the [multiprocessing][8] module. `svtyper-sso` can offer a 2x or more speedup (depending on how many CPU cores used) in genotyping a single sample. **_NOTE: svtyper-sso is not yet stable. There are minor logging differences between the two and svtyper-sso may exit with an error prematurely when processing CRAM files._**\n\n## Example Usage\n\n### `svtyper`\n\n#### As a Command Line Python Script\n\n```bash\nsvtyper \\\n    -i sv.vcf \\\n    -B sample.bam \\\n    -l sample.bam.json \\\n    > sv.gt.vcf\n```\n\n#### As a Python Library\n\n```python\nimport svtyper.classic as svt\n\ninput_vcf = \"/path/to/input.vcf\"\ninput_bam = \"/path/to/input.bam\"\nlibrary_info = \"/path/to/library_info.json\"\noutput_vcf = \"/path/to/output.vcf\"\n\nwith open(input_vcf, \"r\") as inf, open(output_vcf, \"w\") as outf:\n    svt.sv_genotype(bam_string=input_bam,\n                    vcf_in=inf,\n                    vcf_out=outf,\n                    min_aligned=20,\n                    split_weight=1,\n                    disc_weight=1,\n                    num_samp=1000000,\n                    lib_info_path=library_info,\n                    debug=False,\n                    alignment_outpath=None,\n                    ref_fasta=None,\n                    sum_quals=False,\n                    max_reads=None)\n\n# Results will be inside the /path/to/output.vcf file\n```\n\n### `svtyper-sso`\n\n#### As a Command Line Python Script\n\n```bash\nsvtyper-sso \\\n    --core 2 # number of cpu cores to use \\\n    --batch_size 1000 # number of SVs to process in a single batch (default: 1000) \\\n    --max_reads 1000 # skip genotyping if SV contains valid reads greater than this threshold (default: 1000) \\\n    -i sv.vcf \\\n    -B sample.bam \\\n    -l sample.bam.json \\\n    > sv.gt.vcf\n```\n\n#### As a Python Library\n\n```python\nimport svtyper.singlesample as sso\n\ninput_vcf = \"/path/to/input.vcf\"\ninput_bam = \"/path/to/input.bam\"\nlibrary_info = \"/path/to/library_info.json\"\noutput_vcf = \"/path/to/output.vcf\"\n\nwith open(input_vcf, \"r\") as inf, open(output_vcf, \"w\") as outf:\n    sso.sso_genotype(bam_string=input_bam,\n                     vcf_in=inf,\n                     vcf_out=outf,\n                     min_aligned=20,\n                     split_weight=1,\n                     disc_weight=1,\n                     num_samp=1000000,\n                     lib_info_path=library_info,\n                     debug=False,\n                     alignment_outpath=None,\n                     ref_fasta=None,\n                     sum_quals=False,\n                     max_reads=1000,\n                     cores=2,\n                     batch_size=1000)\n\n# Results will be inside the /path/to/output.vcf file\n```\n\n## Development\n\nRequirements:\n- Python 2.7 or newer\n- GNU Make\n- [virtualenv][3] _(or [conda][4] for [anaconda][5] or [miniconda][6] users)_\n\n### Setting Up a Development Environment\n\n#### Using `virtualenv`\n\n    git clone https://github.com/hall-lab/svtyper.git\n    cd svtyper\n    virtualenv myvenv\n    source myvenv/bin/activate\n    pip install -e .\n    <add, edit, or delete code>\n    make test\n\n    # when you're finished with development\n    git push <remote-name> <branch>\n    deactivate\n    cd .. && rm -rf svtyper\n\n#### Using `conda`\n\n    git clone https://github.com/hall-lab/svtyper.git\n    cd svtyper\n    conda create --channel bioconda --name mycenv pysam numpy scipy cytoolz # type 'y' when prompted with \"proceed ([y]/n)?\"\n    source activate mycenv\n    pip install -e .\n    <add, edit, or delete code>\n    make test\n\n\n    # when you're finished with development\n    git push <remote-name> <branch>\n    source deactivate\n    cd .. && rm -rf svtyper\n    conda remove --name mycenv --all\n\n## Troubleshooting\n\nMany common issues are related to abnormal insert size distributions in the BAM file. SVTyper provides methods to assess and visualize the characteristics of sequencing libraries.\n\nRunning SVTyper with the `-l` flag creates a JSON file with essential metrics on a BAM file. SVTyper will sample the first N reads for the file (1 million by default) to parse the libraries, read groups, and insert size histograms. This can be done in the absence of a VCF file.\n```\nsvtyper \\\n    -B my.bam \\\n    -l my.bam.json\n```\n\nThe [lib_stats.R](scripts/lib_stats.R) script produces insert size histograms from the JSON file\n```\nscripts/lib_stats.R my.bam.json my.bam.json.pdf\n```\n![Insert size histogram](etc/my.bam.json.png?raw=true \"Insert size histogram\")\n\n\n## Citation\n\nC Chiang, R M Layer, G G Faust, M R Lindberg, D B Rose, E P Garrison, G T Marth, A R Quinlan, and I M Hall. SpeedSeq: ultra-fast personal genome analysis and interpretation. Nat Meth 12, 966\u2013968 (2015). doi:10.1038/nmeth.3505.\n\nhttp://www.nature.com/nmeth/journal/vaop/ncurrent/full/nmeth.3505.html\n\n[0]: https://github.com/pysam-developers/pysam\n[1]: http://www.numpy.org/\n[2]: https://www.scipy.org/\n[3]: https://github.com/pypa/virtualenv\n[4]: https://conda.io/docs/index.html\n[5]: https://docs.continuum.io/anaconda/\n[6]: https://conda.io/miniconda.html\n[7]: https://github.com/pytoolz/cytoolz\n[8]: https://docs.python.org/2/library/multiprocessing.html\n\n\n",
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