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        "author": "David Heller",
        "author_email": "heller_d@molgen.mpg.de",
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        "classifiers": [
            "Development Status :: 5 - Production/Stable",
            "Environment :: Console",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
            "Programming Language :: Python :: 3.6",
            "Topic :: Scientific/Engineering :: Bio-Informatics"
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        "description": "SVIM - Structural variant identification using long reads\n=========================================================\n\n.. image:: https://badge.fury.io/py/svim.svg\n    :target: https://badge.fury.io/py/svim\n\n.. image:: https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg\n    :target: http://bioconda.github.io\n\nSVIM (pronounced *SWIM*) is a structural variant caller for long reads.\nIt is able to detect, classify and genotype five different classes of structural variants.\nUnlike existing methods, SVIM integrates information from across the genome to precisely distinguish similar events, such as tandem and interspersed duplications and novel element insertions.\nIn our experiments on simulated data and real datasets from PacBio and Nanopore sequencing machines, SVIM reached consistently better results than competing methods.\nFurthermore, it is unique in its capability of extracting both the genomic origin and destination of duplications.\n\nBackground on Structural Variants and Long Reads\n------------------------------------------------\n\n.. image:: https://raw.githubusercontent.com/eldariont/svim/master/docs/SVclasses.png\n    :align: center\n\nStructural variants (SVs) are typically defined as genomic variants larger than 50bps (e.g. deletions, duplications, inversions).\nStudies have shown that they affect more bases in any given genome than SNPs and small Indels taken together.\nConsequently, they have a large impact on genes and regulatory regions.\nThis is reflected in the large number of genetic diseases that are caused by SVs.\n\nCommon sequencing technologies by providers such as Illumina generate short reads with high accuracy.\nHowever, they exhibit weaknesses in repeat and low-complexity regions.\nThis negatively affects SV detection because SVs are associated to such regions.\nSingle molecule long-read sequencing technologies from Pacific Biotechnologies and Oxford Nanopore produce reads with error rates of up to 15% but with lengths of several kb.\nThe high read lengths enable them to cover entire repeats and SVs which facilitates SV detection.\n\nInstallation\n------------\n\n.. code-block:: bash\n\n    #Install via conda: easiest option, installs all dependencies including read alignment dependencies\n    conda install --channel bioconda svim\n\n    #Install via pip (requires Python 3.6.*): installs all dependencies except those necessary for read alignment (ngmlr, minimap2, samtools)\n    pip3 install svim\n\n    #Install from github (requires Python 3.6.*): installs all dependencies except those necessary for read alignment (ngmlr, minimap2, samtools)\n    git clone https://github.com/eldariont/svim.git\n    cd svim\n    pip3 install .\n\nChangelog\n---------\n- **v1.2.0**: add 3 more VCF output options: output sequence instead of symbolic alleles in VCF, output names of supporting reads, output insertion sequences of supporting reads\n- **v1.1.0**: outputs BNDs in VCF, detects large tandem duplications, allows skipping genotyping, makes VCF output more flexible, adds genotype scatter plot\n- **v1.0.0**: adds genotyping of deletions, inversions, insertions and interspersed duplications, produces plots of SV length distribution, improves help descriptions\n- **v0.5.0**: replaces graph-based clustering with hierarchical clustering, modifies scoring function, improves partitioning prior to clustering, improves calling from coordinate-sorted SAM/BAM files, improves VCF output\n- **v0.4.4**: includes exception message into log files, bug fixes, adds tests and sets up Travis\n- **v0.4.3**: adds support for coordinate-sorted SAM/BAM files, improves VCF output and increases compatibility with IGV and truvari, bug fixes\n\nInput\n-----\n\nSVIM analyzes long reads given as a FASTA/FASTQ file (uncompressed or gzipped) or a file list.\nAlternatively, it can analyze an alignment file in BAM format.\nSVIM was tested on both PacBio and Nanopore data.\nIt works best for alignment files produced by `NGMLR <https://github.com/philres/ngmlr>`_ but also supports the faster read mapper `minimap2 <https://github.com/lh3/minimap2>`_.\n\nOutput\n------\n\nSVIM distinguishes five different SV classes (see above schema): deletions, inversions, tandem and interspersed duplications and novel insertions.\nAdditionally, SVIM indicates for detected interspersed duplications whether the genomic origin location seems to be deleted in at least one haplotype (indicating a cut&paste insertion) or not (indicating a canonic interspersed duplication).\nFor each of these SV classes, SVIM produces a BED file with the SV coordinates.\nAdditionally, a VCF file is produced containing all found SVs.\n\nUsage\n----------------------\n\nPlease see our `wiki <https://github.com/eldariont/svim/wiki>`_.\n\nContact\n-------\n\nIf you experience problems or have suggestions please create an issue or a pull request or contact heller_d@molgen.mpg.de.\n\nCitation\n---------\n\nFeel free to read and cite our paper in Bioinformatics: https://doi.org/10.1093/bioinformatics/btz041\n\nLicense\n-------\n\nThe project is licensed under the GNU General Public License.\n\n\n",
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