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        "author": "Mobidic",
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        "classifiers": [
            "Development Status :: 5 - Production/Stable",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: Implementation :: PyPy",
            "Topic :: Scientific/Engineering :: Bio-Informatics"
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        "description": "# MPA: MoBiDiC Prioritizing Algorithm\n--------------------------------------------------------------------------------\n\n[![license](https://img.shields.io/github/license/mobidic/mpa.svg)](https://github.com/mobidic/MPA/blob/master/LICENSE)\n[![release](https://img.shields.io/github/release/mobidic/mpa.svg)](https://github.com/mobidic/MPA/releases)\n[![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat)](http://bioconda.github.io/recipes/mobidic-mpa/README.html)\n[![GitHub commits since latest release](https://img.shields.io/github/commits-since/mobidic/mpa/latest.svg)](https://github.com/mobidic/MPA/commits/master)\n\n[![MPA](https://raw.githubusercontent.com/mobidic/MPA/master/doc/img/logo-MPA.png)](#)\n\n## Overview\n\nThe MPA is a prioritizing algorithm for Next Generation Sequencing molecular\ndiagnosis. We propose an open source and free for academic user workflow.\n\nVariant ranking is made with a unique score that take into account curated\ndatabase, biological assumptions, splicing predictions and the sum of various\npredictors for missense alterations. Annotations are made for exonic and\nsplicing variants up to +300nt.\n\nWe show the pertinence of our clinical diagnosis approach with an updated\nevaluation of in silico prediction tools using DYSF, DMD, LMNA, NEB and TTN\nvariants from the human expert-feeded Universal Mutation Database [1] with\ncourtesy regards of curators for pathogenic variants and from the ExAc database\n[2] to define the dataset of neutral variants.\n\nMPA needs an annotated vcf by ANNOVAR and give as output an annotated vcf with MPA score & ranks.\n\n[![MPA diagram](https://raw.githubusercontent.com/mobidic/MPA/master/doc/img/MPA_diagram.png)](#)\n\n\\*PTC: Premature Truncation Codon : nonsense or frameshift\n\n\\**: intronic positions between -20 and +5\n\n### Input\n\nThe MPA uses, as input, an annotated VCF file with Annovar [3] and the following\ndatabases :\n\n- Curated database: ClinVar [4]\n- Biological assumption : refGene [5]\n- Splicing predicition : SpliceAI [6], dbscSNV [7]\n- Missense prediction : dbNSFP [8]\n\n> Note : Short tutorial to annotate your VCF with Annovar (cf. [Quick guide for Annovar](#quick-guide-for-annovar)).\n\n> **Update April 2019: spliceAI annotations now replace spidex. Waiting for spliceAI to be included in ANNOVAR, Files for this dataset in the proper format are available upon request (hg19 or hg38).**\n\n> Multi-allelic variants in vcf should be splitted to biallelic variants with bcftools norm.\n\n```bash\nbcftools norm -m - file.vcf > file_breakmulti.vcf\n```\n\n### Output\n\n#### In a VCF format\n\nVCF is annotated with multiples items : MPA_impact (Clinvar_pathogenicity, splice_impact, stop and frameshift_impact, missense_impact and unknown_impact), MPA_ranking (1 to 8), MPA_final_score (from 0 to 10) and details for the scoring as MPA_available (from 0 to 10 missense tools which annotate), MPA_deleterious (number of missense tools that annotate pathogenic), MPA_adjusted (normalize missense score from 0 to 10).\n\n#### Ranking : from 1 to 10 and score\n\n- *1.* clinvar_pathogenicity : Pathogenic variants reported on ClinVar (score : 10)\n- *2.* stop or frameshift_impact : Premature Truncation Codon : nonsense or frameshift (score : 10)\n- *3.* splicing_impact (ADA, RF) : Affecting splice variants predictions ranked by algorithm performance robustness and strength (score : 10)\n- *4.* splicing_impact (spliceAI high) : Affecting splice variants predictions ranked by algorithm performance robustness and strength (score : 10)\n- *5.* missense impact moderate to high impact (6-10)\n- *6.* moderate splicing_impact (spliceAI moderate) (score 6)\n- *7.* missense_impact moderate : Missense variants scores low impact (score : 2-6)\n- *8.* low splicing impact (spliceAI low) (indel) (score : 2)\n- *9.* missense_impact low : Missense variants scores low impact (score : 0-2)\n- *10.* unknown impact : Exonic variants with not clearly annotated ORFs and splicing variants not predicted pathogenic ; or NULL (no annotation on genes, splice etc...) (score : 0-10)\n\n#### With a simple interface (Captain ACHAB)\n\nMPA is a part of [MobiDL](https://github.com/mobidic/MobiDL) captainAchab workflow. MPA is the core of ranking in our useful and simple interface to easily interpret NGS variants at a glance named Captain ACHAB.\nFind more informations at [Captain ACHAB](https://github.com/mobidic/Captain-ACHAB)\n\n--------------------------------------------------------------------------------\n\n## Installation\n\n### Conda\n\n[![Conda (channel only)](https://img.shields.io/conda/vn/bioconda/mobidic-mpa.svg)](http://bioconda.github.io/recipes/mobidic-mpa/README.html)\n[![Conda](https://img.shields.io/conda/pn/bioconda/mobidic-mpa.svg)](http://bioconda.github.io/recipes/mobidic-mpa/README.html)\n[![Conda](https://img.shields.io/conda/dn/bioconda/mobidic-mpa.svg)](http://bioconda.github.io/recipes/mobidic-mpa/README.html)\n\nWith an activated Bioconda channel (see [2. set up channels](https://bioconda.github.io/user/install.html#set-up-channels)), install with:\n\n      conda install mobidic-mpa\n\nand update with:\n\n      conda update mobidic-mpa\n\nor use the docker container:\n\n      docker pull quay.io/biocontainers/mobidic-mpa:<tag>\n\n(see [mobidic-mpa/tags](https://quay.io/repository/biocontainers/mobidic-mpa?tab=tags) for valid values for ``<tag>``)\n\n### Pypi\n\n[![pythonV](https://img.shields.io/pypi/v/mobidic-mpa.svg)](https://pypi.org/project/mobidic-mpa)\n[![pypiS](https://img.shields.io/pypi/status/mobidic-mpa.svg)](https://pypi.org/project/mobidic-mpa)\n[![pypiV](https://img.shields.io/pypi/pyversions/mobidic-mpa.svg)](https://pypi.org/project/mobidic-mpa)\n[![PyPI - Downloads](https://img.shields.io/pypi/dm/mobidic-mpa.svg)](https://pypi.org/project/mobidic-mpa)\n\n#### Requirements\n\n* Python 3\n\n#### pip\n\n```bash\npython3 -m pip install mobidic-mpa\n```\n\n## Quick start\n\nTo run the MPA script, use this command line :\n\n```bash\nmpa -i path/to/input.vcf -o path/to/output.vcf\n```\n\n### Quick guide for Annovar\n\nThis algorithm introduce here need some basics annotation. We introduce here a\nquick guide to annotate your VCF files with Annovar.\n\n#### Install Annovar\n\nFollow instruction to download Annovar at :\n> [http://www.openbioinformatics.org/annovar/annovar_download_form.php](http://www.openbioinformatics.org/annovar/annovar_download_form.php)\n\nUnpack the package by using this command :\n\n```bash\ntar xvfz annovar.latest.tar.gz\n```\n\n#### Download all databases\n\nIn Annovar folder, download all database needed with annotate_variation.pl:\n\n```bash\nperl annotate_variation.pl -buildver hg19 -downdb -webfrom annovar refGene humandb/\nperl annotate_variation.pl -buildver hg19 -downdb -webfrom annovar clinvar_20190305 humandb/\nperl annotate_variation.pl -buildver hg19 -downdb -webfrom annovar dbnsfp35a  humandb/\nperl annotate_variation.pl -buildver hg19 -downdb -webfrom annovar dbscsnv11 humandb/\n```\n\nDeprecated: For Spidex database, follow instruction here :\n\n> [http://www.openbioinformatics.org/annovar/spidex_download_form.php](http://www.openbioinformatics.org/annovar/spidex_download_form.php)\n\n> **Update April 2019: spliceAI annotations now replace spidex. Waiting for spliceAI to be included in ANNOVAR, Files for this dataset in the proper format are available upon request (hg19 or hg38).**\n\n#### Annotate a VCF\n\nThe following command line annotate a VCF file :\n\n```bash\nperl path/to/table_annovar.pl path/to/example.vcf humandb/ -buildver hg19 -out path/to/output/name -remove -protocol refGene,refGene,clinvar_20190305,dbnsfp35a,spliceai_filtered,dbscsnv11 -operation g,g,f,f,f,f -nastring . -vcfinput -otherinfo -arg '-splicing 20','-hgvs',,,,\n```\n\n### Citing MPA\n\n> **Yauy et al.** MPA, a free, accessible and efficient pipeline for SNV annotation and prioritization for NGS routine molecular diagnosis. **The Journal of Molecular Diagnostics (2018)** https://doi.org/10.1016/j.jmoldx.2018.03.009\n\n--------------------------------------------------------------------------------\n\n**Montpellier Bioinformatique pour le Diagnostic Clinique (MoBiDiC)**\n\n*CHU de Montpellier*\n\nFrance\n\n[![MoBiDiC](https://raw.githubusercontent.com/mobidic/MPA/master/doc/img/logo-mobidic.png)](https://github.com/mobidic/)\n\n[Visit our website](https://neuro-2.iurc.montp.inserm.fr/mobidic/)\n\n--------------------------------------------------------------------------------\n\n1. B\u00e9roud, C. et al. UMD (Universal Mutation Database): 2005 update. *Hum. Mutat.* **26**, 184\u2013191 (2005).\n2. Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. *Nature* **536**, 285\u2013291 (2016).\n3. Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. *Nucleic Acids Res.* **38**, e164\u2013e164 (2010).\n4. Landrum, M. J. et al. ClinVar: public archive of interpretations of clinically relevant variants. *Nucleic Acids Res.* **44**, D862\u2013D868 (2015).\n5. O\u2019Leary, N. A. et al. Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. *Nucleic Acids Res.* **44**, D733\u201345 (2016).\n6. Jaganathan et al. Predicting Splicing from Primary Sequence with Deep Learning. *Cell* **176**, 535-548 (2019).\n7. Jian, X., Boerwinkle, E. & Liu, X. In silico prediction of splice-altering single nucleotide variants in the human genome. *Nucleic Acids Res.* **42**, 13534\u201313544 (2014).\n8. Liu, X., Wu, C., Li, C. & Boerwinkle, E. dbNSFP v3.0: A One-Stop Database of Functional Predictions and Annotations for Human Nonsynonymous and Splice-Site SNVs. *Hum. Mutat.* **37**, 235\u2013241 (2016).\n\n\n",
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