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        "author": "Kishore Jaganathan",
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        "description": "## SpliceAI: A deep learning-based tool to identify splice variants\n[![release](https://img.shields.io/badge/release-v1.3-orange.svg)](https://img.shields.io/badge/release-v1.3-orange.svg)\n[![license](https://img.shields.io/badge/license-GPLv3-green.svg)](https://img.shields.io/badge/license-GPLv3-green.svg)\n[![downloads](https://pepy.tech/badge/spliceai)](https://pepy.tech/badge/spliceai)\n\nThis package annotates genetic variants with their predicted effect on splicing, as described in [Jaganathan *et al*, Cell 2019 in press](https://doi.org/10.1016/j.cell.2018.12.015).\n\n**Update**: The annotations for all possible substitutions, 1 base insertions, and 1-4 base deletions within genes are available [here](https://basespace.illumina.com/analyses/194103939/files?projectId=66029966) for download.\n\n### Installation\nThe simplest way to install SpliceAI is through pip or conda:\n```sh\npip install spliceai\n# or\nconda install -c bioconda spliceai\n```\n\nAlternately, SpliceAI can be installed from the [github repository](https://github.com/Illumina/SpliceAI.git):\n```sh\ngit clone https://github.com/Illumina/SpliceAI.git\ncd SpliceAI\npython setup.py install\n```\n\nSpliceAI requires ```tensorflow>=1.2.0```, which is best installed separately via pip or conda (see the [TensorFlow](https://www.tensorflow.org/) website for other installation options):\n```sh\npip install tensorflow\n# or\nconda install tensorflow\n```\n\n### Usage\nSpliceAI can be run from the command line:\n```sh\nspliceai -I input.vcf -O output.vcf -R genome.fa -A grch37\n# or you can pipe the input and output VCFs\ncat input.vcf | spliceai -R genome.fa -A grch37 > output.vcf\n```\n\nRequired parameters:\n - ```-I```: Input VCF with variants of interest.\n - ```-O```: Output VCF with SpliceAI predictions `ALLELE|SYMBOL|DS_AG|DS_AL|DS_DG|DS_DL|DP_AG|DP_AL|DP_DG|DP_DL` included in the INFO column (see table below for details). Only SNVs and simple INDELs (REF or ALT is a single base) within genes are annotated. Variants in multiple genes have separate predictions for each gene.\n - ```-R```: Reference genome fasta file. Can be downloaded from [GRCh37/hg19](http://hgdownload.cse.ucsc.edu/goldenPath/hg19/bigZips/hg19.fa.gz) or [GRCh38/hg38](http://hgdownload.cse.ucsc.edu/goldenPath/hg38/bigZips/hg38.fa.gz).\n - ```-A```: Gene annotation file. Can instead provide `grch37` or `grch38` to use GENCODE V24 canonical annotation files included with the package. To create custom gene annotation files, use `spliceai/annotations/grch37.txt` in repository as template.\n\nOptional parameters:\n - ```-D```: Maximum distance between the variant and gained/lost splice site (default: 50).\n - ```-M```: Mask scores representing annotated acceptor/donor gain and unannotated acceptor/donor loss (default: 0).\n\nDetails of SpliceAI INFO field:\n\n|    ID    | Description |\n| -------- | ----------- |\n|  ALLELE  | Alternate allele |\n|  SYMBOL  | Gene symbol |\n|  DS_AG   | Delta score (acceptor gain) |\n|  DS_AL   | Delta score (acceptor loss) |\n|  DS_DG   | Delta score (donor gain) |\n|  DS_DL   | Delta score (donor loss) |\n|  DP_AG   | Delta position (acceptor gain) |\n|  DP_AL   | Delta position (acceptor loss) |\n|  DP_DG   | Delta position (donor gain) |\n|  DP_DL   | Delta position (donor loss) |\n\nDelta score of a variant, defined as the maximum of (DS_AG, DS_AL, DS_DG, DS_DL), ranges from 0 to 1 and can be interpreted as the probability of the variant being splice-altering. In the paper, a detailed characterization is provided for 0.2 (high recall), 0.5 (recommended), and 0.8 (high precision) cutoffs. Delta position conveys information about the location where splicing changes relative to the variant position (positive values are downstream of the variant, negative values are upstream).\n\n### Examples\nA sample input file and the corresponding output file can be found at `examples/input.vcf` and `examples/output.vcf` respectively. The output `T|RYR1|0.00|0.00|0.91|0.08|-28|-46|-2|-31` for the variant `19:38958362 C>T` can be interpreted as follows:\n* The probability that the position 19:38958360 (=38958362-2) is used as a splice donor increases by 0.91.\n* The probability that the position 19:38958331 (=38958362-31) is used as a splice donor decreases by 0.08.\n\nSimilarly, the output `CA|TTN|0.07|1.00|0.00|0.00|-7|-1|35|-29` for the variant `2:179415988 C>CA` has the following interpretation:\n* The probability that the position 2:179415981 (=179415988-7) is used as a splice acceptor increases by 0.07.\n* The probability that the position 2:179415987 (=179415988-1) is used as a splice acceptor decreases by 1.00.\n### Contact\nKishore Jaganathan: kishorejaganathan@gmail.com\n\n\n",
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