SpliceAI: A deep learning-based tool to identify splice variants

This package annotates genetic variants with their predicted effect on splicing, as described in Jaganathan et al, Cell 2019 in press.

Installation

The simplest way to install SpliceAI is through pip:

pip install spliceai

Alternately, SpliceAI can be installed from the github repository:

git clone https://github.com/Illumina/SpliceAI.git
cd SpliceAI
python setup.py install

SpliceAI requires tensorflow>=1.2.0, which is best installed separately via pip: pip install tensorflow. See the TensorFlow website for other installation options.

Usage

SpliceAI can be run from the command line:

spliceai -I input.vcf -O output.vcf -R genome.fa [-A annotations.tsv]

# or you can pipe the input and output VCFs
cat input.vcf | spliceai -R genome.fa [-A annotations.tsv] > output.vcf

Options:

• -I: The input VCF with variants of interest. Only SNVs and simple indels (ref or alt must be a single base) are scored.
• -O: The output VCF with SpliceAI predictions included in the INFO column (SpliceAI=ALLELE|SYMBOL|DS_AG|DS_AL|DS_DG|DS_DL|DP_AG|DP_AL|DP_DG|DP_DL, see table below for details). Variants in multiple genes have separate predictions for each gene. Variants outside gene regions are not scored.
• -R: Reference genome fasta file (should be on hg19/GRCh37 if using default -A parameter).
• -A: Optional tab-separated file with columns for gene symbol, chromosome, strand, transcription start, and transcription end. See spliceai/annotations/GENCODE.v24lift37 in repository as template for creating custom annotations).

Details of SpliceAI INFO field:

ID	Description
ALLELE	Alternate allele
SYMBOL	Gene symbol
DS_AG	Delta score (acceptor gain)
DS_AL	Delta score (acceptor loss)
DS_DG	Delta score (donor gain)
DS_DL	Delta score (donor loss)
DP_AG	Delta position (acceptor gain)
DP_AL	Delta position (acceptor loss)
DP_DG	Delta position (donor gain)
DP_DL	Delta position (donor loss)

Delta score of a variant 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/likely pathogenic), 0.5 (recommended/pathogenic), and 0.8 (high precision/pathogenic) cutoffs. Delta position conveys information about the location where splicing changes relative to the variant position (positive values are upstream of the variant, negative values are downstream).

For the sake of convenience, we have precomputed scores for all possible single nucleotide variants within genes, which are available here.

Examples

A sample input file and the corresponding output file can be found at examples/input.vcf and examples/output.vcf respectively. The output SpliceAI=T|RYR1|0.22|0.00|0.91|0.70|-107|-46|-2|90 for the variant 19:38958362 C>T can be interpreted as follows:

• The probability that the position 19:38958255 is used as a splice acceptor increases by 0.22.
• The probability that the position 19:38958360 is used as a splice donor increases by 0.91.
• The probability that the position 19:38958452 is used as a splice donor decreases by 0.70.

Contact

Kishore Jaganathan: [email protected]