SpliceAI_AllDelta: 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.

This is a modified version for retreiving not only maximum of delta values, but all delta values.

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 grch37

# or you can pipe the input and output VCFs
cat input.vcf | spliceai -R genome.fa -A grch37 > output.vcf

Options:

• -I: Input VCF with variants of interest.
• -O: Output VCF with SpliceAI predictions SpliceAI=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 must be a single base) within genes are annotated. Variants in multiple genes have separate predictions for each gene.
• -R: Reference genome fasta file.
• -A: Gene annotation file. Can instead provide grch37 or grch38 to use GENCODE canonical annotation files included with the package. To create custom annotation files, use spliceai/annotations/grch37.txt in repository as template.

Note: The annotations for all possible SNVs within genes are available here for download.

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).

Examples

A sample input file and the corresponding output file can be found at examples/input.vcf and examples/output.vcf respectively (grch37 annotation). 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: kishorejaganathan@gmail.com