spliceai 1.2.1

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

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 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: [e-mail]