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. The annotations for all possible substitutions, 1 base insertions, and 1-4 base deletions within genes are available here for download. These annotations are free for academic and not-for-profit use; other use requires a commercial license from Illumina, Inc.

License

SpliceAI source code is provided under the GPLv3 license. SpliceAI includes several third party packages provided under other open source licenses, please see NOTICE for additional details. The trained models used by SpliceAI (located in this package at spliceai/models) are provided under the CC BY NC 4.0 license for academic and non-commercial use; other use requires a commercial license from Illumina, Inc.

Installation

This release can most easily be used as a docker container:

docker pull cmgantwerpen/spliceai_v1.3:latest

docker run --gpus all cmgantwerpen/spliceai_v1.3:latest spliceai -h 

The simplest way to install (the original version of) SpliceAI is through pip or conda:

pip install spliceai
# or
conda install -c bioconda spliceai

Alternately, SpliceAI can be installed from the github repository:

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

SpliceAI requires tensorflow>=1.2.0, which is best installed separately via pip or conda (see the TensorFlow website for other installation options):

pip install tensorflow
# or
conda install tensorflow

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

Required parameters:

• -I: Input VCF with variants of interest.
• -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.
• -R: Reference genome fasta file. Can be downloaded from GRCh37/hg19 or GRCh38/hg38.
• -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 and provide as full path.

Optional parameters:

• -D: Maximum distance between the variant and gained/lost splice site (default: 50).
• -M: Mask scores representing annotated acceptor/donor gain and unannotated acceptor/donor loss (default: 0).
• -B: Number of predictions to collect before running models on them in batch. (default: 1 (don't batch))
• -T: Internal Tensorflow predict() batch size if you want something different from the -B value. (default: the -B value)
• -V: Enable verbose logging during run
• -t: Specify a location to create the temporary files
• -G: Specify the GPU(s) to run on : either indexed (eg : 0,2) or 'all'. (default: 'all')
• -S: Simulate n multiple GPUs on a single physical device. Used for development only, currently all values above 2 crashed due to memory issues. (default: 0)
• -P: Port to use when connecting to the socket (default: 54677, only used in batch mode).

Batching Considerations:

When setting the batching parameters, be mindful of the system and gpu memory of the machine you are running the script on. Feel free to experiment, but some reasonable -T numbers would be 64/128. CPU memory is larger, and increasing -B might further improve performance.

Batching Performance Benchmarks:

• Input data: GATK generated WES sample with ~ 90K variants in genome build GRCh37.
• Total predictions made : 174,237
• invitae v2 mainly implements logic to prioritize full batches while predicting
• settings :
  • invitae & invitae v2 : B = T = 64
  • invitae v2 optimal : on V100 : B = 4096 ; T = 256 -- on K80/GeForce : B = 4096 ; T = 64

Benchmark results

Type	Implementation	Total Time	Speed (predictions / hour)
CPU (intel i5-8365U)a	illumina	~100h	~1000 pred/h
	invitae	~39h	~4500 pred/h
	invitae v2	~35h	~5000 pred/h
	invitae v2 optimal	~35h	~5000 pred/h
K80 GPU (AWS p2.large)	illuminab	~25 h	~7000 pred/h
	invitae	242m	~43,000 pred / h
	invitae v2	213m	~50,000 pred / h
	invitae v2 optimal	188 m	~56,000 pred / h
GeForce RTX 2070 SUPER GPU (desktop)	illuminab	~10 h	~ 17,000 pred/h
	invitae	76m	~137,000 pred / h
	invitae v2	63m	~166,000 pred / h
	invitae v2 optimal	52m	~200,000 pred / h
V100 GPU (AWS p3.xlarge)	illuminab	~10h	~18,000 pred/h
	invitae	78m	~135,000 pred / h
	invitae v2	54m	~190,000 pred / h
	invitae v2 optimal	31 m	~335,000 pred / h

^(a) : Extrapolated from first 500 variants

^(b) : Illumina implementation showed a memory leak with the installed versions of tf/keras/.... Values extrapolated from incomplete runs at the point of OOM.

Note: On a p3.8xlarge machine, hosting 4 V100 GPU's, we were able reach 1,379,505 predictions/hour ! This is a nearly linear scale-up.

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, 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).

Examples

A 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:

• The probability that the position 19:38958360 (=38958362-2) is used as a splice donor increases by 0.91.
• The probability that the position 19:38958331 (=38958362-31) is used as a splice donor decreases by 0.08.

Similarly, 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:

• The probability that the position 2:179415981 (=179415988-7) is used as a splice acceptor increases by 0.07.
• The probability that the position 2:179415987 (=179415988-1) is used as a splice acceptor decreases by 1.00.

Frequently asked questions

1. Why are some variants not scored by SpliceAI?

SpliceAI only annotates variants within genes defined by the gene annotation file. Additionally, SpliceAI does not annotate variants if they are close to chromosome ends (5kb on either side), deletions of length greater than twice the input parameter -D, or inconsistent with the reference fasta file.

2. What are the differences between raw (-M 0) and masked (-M 1) precomputed files?

The raw files also include splicing changes corresponding to strengthening annotated splice sites and weakening unannotated splice sites, which are typically much less pathogenic than weakening annotated splice sites and strengthening unannotated splice sites. The delta scores of such splicing changes are set to 0 in the masked files. We recommend using raw files for alternative splicing analysis and masked files for variant interpretation.

3. Can SpliceAI be used to score custom sequences?

Yes, install SpliceAI and use the following script:

from keras.models import load_model
from pkg_resources import resource_filename
from spliceai.utils import one_hot_encode
import numpy as np

input_sequence = 'CGATCTGACGTGGGTGTCATCGCATTATCGATATTGCAT'
# Replace this with your custom sequence

context = 10000
paths = ('models/spliceai{}.h5'.format(x) for x in range(1, 6))
models = [load_model(resource_filename('spliceai', x)) for x in paths]
x = one_hot_encode('N'*(context//2) + input_sequence + 'N'*(context//2))[None, :]
y = np.mean([models[m].predict(x) for m in range(5)], axis=0)

acceptor_prob = y[0, :, 1]
donor_prob = y[0, :, 2]

Modifications to Original

Batching Support - Invitae (December 2021)

• Adds new command line parameters, --prediction-batch-size and --tensorflow-batch-size to support batching variants to optimize prediction utilization on a GPU
• Adds a VCFPredictionBatch class that manages collection the VCF records, placing them in batches based on the encoded tensor size. Once the batch size is reached, predictions are run in batches, then output is written back in the original order reassembling the annotations for the VCF record. Each VCF record has a lookup key for where each of the ref/alts are within their batches, so it knows where to grab the results during reassembly
• Breaks out code in the existing get_delta_scores method into reusable methods used in the batching and the original source code. This way the batching code can utilize the same logic inside that method while still maintaining the original version
• Adds batch utility methods that split up what was all previously done in get_delta_scores. encode_batch_record handles what was in the first half, taking in the VCF record and generating one-hot encoded matrices for the ref/alts. extract_delta_scores handles the second half of the get_delta_scores by reassembling the annotations based on the batched predictions
• Adds test cases to run a small file using a generated FASTA reference to test if the results are the same with no batching and with different batching sizes
• Slightly modifies the entrypoint of running the code to allow for easier unit testing. Being able to pass in what would normally come from the argparser

Multi-GPU support - Geert Vandeweyer (November 2022)

• Offload more code to CPU (eg np to tensor conversion) to only perform predictions on the GPU
• Implement queuing system to always have full batches ready for prediction
• Implement new parameter, --tmpdir to support a custom tmp folder to store prepped batches
• Implement socket-based client/server approach to scale over multiple GPUs

Contact

Kishore Jaganathan: [email protected]

Geert Vandeweyer (This implementation) : [email protected]