SpliceAI is an open-source deep learning splicing prediction algorithm that can predict splicing alterations caused by DNA variations. Such variants may activate nearby cryptic splice sites, leading to abnormal transcript isoforms. SpliceAI was developed at Illumina; a lookup tool is provided by the Broad institute.
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.
The unmasked tracks 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 the unmasked tracks for alternative splicing analysis and masked tracks for variant interpretation.
-Variants are colored by their predicted effects: +Variants are colored according to Walker et al. 2023 splicing imact:
The scores range 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.
The data were downloaded from Illumina.
The spliceAI scores are represented in the VCF INFO field as
SpliceAI=G|OR4F5|0.01|0.00|0.00|0.00|-32|49|-40|-31
Here, the pipe-separated fields contain
Since most of the values are 0 or almost 0, we selected only those variants with a score equal to or greater than 0.02.
The complete processing of this track can be found in the makedoc.
FOR ACADEMIC AND NOT-FOR-PROFIT RESEARCH USE ONLY. The SpliceAI scores are made available by Illumina only for academic or not-for-profit research only. By accessing the SpliceAI data, you acknowledge and agree that you may only use this data for your own personal academic or not-for-profit research only, and not for any other purposes. You may not use this data for any for-profit, clinical, or other commercial purpose without obtaining a commercial license from Illumina, Inc.
Jaganathan K, Kyriazopoulou Panagiotopoulou S, McRae JF, Darbandi SF, Knowles D, Li YI, Kosmicki JA, Arbelaez J, Cui W, Schwartz GB et al. Predicting Splicing from Primary Sequence with Deep Learning. Cell. 2019 Jan 24;176(3):535-548.e24. PMID: 30661751
+ ++Walker LC, Hoya M, Wiggins GAR, Lindy A, Vincent LM, Parsons MT, Canson DM, Bis-Brewer D, Cass A, +Tchourbanov A et al. + +Using the ACMG/AMP framework to capture evidence related to predicted and observed impact on +splicing: Recommendations from the ClinGen SVI Splicing Subgroup. +Am J Hum Genet. 2023 Jul 6;110(7):1046-1067. +PMID: 37352859; PMC: PMC10357475 +