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SpliceAI SpliceAI SNVs (masked) Track Settings
 
SpliceAI SNVs (masked)

Track collection: SpliceAI: Splice Variant Prediction Score

+  Description
+  All tracks in this collection (4)

Display mode:      Duplicate track

Minimum spliceAI score: (0.02 to 1)
Filter by Splice type (select multiple items - Help)


Display data as a density graph:
Source data version: Illumina SpliceAI Score v1.3
Assembly: Human Feb. 2009 (GRCh37/hg19)
Data last updated at UCSC: 2024-08-22 22:33:52


Important: The SpliceAI data on the UCSC Genome Browser is directly from Illumina (See Data Access below). However, since SpliceAI refers to the algorithm, and not the computed dataset, the data on the Broad server or other sources may have some differences between them.

Description

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.

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.

What are the differeneces between masked and unmasked tracks?

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.

Display Conventions and Interpretation

Variants are colored according to Walker et al. 2023 splicing imact:

  • Predicted impact on splicing: Score >= 0.2
  • Not informative: Score < 0.2 and > 0.1
  • No impact on splicing: Score <= 0.1

Mouseover on items shows the variant, gene name, type of change (donor gain/loss, acceptor gain/loss), location of affected cryptic splice, and spliceAI score. Clicking on any item brings up a table with this information.

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.

Methods

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

  • ALT allele
  • Gene name
  • Acceptor gain score
  • Acceptor loss score
  • Donor gain score
  • Donor loss score
  • Relative location of affected cryptic acceptor
  • Relative location of affected acceptor
  • Relative location of affected cryptic donor
  • Relative location of affected donor

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.

Data Access

These data are not available for download from the Genome Browser. The raw data can be found directly on Illumina. See below for a copy of the license restrictions pertaining to these data.

License

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.

References

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