ecbc3bbbb0480197f474c00f0836eedf926b125a lrnassar Fri Aug 23 14:11:56 2024 -0700 Updating the mouseovers of spliceAI due to feedback, refs #34336 diff --git src/hg/makeDb/trackDb/human/spliceAI.html src/hg/makeDb/trackDb/human/spliceAI.html index d73d187..7c15496 100644 --- src/hg/makeDb/trackDb/human/spliceAI.html +++ src/hg/makeDb/trackDb/human/spliceAI.html @@ -20,37 +20,36 @@ 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 by their predicted effects: +Variants are colored according to Walker et al. 2023 splicing imact:

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. @@ -95,15 +94,26 @@ 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 +