SpliceAI-visual: a free online tool to improve SpliceAI splicing variant interpretation

Abstract

SpliceAI is an open-source deep learning splicing prediction algorithm that has demonstrated in the past few years its high ability to predict splicing defects caused by DNA variations. However, its outputs present several drawbacks: (1) although the numerical values are very convenient for batch filtering, their precise interpretation can be difficult, (2) the outputs are delta scores which can sometimes mask a severe consequence, and (3) complex delins are most often not handled. We present here SpliceAI-visual, a free online tool based on the SpliceAI algorithm, and show how it complements the traditional SpliceAI analysis. First, SpliceAI-visual manipulates raw scores and not delta scores, as the latter can be misleading in certain circumstances. Second, the outcome of SpliceAI-visual is user-friendly thanks to the graphical presentation. Third, SpliceAI-visual is currently one of the only SpliceAI-derived implementations able to annotate complex variants (e.g., complex delins). We report here the benefits of using SpliceAI-visual and demonstrate its relevance in the assessment/modulation of the PVS1 classification criteria. We also show how SpliceAI-visual can elucidate several complex splicing defects taken from the literature but also from unpublished cases. SpliceAI-visual is available as a Google Colab notebook and has also been fully integrated in a free online variant interpretation tool, MobiDetails (https://mobidetails.iurc.montp.inserm.fr/MD). Graphical abstract

In silico Splicing Analysis of the PMS2 Gene: Exploring Alternative Molecular Mechanisms in PMS2-Associated Lynch Syndrome

“…(3) The DS threshold > 0.2 could impact variant classification in PMS2 gene, clinically significant variants being reportedly overseen in other genes when a standard cutoff value was used [17]; (4) Alternative splicing have a secondary role in the biology of PMS2 gene, according to what was previously reported in literature [40].…”

“…The strength analysis of canonical acceptor and donor splice sites was conducted using freely available online tools, including ESEfinder 3.0 for splice sites (https://esefinder.ahc.umn.edu/), FSplice (http://www.softberry.com/), MaxEntScan (http://hollywood.mit.edu/), NetGene2 (https://services.healthtech.dtu.dk/) and NNSplice (https://www.fruitfly.org/). The potential splicing impact of reported variants was estimated using SpliceAI, one of the most proficient deep learningbased tool reported to date [16,17,33]. Delta scores (DS) greater than 0.2 were used to screen for splicealtering variants, as outlined in the original publication [16].…”

“…Further research uncovered the significance of additional factors in exon recognition, such as splicing regulatory elements, chromatin structure, transcription rate, and the secondary and tertiary structure of the transcript [13][14][15]. With the recent development and implementation of in silico tools and deep learning-based algorithms [16,17], many exonic variants formerly classified as missense, nonsense, or silent are susceptible to reclassification, with some revealing a predicted impact on splicing [17][18][19][20][21]. In this scenario, the pathogenic mechanism usually involve a mixture of transcripts with abnormal splicing patterns and transcripts carrying the causative variant [22][23][24].…”

Waardenburg Syndrome: The Contribution of Next-Generation Sequencing to the Identification of Novel Causative Variants

“…The exon and gene deletions, indels, nonsense variants, and one of the splice variants are loss-of-function mutations, thus considered to be the cause of the phenotypes. The synonymous alteration (p.Thr303=) in MITF was predicted by SpliceAI [36] to affect splicing and it was functionally tested and demonstrated to generate a new splice site, removing the first 52 base pairs of exon 9 and generating a frameshift that adds 7 new amino acids at position 387, creating a new premature stop codon [37]. This synonymous variant can be described by its RNA alteration and final protein implication as r.859_910del and p.Glu287Valfs*8.…”

CFAP47 is a novel causative gene implicated in X-linked polycystic kidney disease

“…Reads were base called with Guppy 5.0.7 (Oxford Nanopore Technologies) using the super accurate model and aligned to GRCh38 with minimap2 16 . Variants were called and phased using Medaka (Oxford Nanopore Technologies), then annotated with VEP 17 to include CADD 18 , SpliceAI 19 scores, and gnomAD v3 allele frequency. SVs were called using Sniffles 20 and SVIM 21 .…”