Performance evaluation of differential splicing analysis methods and splicing analytics platform construction

Abstract

A proportion of previously defined benign variants or variants of uncertain significance in humans, which are challenging to identify, may induce an abnormal splicing process. An increasing number of methods have been developed to predict splicing variants, but their performance has not been completely evaluated using independent benchmarks. Here, we manually sourced ∼50 000 positive/negative splicing variants from > 8000 studies and selected the independent splicing variants to evaluate the performance of prediction methods. These methods showed different performances in recognizing splicing variants in donor and acceptor regions, reminiscent of different weight coefficient applications to predict novel splicing variants. Of these methods, 66.67% exhibited higher specificities than sensitivities, suggesting that more moderate cut-off values are necessary to distinguish splicing variants. Moreover, the high correlation and consistent prediction ratio validated the feasibility of integration of the splicing prediction method in identifying splicing variants. We developed a splicing analytics platform called SPCards, which curates splicing variants from publications and predicts splicing scores of variants in genomes. SPCards also offers variant-level and gene-level annotation information, including allele frequency, non-synonymous prediction and comprehensive functional information. SPCards is suitable for high-throughput genetic identification of splicing variants, particularly those located in non-canonical splicing regions.

Novel MEIOB pathogenic variants including a homozygous non‐canonical splicing variant, cause meiotic arrest and human non‐obstructive azoospermia

“…9 All the three novel variants of MEIOB were considered to be absent in the general population and predicted to be damaging. 10,11 Further Sanger sequencing confirmed MEIOB variants in the two probands conformed to typical autosomal recessive inheritance pattern (Figure 1A,B). The MEIOB (p.R272X and p.A326T) variants in patient 1 were located in OB domains and were highly conserved across different species (Figure 1C).…”

“…The MEIOB homozygous nonsense variant (c.814C > T) has been identified in one POI patient 9 . All the three novel variants of MEIOB were considered to be absent in the general population and predicted to be damaging 10,11 . Further Sanger sequencing confirmed MEIOB variants in the two probands conformed to typical autosomal recessive inheritance pattern (Figure 1A,B).…”

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

“…Therefore, there is a strong need for in silico tools that can facilitate the precise interpretation of candidate variants to (1) correctly prioritize the best candidates to be investigated, and (2) choose the optimal functional validation test according to the expected alteration. The efficiency of SpliceAI to predict a variant's splicing alteration has been attested by multiple studies [2][3][4][5][6][7][8][9][10]. Furthermore, thanks to its neural network, SpliceAI is able to make predictions about the global splicing outcome (e.g., exon skipping, splicing rescue by cryptic site activation, pseudo-exon creation, etc.).…”

An RNA foundation model enables discovery of disease mechanisms and candidate therapeutics

“…Another class of pathogenic splicing variants are cryptic splicing mutations that cause full intron retention. We evaluated BigRNA on its ability to predict a set of reported intron retention variants 27 , using nearby common variants as the negative set. We observed strong performance on classifying these mutations (AUC=0.9, Fig.…”