Mechanism and modeling of human disease-associated near-exon intronic variants that perturb RNA splicing

Chiang, Hung-Lun; Chen, Yi-Ting; Su, Jia-Ying; H, Lin; Yu, Chen-Hsin Albert; Hung, Yu-Jen; Wang, Yunlin; Huang, Yen‐Tsung; Lin, Chien-Ling

doi:10.1038/s41594-022-00844-1

Cited by 14 publications

(8 citation statements)

References 60 publications

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“…BPs are known to be highly degenerate, with only the A and the T located two base pairs upstream being highly conserved within the consensus branchpoint motif [87]. In addition, BPs can occur at different positions within the intron, some introns have multiple BP, and the molecular consequences of interfering with the BP can be very diverse [88, 89, 90]. Similarly, predicting variant effects on splicing regulatory elements is challenging.…”

Section: Discussionmentioning

confidence: 99%

Computational prediction of human deep intronic variation

Barbosa

Savisaar²,

Carmo‐Fonseca

et al. 2023

Preprint

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The adoption of whole genome sequencing in genetic screens has facilitated the detection of genetic variation in the intronic regions of genes, far from annotated splice sites. However, selecting an appropriate computational tool to differentiate functionally relevant genetic variants from those with no effect is challenging, particularly for deep intronic regions where independent benchmarks are scarce. In this study, we have provided an overview of the computational methods available and the extent to which they can be used to analyze deep intronic variation. We leveraged diverse datasets to extensively evaluate tool performance across different intronic regions, distinguishing between variants that are expected to disrupt splicing through different molecular mechanisms. Notably, we compared the performance of SpliceAI, a widely used sequence-based deep learning model, with that of more recent methods that extend its original implementation. We observed considerable differences in tool performance depending on the region considered, with variants generating cryptic splice sites being better predicted than those that affect splicing regulatory elements or the branchpoint region. Finally, we devised a novel quantitative assessment of tool interpretability and found that tools providing mechanistic explanations of their predictions are often correct with respect to the ground truth information, but the use of these tools results in decreased predictive power when compared to black box methods. Our findings translate into practical recommendations for tool usage and provide a reference framework for applying prediction tools in deep intronic regions, enabling more informed decision-making by practitioners.

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Section: Discussionmentioning

confidence: 99%

Computational prediction of human deep intronic variation

Barbosa

Savisaar²,

Carmo‐Fonseca

et al. 2023

Preprint

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“…For example, multiplexed splicing readouts were recently applied to LMNA, MYBPC3 , and TTN variants using minigene-based large oligo constructs and barcoding 20,21 . Although some previous studies have examined splice outcomes for thousands of multiplexed variants, these experiments are typically limited to only small exons with minimal adjacent intronic sequence due to the size limits of chip-based oligonucleotide synthesis 6,18,19,69 . This approach often necessitates the inclusion of only small adjacent intronic regions and precludes studying large exons.…”

Section: Discussionmentioning

confidence: 99%

ParSE-seq: A Calibrated Multiplexed Assay to Facilitate the Clinical Classification of Putative Splice-altering Variants

O’Neill,

Yang,

Laudeman

et al. 2023

Preprint

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BackgroundInterpreting the clinical significance of putative splice-altering variants outside 2-base pair canonical splice sites remains difficult without functional studies.MethodsWe developed Parallel Splice Effect Sequencing (ParSE-seq), a multiplexed minigene-based assay, to test variant effects on RNA splicing quantified by high-throughput sequencing. We studied variants in SCN5A, an arrhythmia-associated gene which encodes the major cardiac voltage-gated sodium channel. We used the computational tool SpliceAI to prioritize exonic and intronic candidate splice variants, and ClinVar to select benign and pathogenic control variants. We generated a pool of 284 barcoded minigene plasmids, transfected them into Human Embryonic Kidney (HEK293) cells and induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs), sequenced the resulting pools of splicing products, and calibrated the assay to the American College of Medical Genetics and Genomics scheme. Variants were interpreted using the calibrated functional data, and experimental data were compared to SpliceAI predictions. We further studied some splice-altering missense variants by cDNA-based automated patch clamping (APC) in HEK cells and assessed splicing and sodium channel function in CRISPR-edited iPSC-CMs.ResultsParSE-seq revealed the splicing effect of 224SCN5Avariants in iPSC-CMs and 244 variants in HEK293 cells. The scores between the cell types were highly correlated (R2=0.84). In iPSCs, the assay had concordant scores for 21/22 benign/likely benign and 24/25 pathogenic/likely pathogenic control variants from ClinVar. 43/112 exonic variants and 35/70 intronic variants with determinate scores disrupted splicing. 11 of 42 variants of uncertain significance were reclassified, and 29 of 34 variants with conflicting interpretations were reclassified using the functional data. SpliceAI computational predictions correlated well with experimental data (AUC = 0.96). We identified 20 uniqueSCN5Amissense variants that disrupted splicing, and 2 clinically observed splice-altering missense variants of uncertain significance had normal function when tested with the cDNA-based APC assay. A splice-altering intronic variant detected by ParSE-seq, c.1891-5C>G, also disrupted splicing and sodium current when introduced into iPSC-CMs at the endogenous locus by CRISPR editing.ConclusionsParSE-seq is a calibrated, multiplexed, high-throughput assay to facilitate the classification of candidate splice-altering variants.

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“…20 This is problematic given that an estimated 10 to 30% of disease variants affect splicing. [20][21][22][23] Tools such as Introme 20 can be used to detect potential atypical splicing variants with clinical inference, and hold promise of improving diagnostic yields, even for retrospective analyses of existing datasets and previously overlooked synonymous coding variants.…”

Section: Advances In the Analysis And Interpretation Of Short-read Se...mentioning

confidence: 99%

The Next, Next-Generation of Sequencing, Promising to Boost Research and Clinical Practice

Kumar,

Cowley,

Davis

2024

Semin Thromb Hemost

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Mechanism and modeling of human disease-associated near-exon intronic variants that perturb RNA splicing

Cited by 14 publications

References 60 publications

Computational prediction of human deep intronic variation

Computational prediction of human deep intronic variation

ParSE-seq: A Calibrated Multiplexed Assay to Facilitate the Clinical Classification of Putative Splice-altering Variants

The Next, Next-Generation of Sequencing, Promising to Boost Research and Clinical Practice

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