Vex-seq: high-throughput identification of the impact of genetic variation on pre-mRNA splicing efficiency

Adamson, Scott I.; Zhan, Lijun; Graveley, Brenton R.

doi:10.1186/s13059-018-1437-x

Cited by 76 publications

(90 citation statements)

References 31 publications

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“…The Vex‐seq experiment measured the

Δ Ψ

of 2,055 variants from the Exome Aggregation Consortium (ExAC; Kircher et al, ) using a library of reporter constructs transfected into HepG2 cells (Adamson et al, ). Variants on chromosomes 1–8 were assigned to the training set, and variants on chromosomes 9–22 and chromosome X were assigned to the test set.…”

Section: Methodsmentioning

confidence: 99%

“…A splicing reporter mini‐gene assay is an experimental strategy to systematically evaluate the effects of genetic variants on splicing of a certain exon. Recently, a high‐throughput reporter system called Vex‐seq was developed to determine the splicing impact of exonic and intronic variants for the same exon simultaneously (Adamson, Zhan, & Graveley, ). Vex‐seq compares the percent spliced‐in (PSI or

normalΨ

)—a metric representing the fraction of transcripts harboring a given exon—between constructs containing a reference sequence and constructs containing a particular variant.…”

Section: Introductionmentioning

confidence: 99%

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Using secondary structure to predict the effects of genetic variants on alternative splicing

Wang

2019

Human Mutation

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Accurate interpretation of genomic variants that alter RNA splicing is critical to precision medicine. We present a computational framework, Prediction of variant Effect on Percent Spliced In (PEPSI), that predicts the splicing impact of coding and noncoding variants for the Fifth Critical Assessment of Genome Interpretation (CAGI5) “Vex‐seq” challenge. PEPSI is a random forest regression model trained on multiple layers of features associated with sequence conservation and regulatory sequence elements. Compared to other splicing defect prediction tools from the literature, our framework integrates secondary structure information in predicting variants that disrupt splicing regulatory elements (SREs). We applied our model to classify splice‐disrupting variants among 2,094 single‐nucleotide polymorphisms from the Exome Aggregation Consortium using model‐predicted changes in percent spliced in (ΔPSI) associated with tested variants. Benchmarking our model against widely used state‐of‐the‐art tools, we demonstrate that PEPSI achieves comparable performance in terms of sensitivity and precision. Moreover, we also show that using secondary structure context can help resolve several cases where changes in the counts of SREs do not correspond with the directionality of ΔPSI measured for tested variants.

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“…The Vex‐seq experiment measured the

Δ Ψ

Section: Methodsmentioning

confidence: 99%

normalΨ

)—a metric representing the fraction of transcripts harboring a given exon—between constructs containing a reference sequence and constructs containing a particular variant.…”

Section: Introductionmentioning

confidence: 99%

Using secondary structure to predict the effects of genetic variants on alternative splicing

Wang

2019

Human Mutation

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“…Even though the approach of learning the splicing code from reference sequence was successful, the model may suffer from evolutionary confounding and fail to learn causal features. To address this issue, large-scale perturbation assays, such as massively parallel reporter assay (MPRA) and saturation mutagenesis screens, have been developed (Barash et al, 2010;Xiong et al, 2015;Rosenberg, Patwardhan, Shendure, & Seelig, 2015;Adamson, Zhan, & Graveley, 2018;Ke et al, 2018). In particular, Rosenberg, Patwardhan, Shendure, and Seelig (2015) probed millions of exonic and intronic random sequences to test their impact on splicing.…”

Section: Introductionmentioning

confidence: 99%

CAGI 5 splicing challenge: Improved exon skipping and intron retention predictions with MMSplice

et al. 2019

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Pathogenic genetic variants often primarily affect splicing. However, it remains difficult to quantitatively predict whether and how genetic variants affect splicing. In 2018, the fifth edition of the Critical Assessment of Genome Interpretation proposed two splicing prediction challenges based on experimental perturbation assays: Vex‐seq, assessing exon skipping, and MaPSy, assessing splicing efficiency. We developed a modular modeling framework, MMSplice, the performance of which was among the best on both challenges. Here we provide insights into the modeling assumptions of MMSplice and its individual modules. We furthermore illustrate how MMSplice can be applied in practice for individual genome interpretation, using the MMSplice VEP plugin and the Kipoi variant interpretation plugin, which are directly applicable to VCF files.

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“…Using highly stringent criteria, this study showed that 10% of exonic mutations altered splicing (Soemedi et al, 2017b). The ability to evaluate variants for defective splicing is beginning to emerge as an achievable goal with the advent of massively parallel reporter assays (MPRAs) and high-throughput screens (Adamson, Zhan, & Graveley, 2018;Ke et al, 2018;Soemedi et al, 2017b). Computational methods aimed at leveraging MPRAs and high-throughput assay data have led to improved predictive models for classifying splicing variants that have not been empirically verified (Bretschneider, Gandhi, Deshwar, Zuberi, & Frey, 2018;Desmet et al, 2009;Fairbrother, Yeh, Sharp, & Burge, 2002;Mort et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Future directions for high‐throughput splicing assays in precision medicine

et al. 2019

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Classification of variants of unknown significance is a challenging technical problem in clinical genetics. As up to one‐third of disease‐causing mutations are thought to affect pre‐mRNA splicing, it is important to accurately classify splicing mutations in patient sequencing data. Several consortia and healthcare systems have conducted large‐scale patient sequencing studies, which discover novel variants faster than they can be classified. Here, we compare the advantages and limitations of several high‐throughput splicing assays aimed at mitigating this bottleneck, and describe a data set of ~5,000 variants that we analyzed using our Massively Parallel Splicing Assay (MaPSy). The Critical Assessment of Genome Interpretation group (CAGI) organized a challenge, in which participants submitted machine learning models to predict the splicing effects of variants in this data set. We discuss the winning submission of the challenge (MMSplice) which outperformed existing software. Finally, we highlight methods to overcome the limitations of MaPSy and similar assays, such as tissue‐specific splicing, the effect of surrounding sequence context, classifying intronic variants, synthesizing large exons, and amplifying complex libraries of minigene species. Further development of these assays will greatly benefit the field of clinical genetics, which lack high‐throughput methods for variant interpretation.

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Vex-seq: high-throughput identification of the impact of genetic variation on pre-mRNA splicing efficiency

Cited by 76 publications

References 31 publications

Using secondary structure to predict the effects of genetic variants on alternative splicing

Using secondary structure to predict the effects of genetic variants on alternative splicing

CAGI 5 splicing challenge: Improved exon skipping and intron retention predictions with MMSplice

Future directions for high‐throughput splicing assays in precision medicine

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