Predicting the change of exon splicing caused by genetic variant using support vector regression

Abstract

Alternative splicing can be disrupted by genetic variants that are related to diseases like cancers. Discovering the influence of genetic variations on the alternative splicing will improve the understanding of the pathogenesis of variants. Here, we developed a new approach, PredPSI‐SVR to predict the impact of variants on exon skipping events by using the support vector regression. From the sequence of a particular exon and its flanking regions, 42 comprehensive features related to splicing events were extracted. By using a greedy feature selection algorithm, we found eight features contributing most to the prediction. The trained model achieved a Pearson correlation coefficient (PCC) of 0.570 in the 10‐fold cross‐validation based on the training data set provided by the “vex‐seq” challenge of the 5th Critical Assessment of Genome Interpretation. In the blind test also held by the challenge, our prediction ranked the 2nd with a PCC of 0.566 that demonstrates the robustness of our method. A further test indicated that the PredPSI‐SVR is helpful in prioritizing deleterious synonymous mutations. The method is available on https://github.com/chenkenbio/PredPSI-SVR.

Reports from the fifth edition of CAGI: The Critical Assessment of Genome Interpretation

“…Previously, CAGI has had just one small splicing challenge [https://genomeinterpretation.org/content/Splicing-2012]. CAGI5 included two full‐scale splicing challenges (Mount et al, ) and these have resulted in five papers from participants (Chen, Lu, Zhao, & Yang, ; Cheng, Çelik, Nguyen, Avsec, & Gagneur, ; Gotea, Margolin, & Elnitski, ; Naito, ; Wang, Wang, & Hu, ). The issue also contains an overview paper from one of the splicing data providers (Rhine et al, ).…”

Leveraging AI Advances and Online Tools for Structure‐Based Variant Analysis

“…For example, aggregation‐enhancing variants may also inactivate associated proteins by increasing their degradation or by sequestering them in nonfunctional aggregates (Anderson et al., 2021). Mutations may also induce changes in exon usage (Chen et al., 2019). Even synonymous mutations can have negative effects on protein function by altering codon usage, which influences the speed of translation and can potentially lead to misfolding (Liu et al., 2021).…”

“…For example, aggregation-enhancing variants may also inactivate associated proteins by increasing their degradation or by sequestering them in nonfunctional aggregates (Anderson et al, 2021). Mutations may also induce changes in exon usage (Chen et al, 2019).…”

Intra‐individual association between C‐reactive protein and insulin administration in postoperative lumbar spinal canal stenosis patients: A retrospective cohort study

“…Using the training dataset, we developed the linear mixed effects model by full information maximum likelihood. By calculating the Pearson’s correlation coefficient between the predicted and actual amounts of ΔInsulin using fivefold cross‐validation, we determined to add each variable or random effect. During cross‐validation, the model was evaluated using the data of patients not included in model development, similar to leave‐subject‐out cross‐validation.…”