Improved identification of conserved cassette exons using Bayesian networks

Predicting the impact of single nucleotide variants on splicing via sequence-based deep neural networks and genomic features

Single nucleotide mutations in exonic regions can significantly affect gene function through a disruption of splicing, and various computational methods have been developed to predict the splicing-related effects of a single nucleotide mutation. We implemented a new method using ensemble learning that combines two types of predictive models: (a) base sequence-based deep neural networks (DNNs) and (b) machine learning models based on genomic attributes. This method was applied to the Massively Parallel Splicing Assay challenge of the Fifth Critical Assessment of Genome Interpretation, in which challenge participants predicted various experimentally-defined exonic splicing mutations, and achieved a promising result. We successfully revealed that combining different predictive models based upon the stacked generalization method led to significant improvement in prediction performance. In addition, whereas most of the genomic features adopted in constructing machine learning models were previously reported, feature values generated with DSSP, a DNN-based splice site prediction tool, were novel and helpful for the prediction. Learning the sequence patterns associated with normal splicing and the change in splicing site probabilities caused by a mutation was presumed to be helpful in predicting splicing disruption.

Recognition of alternatively spliced cassette exons based on a hybrid model

Alternative splicing (AS) is an important mechanism of gene regulation that contributes to protein diversity. It is of great significance to recognize different kinds of AS accurately so as to understand the mechanism of gene regulation. Many in silico methods have been applied to detecting AS with vast features, but the result is far from satisfactory. In this paper, we used the features proven to be useful in recognizing AS in previous literature and proposed a hybrid method combining Gene Expression Programming (GEP) and Random Forests (RF) to classify the constitutive exons and cassette exons which is the most common AS phenomenon. GEP will firstly make prediction to the samples of strong signal, and the other samples of weak signal will be distinguished with a more complex classifier based on RF. The experiment result indicates that this method can highly improve the recognition level in this issue.

MutPred Splice: machine learning-based prediction of exonic variants that disrupt splicing

We have developed a novel machine-learning approach, MutPred Splice, for the identification of coding region substitutions that disrupt pre-mRNA splicing. Applying MutPred Splice to human disease-causing exonic mutations suggests that 16% of mutations causing inherited disease and 10 to 14% of somatic mutations in cancer may disrupt pre-mRNA splicing. For inherited disease, the main mechanism responsible for the splicing defect is splice site loss, whereas for cancer the predominant mechanism of splicing disruption is predicted to be exon skipping via loss of exonic splicing enhancers or gain of exonic splicing silencer elements. MutPred Splice is available at http://mutdb.org/mutpredsplice.

Genetic variants in the Runt-related transcription factor 3 gene contribute to gastric cancer risk in a Chinese population

Runt-related transcription factor 3 (RUNX3) is a well known gene for its functions in gastric cancer suppression, but the effect of its genetic variations on the risk of gastric cancer remains unclear. In this study, ten tagging single nucleotide polymorphisms (tSNPs) of the RUNX3 gene were selected and genotyped in a hospital-based case-control study of 312 gastric cancer patients and 329 cancer-free controls in a Chinese population. In the single-locus analysis, three RUNX3 intronic tSNPs associated with significantly increased risk of gastric cancer were observed: the SNP3 rs11249206 CC genotype (adjusted odds ratio [OR] = 1.75, 95% confidence interval [CI] = 1.03-2.99), compared with the TT genotype; the SNP7 rs760805 AA genotype (adjusted OR = 1.82, 95% CI = 1.14-2.92), compared with the TT genotype; and the SNP8 rs2236852 GG genotype (adjusted OR = 1.69, 95% CI = 1.05-2.72), compared with the AA genotype. In the combined analyses of these three tSNPs, we found that the combined genotypes with four to six variant (risk) alleles (i.e. SNP3 C, SNP7 A, and SNP8 G alleles) were associated with an increased risk of gastric cancer compared with those with one to three variant (risk) alleles (adjusted OR = 2.00, 95% CI = 1.41-2.85), and this increased risk was more pronounced among subgroups of age > or =65 years, never smokers, and never drinkers. However, no significant association was observed in the clinicopathological features analyses. In conclusion, the RUNX3 genetic variants may modulate the risk of gastric cancer in a Chinese population. Further larger and functional studies are warranted to validate the findings.