Dataset Information

Using machine learning to detect the differential usage of novel gene isoforms.

ABSTRACT:

Background

Differential isoform usage is an important driver of inter-individual phenotypic diversity and is linked to various diseases and traits. However, accurately detecting the differential usage of different gene transcripts between groups can be difficult, in particular in less well annotated genomes where the spectrum of transcript isoforms is largely unknown.

Results

We investigated whether machine learning approaches can detect differential isoform usage based purely on the distribution of reads across a gene region. We illustrate that gradient boosting and elastic net approaches can successfully identify large numbers of genes showing potential differential isoform usage between Europeans and Africans, that are enriched among relevant biological pathways and significantly overlap those identified by previous approaches. We demonstrate that diversity at the 3' and 5' ends of genes are primary drivers of these differences between populations.

Conclusion

Machine learning methods can effectively detect differential isoform usage from read fraction data, and can provide novel insights into the biological differences between groups.

SUBMITTER: Zhang X

PROVIDER: S-EPMC8764765 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

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Publications

Using machine learning to detect the differential usage of novel gene isoforms.

Zhang Xiaopu X Hassan Musa A MA Prendergast James G D JGD

BMC bioinformatics 20220118 1

<h4>Background</h4>Differential isoform usage is an important driver of inter-individual phenotypic diversity and is linked to various diseases and traits. However, accurately detecting the differential usage of different gene transcripts between groups can be difficult, in particular in less well annotated genomes where the spectrum of transcript isoforms is largely unknown.<h4>Results</h4>We investigated whether machine learning approaches can detect differential isoform usage based purely on ...[more]

PMID: 35042461

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Project description:BACKGROUND: Discovering novel disease genes is still challenging for diseases for which no prior knowledge--such as known disease genes or disease-related pathways--is available. Performing genetic studies frequently results in large lists of candidate genes of which only few can be followed up for further investigation. We have recently developed a computational method for constitutional genetic disorders that identifies the most promising candidate genes by replacing prior knowledge by experimental data of differential gene expression between affected and healthy individuals.To improve the performance of our prioritization strategy, we have extended our previous work by applying different machine learning approaches that identify promising candidate genes by determining whether a gene is surrounded by highly differentially expressed genes in a functional association or protein-protein interaction network. RESULTS: We have proposed three strategies scoring disease candidate genes relying on network-based machine learning approaches, such as kernel ridge regression, heat kernel, and Arnoldi kernel approximation. For comparison purposes, a local measure based on the expression of the direct neighbors is also computed. We have benchmarked these strategies on 40 publicly available knockout experiments in mice, and performance was assessed against results obtained using a standard procedure in genetics that ranks candidate genes based solely on their differential expression levels (Simple Expression Ranking). Our results showed that our four strategies could outperform this standard procedure and that the best results were obtained using the Heat Kernel Diffusion Ranking leading to an average ranking position of 8 out of 100 genes, an AUC value of 92.3% and an error reduction of 52.8% relative to the standard procedure approach which ranked the knockout gene on average at position 17 with an AUC value of 83.7%. CONCLUSION: In this study we could identify promising candidate genes using network based machine learning approaches even if no knowledge is available about the disease or phenotype.

Dataset Information

Using machine learning to detect the differential usage of novel gene isoforms.

Background

Results

Conclusion

Publications

Using machine learning to detect the differential usage of novel gene isoforms.

Similar Datasets

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