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MET Exon 14 Skipping: A Case Study for the Detection of Genetic Variants in Cancer Driver Genes by Deep Learning
- Source :
- International Journal of Molecular Sciences, Volume 22, Issue 8, International Journal of Molecular Sciences, Vol 22, Iss 4217, p 4217 (2021)
- Publication Year :
- 2021
-
Abstract
- Background: Disruption of alternative splicing (AS) is frequently observed in cancer and might represent an important signature for tumor progression and therapy. Exon skipping (ES) represents one of the most frequent AS events, and in non-small cell lung cancer (NSCLC) MET exon 14 skipping was shown to be targetable. Methods: We constructed neural networks (NN/CNN) specifically designed to detect MET exon 14 skipping events using RNAseq data. Furthermore, for discovery purposes we also developed a sparsely connected autoencoder to identify uncharacterized MET isoforms. Results: The neural networks had a Met exon 14 skipping detection rate greater than 94% when tested on a manually curated set of 690 TCGA bronchus and lung samples. When globally applied to 2605 TCGA samples, we observed that the majority of false positives was characterized by a blurry coverage of exon 14, but interestingly they share a common coverage peak in the second intron and we speculate that this event could be the transcription signature of a LINE1 (Long Interspersed Nuclear Element 1)-MET (Mesenchymal Epithelial Transition receptor tyrosine kinase) fusion. Conclusions: Taken together, our results indicate that neural networks can be an effective tool to provide a quick classification of pathological transcription events, and sparsely connected autoencoders could represent the basis for the development of an effective discovery tool.
- Subjects :
- Neural Networks
QH301-705.5
Exon
Computational biology
Article
Catalysis
Receptor tyrosine kinase
Inorganic Chemistry
Computer
Deep Learning
medicine
biochemistry
Humans
Biology (General)
Physical and Theoretical Chemistry
QD1-999
Molecular Biology
Genetic variant
Spectroscopy
biology
genetic variants
Organic Chemistry
Alternative splicing
Intron
Cancer
Genetic Variation
General Medicine
Exons
medicine.disease
Exon skipping
Neural network
Computer Science Applications
Long interspersed nuclear element
Chemistry
Tumor progression
Deep learning
Genetic variants
MET
Neural Networks, Computer
biology.protein
Human
Subjects
Details
- ISSN :
- 14220067
- Volume :
- 22
- Issue :
- 8
- Database :
- OpenAIRE
- Journal :
- International journal of molecular sciences
- Accession number :
- edsair.doi.dedup.....060ff7d685743f48be64a6cd274f3926