RNA alternative splicing prediction with discrete compositional energy network

Chan, Alvin; Korsakova, Anna; Ong, Yew-Soon; Winnerdy, Fernaldo Richtia; Lim, Kah Wai; Phan, Anh Tuân

doi:10.1145/3450439.3451857

Cited by 3 publications

(3 citation statements)

References 42 publications

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“…epiG4NN is a neural network model that consists of stacks of convolutional layers with skip connections for better training convergence (58). Convolutional networks (CNNs) are a class of deep learning methods that achieved significant breakthroughs in the genomic predictions (64–67). Convolutional kernels slide along the inputs and extract input features, passed on to the next layers.…”

Section: Resultsmentioning

confidence: 99%

Prediction of G4 formation in live cells with epigenetic data: a deep learning approach

Korsakova

Phan

2023

Preprint

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G-quadruplexes (G4s) are secondary structures abundant in DNA that may play regulatory roles in cells. Despite the ubiquity of the putative G-quadruplex sequences (PQS) in the human genome, only a small fraction forms secondary structures in cells. Folded G4, histone methylation and chromatin accessibility are all parts of the complex cis regulatory landscape. We propose an approach for G4 formation prediction in cells that incorporates epigenetic and chromatin accessibility data. The novel approach termed epiG4NN efficiently predicts cell-specific G4 formation in live cells based on a local epigenomic snapshot. Our architecture confirms the close relationship between H3K4me3 histone methylation, chromatin accessibility and G4 structure formation. Trained on A549 cell data, epiG4NN was then able to predict G4x formation in HEK293T and K562 cell lines. We observe the dependency of model performance with different epigenetic features on the underlying experimental condition of G4 detection. We expect that this approach will contribute to the systematic understanding of correlations between structural and epigenomic feature landscape.

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Section: Resultsmentioning

confidence: 99%

Prediction of G4 formation in live cells with epigenetic data: a deep learning approach

Korsakova

Phan

2023

Preprint

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“…Recently, deep learning methods have been extensively used to learn and predict key events in cellular processing. These include DNA and RNA structure formation [78][79][80] , RNA splicing 81,82 , protein folding 83,84 , gene expression 85 . Recent models focus on increasing the receptive windows of the first layers to extract long-range interactions in the input sequence 81,85 .…”

Section: Integration Of Contextual Data For Genomic Learning Modelsmentioning

confidence: 99%

“…Additionally, novel architecture types and features, such as transformer models 83,85 and discrete encoded states 82,83,86 applied to biological tasks, improve both the predictive power and the physical interpretability of the models.…”

Section: Integration Of Contextual Data For Genomic Learning Modelsmentioning

confidence: 99%

DNA/RNA structure and processing in vitro and in cells: from probing to prediction

Korsakova¹

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Human DNA sequence determines the cellular fate through transcription to RNA and translation to proteins. DNA and RNA undergo extensive processing in the cells based on the sequence and cellular state, where alternative splicing in particular determines RNA isoform choice. In the recent years, in addition to the sequence of nucleic acid, its structure and epigenetic landscape have been shown to play important roles in cellular functions. For example, DNA and RNA G-quadruplex (G4) structures were found to affect oncogene expression and to be attractive therapeutic targets. This thesis mainly focuses on the DNA and RNA G4 structure formation and RNA splicing in cellular contexts.First, I analyse the formation of irregular G4 forming motifs using reported experimental data on DNA G4 in cells. I find possible correlations of DNA G4 formation with contextual epigenetic features using neural networks and propose a deep learning-based method for G4 prediction in cells. Motivated by the scarcity of RNA G4 probing methods, I additionally propose a method for detection of RNA G-quadruplexes in long RNA with direct nanopore sequencing. Contextual machine learning is further applied to predict alternative RNA splicing in cells using RNA-binding protein (RBP) levels. In summary, I have developed deep learning methods for prediction of G4 structure formation and RNA splicing in cells, which will help to advance our understanding of DNA/RNA structure and processing in different cellular contexts.

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Prediction of G4 formation in live cells with epigenetic data: a deep learning approach

Korsakova,

Phan

2023

NAR Genomics and Bioinformatics

Self Cite

View full text Add to dashboard Cite

G-quadruplexes (G4s) are secondary structures abundant in DNA that may play regulatory roles in cells. Despite the ubiquity of the putative G-quadruplex-forming sequences (PQS) in the human genome, only a small fraction forms G4 structures in cells. Folded G4, histone methylation and chromatin accessibility are all parts of the complex cis regulatory landscape. We propose an approach for prediction of G4 formation in cells that incorporates epigenetic and chromatin accessibility data. The novel approach termed epiG4NN efficiently predicts cell-specific G4 formation in live cells based on a local epigenomic snapshot. Our results confirm the close relationship between H3K4me3 histone methylation, chromatin accessibility and G4 structure formation. Trained on A549 cell data, epiG4NN was then able to predict G4 formation in HEK293T and K562 cell lines. We observe the dependency of model performance with different epigenetic features on the underlying experimental condition of G4 detection. We expect that this approach will contribute to the systematic understanding of correlations between structural and epigenomic feature landscape.

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RNA alternative splicing prediction with discrete compositional energy network

Cited by 3 publications

References 42 publications

Prediction of G4 formation in live cells with epigenetic data: a deep learning approach

Prediction of G4 formation in live cells with epigenetic data: a deep learning approach

DNA/RNA structure and processing in vitro and in cells: from probing to prediction

Prediction of G4 formation in live cells with epigenetic data: a deep learning approach

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