Shiau, C.-K. | Search bioRxiv

bioRxiv2026

Fusion gene discovery in single cells from high throughput long read single cell transcriptomes

High throughput long-read single cell RNA sequencing allows for fusion gene discovery in thousands of cells in parallel, which is however constrained by the lack of robust computational tools. Here, we develop LongFUSE that employs the XOR logic operation and stringent filtering criteria for the accurate detection of cell-specific fusion genes and their splicing isoforms. LongFUSE outperforms existing tools in both simulation and real data, demonstrating high accuracy and robustness.

bioinformatics↗

bioRxiv2023

Predicting splicing patterns from the transcription factor binding sites in the promoter with deep learning

BackgroundAlternative splicing is a crucial mechanism of post-transcriptional modification responsible for the transcriptome plasticity and proteome diversity of a metazoan cell. Although many splicing regulations around the exon/intron regions have been discovered, the relationship between promoter-bound transcription factors and the downstream alternative splicing remains largely unexplored. ResultsIn this study, we present computational approaches to decipher the regulation relationship connecting the promoter-bound transcription factor binding sites (TFBSs) and the splicing patterns. We curated a fine data set, including DNase I hypersensitive sites sequencing and transcriptome in fifteen human tissues from ENCODE. Specifically, we proposed different representations of TF binding context and splicing patterns to tackle the associations between the promoter and downstream splicing events. Our results demonstrated that the convolutional neural network (CNN) models learned from the TF binding changes in the promoter to predict the splicing pattern changes. Furthermore, through an in silico perturbation-based analysis of the CNN models, we identified several TFs that considerably reduced the model performance of splicing prediction. ConclusionIn conclusion, our finding highlights the potential role of promoter-bound TFBSs in influencing the regulation of downstream splicing patterns and provides insights for discovering alternative splicing regulations.

bioinformatics↗