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Alternative splicing is widely recognized for playing roles in regulating genes and creating gene diversity. Consequently the identification and quantification of differentially spliced transcripts are pivotal for transcriptome analysis. However, how these diversified isoforms are spliced during genomic transcription and protein expression and what biological factors might influence the regulation of this are still required for further exploration. The advances in next-generation sequencing of messenger RNA (RNA-seq) have enabled us to survey gene expression and splicing more accurately. We have introduced a novel computational method, graph-based exon-skipping scanner (GESS), for de novo detection of skipping event sites from raw RNA-seq reads without prior knowledge of gene annotations, as well as for determining the dominant isoform generated from such sites. We have applied our method to publicly available RNA-seq data in GM12878 and K562 cells from the ENCODE consortium, and integrated other sequencing-based genomic data to investigate the impact of splicing activities, transcription factors (TFs) and epigenetic histone modifications on splicing outcomes. In a separate study, we also apply this algorithm in prostate cancer in The Cancer Genomics Atlas (TCGA) for de novo skipping event discovery to the understanding of abnormal splicing in each patient and to identify potential markers for prediction and progression of diseases.

Computational Methods and Correlation of Exon-skipping Events with Splicing, Transcription, and Epigenetic Factors