Biology subjects

Chang, J.-W.

Publications and source records attributed to Chang, J.-W..

3 recordsLinked to original sources

bioRxiv2020

mTOR-driven widespread exon skipping renders multifaceted gene regulation and proteome complexity

The mammalian target of rapamycin (mTOR) pathway is crucial in cell proliferation. Previously, we reported transcriptome-wide 3’-untranslated region (UTR) shortening by alternative polyadenylation upon mTOR activation and its impact on the proteome. Here, we further interrogated the mTOR-activated transcriptome and found that hyperactivation of mTOR promotes transcriptome-wide exon-skipping/exclusion, producing short isoform transcripts from genes. This widespread exon skipping confers multifarious regulations in the mTOR-controlled functional proteomics: alternative splicing (AS) in the 5’-UTR controls translation efficiency while AS in coding regions widely affects the protein length and functional domains. They also alter the half-life of proteins and affect the regulatory post-translational modifications. Among the RNA processing factors differentially regulated by mTOR signaling, we found that SRSF3 mechanistically facilitates exon skipping in the mTOR-activated transcriptome. This study reveals a role of mTOR in AS regulation and demonstrates that widespread AS is a multifaceted modulator of the mTOR-regulated functional proteome.Competing Interest StatementThe authors have declared no competing interest.View Full Text

biochemistry↗

bioRxiv2020

APA-Scan: Detection and Visualization of 3'-UTR APA with RNA-seq and 3'-end-seq Data

BackgroundThe eukaryotic genome is capable of producing multiple isoforms from a gene by alternative polyadenylation (APA) during pre-mRNA processing. APA in the 3-untranslated region (3-UTR) of mRNA produces transcripts with shorter or longer 3-UTR. Often, 3-UTR serves as a binding platform for microRNAs and RNA-binding proteins, which affect the fate of the mRNA transcript. Thus, 3-UTR APA is known to modulate translation and provides a mean to regulate gene expression at the post-transcriptional level. Current bioinformatics pipelines have limited capability in profiling 3-UTR APA events due to incomplete annotations and a low-resolution analyzing power: widely available bioinformatics pipelines do not reference actionable polyadenylation (cleavage) sites but simulate 3-UTR APA only using RNA-seq read coverage, causing false positive identifications. To overcome these limitations, we developed APA-Scan, a robust program that identifies 3-UTR APA events and visualizes the RNA-seq short-read coverage with gene annotations. MethodsAPA-Scan utilizes either predicted or experimentally validated actionable polyadenylation signals as a reference for polyadenylation sites and calculates the quantity of long and short 3-UTR transcripts in the RNA-seq data. APA-Scan works in three major steps: (i) calculate the read coverage of the 3-UTR regions of genes; (ii) identify the potential APA sites and evaluate the significance of the events among two biological conditions; (iii) graphical representation of user specific event with 3-UTR annotation and read coverage on the 3-UTR regions. APA-Scan is implemented in Python3. Source code and a comprehensive users manual are freely available at https://github.com/compbiolabucf/APA-Scan. ResultAPA-Scan was applied to both simulated and real RNA-seq datasets and compared with two widely used baselines DaPars and APAtrap. In simulation APA-Scan significantly improved the accuracy of 3-UTR APA identification compared to the other baselines. The performance of APA-Scan was also validated by 3-end-seq data and qPCR on mouse embryonic fibroblast cells. The experiments confirm that APA-Scan can detect unannotated 3 -UTR APA events and improve genome annotation. ConclusionAPA-Scan is a comprehensive computational pipeline to detect transcriptome-wide 3-UTR APA events. The pipeline integrates both RNA-seq and 3-end-seq data information and can efficiently identify the significant events with a high-resolution short reads coverage plots.

bioinformatics↗

bioRxiv2020

AS-Quant: Detection and Visualization of Alternative Splicing Events with RNA-seq Data

A simplistic understanding of the central dogma falls short in correlating the number of genes in the genome to the number of proteins in the proteome. Post-transcriptional alternative splicing contributes to the complexity of proteome and are critical in understanding gene expression. mRNA-sequencing (RNA-seq) has been widely used to study the transcriptome and provides opportunity to detect alternative splicing events among different biological conditions. Despite the popularity of studying transcriptome variants with RNA-seq, few efficient and user-friendly bioinformatics tools have been developed for the genome-wide detection and visualization of alternative splicing events. We have developed AS-Quant (Alternative Splicing Quantitation), a robust program to identify alternative splicing events and visualize the short-read coverage with gene annotations. AS-Quant works in three steps: (i) calculate the read coverage of the potential splicing exons and the corresponding gene; (ii) categorize the splicing events into five different types based on annotation, and assess the significance of the events between two biological conditions; (iii) generate the short reads coverage plot with a complete gene annotation for user specified splicing events. To evaluate the performance, two significant alternative splicing events identified by AS-Quant between two biological contexts were validated by RT-PCR. ImplementationAS-Quant is implemented in Python. Source code and a comprehensive users manual are freely available at https://github.com/CompbioLabUCF/AS-Quant

bioinformatics↗