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 the proteome and is 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.

University of Central Florida researchers have developed AS-Quant, (Alternative Splicing Quantitation), a robust program to identify alternative splicing events from RNA-seq data. The researchers then extended AS-Quant to visualize the splicing events with short-read coverage plots along with complete gene annotation. The tool works in three major steps: (i) calculate the read coverage of the potential spliced exons and the corresponding gene; (ii) categorize the events into five different categories according to the annotation, and assess the significance of the events between two biological conditions; (iii) generate the short reads coverage plot for user specified splicing events. Extensive experiments on simulated and real datasets demonstrate that AS-Quant outperforms the other three widely used baselines, SUPPA2, rMATS, and diffSplice for detecting alternative splicing events. Moreover, the significant alternative splicing events identified by AS-Quant between two biological contexts were validated by RT-PCR experiment.

Workflow of AS-Quant

Starting with aligned RNA-seq bam files, AS-Quant consists of three steps (i) read coverage estimation, (ii) splicing events categorization and assessment, (iii) visualization.

Availability – Source code and a comprehensive user’s manual are freely available at https://github.com/CompbioLabUCF/AS-Quant