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

By Naima Ahmed Fahmi, Hsin-Sung Yeh, Jae-Woong Chang, Heba Nassereddeen, Deliang Fan, Jeongsik Yong, Wei Zhang

Posted 16 Feb 2020
bioRxiv DOI: 10.1101/2020.02.15.950287

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 ( A lternative S plicing Quant itation), 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. Implementation AS-Quant is implemented in Python. Source code and a comprehensive user’s manual are freely available at <https://github.com/CompbioLabUCF/AS-Quant>

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