aTAP: automated transcriptome analysis platform for processing RNA-seq data by de novo assembly

• Published in: Heliyon 2022-08, Vol.8 (8), p.e10255-e10255, Article e10255
• Main Authors: Surachat, Komwit, Taylor, Todd Duane, Wattanamatiphot, Wanicbut, Sukpisit, Sukgamon, Jeenkeawpiam, Kongpop
• Format: Article
• Language: English
• Subjects: Bioinformatics workflow; Differentially expressed genes; Gene expression profile; RNA-seq; Transcriptome
• ISSN: 2405-8440; 2405-8440
• DOI: 10.1016/j.heliyon.2022.e10255

RNA-seq is a sequencing technique that uses next-generation sequencing (NGS) to explore and study the entire transcriptome of a biological sample. NGS-based analyses are mostly performed via command-line interfaces, which is an obstacle for molecular biologists and researchers. Therefore, the higher throughputs from NGS can only be accessed with the help of bioinformatics and computer science expertise. As the cost of sequencing is continuously falling, the use of RNA-seq seems certain to increase. To minimize the problems encountered by biologists and researchers in RNA-seq data analysis, we propose an automated platform with a web application that integrates various bioinformatics pipelines. The platform is intended to enable academic users to more easily analyze transcriptome datasets. Our automated Transcriptome Analysis Platform (aTAP) offers comprehensive bioinformatics workflows, including quality control of raw reads, trimming of low-quality reads, de novo transcriptome assembly, transcript expression quantification, differential expression analysis, and transcript annotation. aTAP has a user-friendly graphical interface, allowing researchers to interact with and visualize results in the web browser. This project offers an alternative way to analyze transcriptome data, by integrating efficient and well-known tools, that is simpler and more accessible to research communities. aTAP is freely available to academic users at https://atap.psu.ac.th/. Transcriptome, RNA-seq, Bioinformatics workflow, Differentially expressed genes, Gene expression profile.