SEQUIN is an R/Shiny framework for rapid and reproducible analysis of RNA-seq data.

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• RNA sequencing (RNA-seq) is a powerful technology for transcriptome analysis.
• Analyzing complex RNA-seq datasets, especially single-cell data, requires specialized bioinformatics tools.
• Existing tools may lack comprehensive features or user-friendliness for interdisciplinary scientists.

Purpose of the Study:

• To develop a free, web-based application named SEQUIN for fast and intuitive RNA sequencing data analysis.
• To integrate essential analysis functions including data uploading, quality control, gene set enrichment, data visualization, and differential gene expression analysis.
• To introduce the iPSC Profiler for assessing pluripotency and differentiation states of cells.

Main Methods:

• Development of a web-based application (SEQUIN) with integrated analysis modules.
• Implementation of standard bioinformatics pipelines for RNA-seq data processing and analysis.
• Comparative benchmarking against existing commercial and non-commercial bioinformatics tools.

Main Results:

• SEQUIN provides a user-friendly interface for comprehensive RNA sequencing data analysis.
• The application includes modules for quality control, gene set enrichment, visualization, and differential gene expression.
• The iPSC Profiler tool facilitates the comparison of cell types based on gene expression modules.
• Benchmarking revealed advantages of SEQUIN over other available tools in terms of functionality and usability.

Conclusions:

• SEQUIN democratizes access to advanced RNA sequencing data analysis for a wider scientific community.
• The application enhances the throughput and efficiency of interrogating biological questions using next-generation sequencing data.
• SEQUIN empowers scientists to conduct and present transcriptome analyses with state-of-the-art statistical methods.