1 Motivation

RNA sequencing (RNA-seq) is an accurate and versatile method for transcriptome-wide gene expression analysis. Despite its widespread use, RNA-seq data often remain under-utilized and under-analyzed. Lack of computational skills or resources are often cited by end users as a primary obstacle. Although a number of free and commercial programs have been developed most of them focus on analytical procedures and therefore require substantial time commitment from a non-expert. Typically extensive involvement of a bioinformatician is required to perform routine data mining and visualization which increases the waiting time for the analyses, introduces unpredictability in analytic workflow, and decreases productivity of both experimental and computational biologists involved. Efforts to automate the routine analyses resulted in creating multiple computational pipelines that perform initial steps of RNA-seq data processing and summarization with little human involvement. However, these initial data processing steps do not return data that is easily interpretable by users with minimal computational experience.

To simplify end-user RNA-seq data interpretation we created rnaseqDRaMA - an interactive reporting system based on R-Shiny that provides a user-friendly web interface for data exploration and visualization. Our primary goal for the HSS Genomics Research Center pipeline was to create an integrated workflow starting from unprocessed fastq files through read alignment, gene-based read counting, experiment-level data summarization, and differential expression for differentially expressed genes. Our secondary goal was to minimize the time researchers had to wait to begin looking at and understanding their data.