OneStopRNAseq: A Web Application for Comprehensive and Efficient Analyses of RNA-Seq Data.

Rui Li,Lihua Julie Zhu,Haibo Liu,Michael R Green,Kai Hu

doi:10.3390/genes11101165

Abstract

Over the past decade, a large amount of RNA sequencing (RNA-seq) data were deposited in public repositories, and more are being produced at an unprecedented rate. However, there are few open source tools with point-and-click interfaces that are versatile and offer streamlined comprehensive analysis of RNA-seq datasets. To maximize the capitalization of these vast public resources and facilitate the analysis of RNA-seq data by biologists, we developed a web application called OneStopRNAseq for the one-stop analysis of RNA-seq data. OneStopRNAseq has user-friendly interfaces and offers workflows for common types of RNA-seq data analyses, such as comprehensive data-quality control, differential analysis of gene expression, exon usage, alternative splicing, transposable element expression, allele-specific gene expression quantification, and gene set enrichment analysis. Users only need to select the desired analyses and genome build, and provide a Gene Expression Omnibus (GEO) accession number or Dropbox links to sequence files, alignment files, gene-expression-count tables, or rank files with the corresponding metadata. Our pipeline facilitates the comprehensive and efficient analysis of private and public RNA-seq data.

Highlights

OneStopRNAseq is an easy-to-use web application designed for the comprehensive analyses of RNA sequencing (RNA-seq) data for both biologists and bioinformaticians
In order to simplify and streamline RNA-seq analyses, we integrated a set of widely used analysis components into our pipeline, including differential gene expression (DGE), differential exon usage (DEU), differential alternative splicing (DAS), gene-set enrichment analysis (GSEA), and differential transposable element expression (DTE) analyses, and allele-specific gene expression (ASE) quantification
RNA-seq has become a widely used technology in many fields, including genomics and clinical diagnostics, but only differential gene-expression analysis has been performed for the majority of RNA-seq experiments, partially due to the lack of comprehensive RNA-seq analysis pipelines

Summary

Introduction

The transcriptome is composed of diverse species of RNA, including protein-coding messengerRNA (mRNA) and noncoding RNA (ncRNA), and both are transcribed and expressed in a broad range of abundance in a given cell type [1]. mRNA is the essential intermediate in gene expression, bridging the genome to protein function [2]; ncRNAs can regulate gene expression by modulating chromatin formation and regulation, translation, macromolecule interactions, or even catalytic processes [3,4,5].The transcriptome dynamically changes in response to internal and external cues; it can be used as a proxy for gene-transcription activities [6,7], and abundance of gene end products on the bulk level under steady-state conditions [8,9,10]. RNA–seq has been widely used to profile the changes of transcriptomes between conditions to understand the cause and effect of biological processes through differential gene-/transcript-/exon-expression analysis [19,20]. Beyond differential gene-expression analysis, RNA–seq can be applied to achieve more detailed transcriptome characterization, including the analysis of alternative splicing (AS) and transposable-element (TE) expression, RNA modification and editing, and the identification of novel transcripts. RNA–seq has proven an ideal approach for novel transcriptome assembly, which is especially helpful for genome annotation for non-model organisms As it is sequence-based, RNA–seq has proven helpful in identifying expression genetic variants, for expression quantitative trait loci (eQTL) analysis, and even clinical diagnosis [19,21,22,23,24,25]. RNA–seq has been widely used in many fields, from basic research to clinical applications [32]

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