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

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.

[1]  W. Huber,et al.  Detecting differential usage of exons from RNA-seq data , 2012, Genome research.

[2]  Sarah Geisler,et al.  RNA in unexpected places: long non-coding RNA functions in diverse cellular contexts , 2013, Nature Reviews Molecular Cell Biology.

[3]  David R. Powell,et al.  From reads to insight: a hitchhiker’s guide to ATAC-seq data analysis , 2020, Genome Biology.

[4]  R. Paro,et al.  Trithorax and Polycomb group-dependent regulation: a tale of opposing activities , 2015, Development.

[5]  E. Marcotte,et al.  Global signatures of protein and mRNA expression levelsw , 2009 .

[6]  In Seok Yang,et al.  Analysis of Whole Transcriptome Sequencing Data: Workflow and Software , 2015, Genomics & informatics.

[7]  B. Williams,et al.  Mapping and quantifying mammalian transcriptomes by RNA-Seq , 2008, Nature Methods.

[8]  Alexander G Williams,et al.  Transposable element expression in tumors is associated with immune infiltration and increased antigenicity , 2019, Nature Communications.

[9]  Måns Magnusson,et al.  MultiQC: summarize analysis results for multiple tools and samples in a single report , 2016, Bioinform..

[10]  Obi L. Griffith,et al.  Informatics for RNA Sequencing: A Web Resource for Analysis on the Cloud , 2015, PLoS Comput. Biol..

[11]  Thomas Shafee,et al.  Transcriptomics technologies , 2017, PLoS Comput. Biol..

[12]  D. C. Hancks,et al.  Active human retrotransposons: variation and disease. , 2012, Current opinion in genetics & development.

[13]  R. Pal,et al.  Send Orders of Reprints at Reprints@benthamscience.net Integrated Analysis of Transcriptomic and Proteomic Data , 2022 .

[14]  Daniel J. Gaffney,et al.  A survey of best practices for RNA-seq data analysis , 2016, Genome Biology.

[15]  A. Mortazavi,et al.  Integrating ChIP-seq with other functional genomics data , 2018, Briefings in functional genomics.

[16]  Mauricio O. Carneiro,et al.  From FastQ Data to High‐Confidence Variant Calls: The Genome Analysis Toolkit Best Practices Pipeline , 2013, Current protocols in bioinformatics.

[17]  I. MacRae,et al.  Regulation of microRNA function in animals , 2018, Nature Reviews Molecular Cell Biology.

[18]  Katharina M. Hembach,et al.  RNA Sequencing Data: Hitchhiker's Guide to Expression Analysis , 2018, Annual Review of Biomedical Data Science.

[19]  Monther Alhamdoosh,et al.  Combining multiple tools outperforms individual methods in gene set enrichment analyses , 2015, bioRxiv.

[20]  Mark D. Robinson,et al.  edgeR: a Bioconductor package for differential expression analysis of digital gene expression data , 2009, Bioinform..

[21]  Ying Jin,et al.  TEtranscripts: a package for including transposable elements in differential expression analysis of RNA-seq datasets , 2015, Bioinform..

[22]  E. Marcotte,et al.  Insights into the regulation of protein abundance from proteomic and transcriptomic analyses , 2012, Nature Reviews Genetics.

[23]  R. Aebersold,et al.  On the Dependency of Cellular Protein Levels on mRNA Abundance , 2016, Cell.

[24]  D. Naquin,et al.  Systematic comparison of small RNA library preparation protocols for next-generation sequencing , 2018, BMC Genomics.

[25]  Onur Yukselen,et al.  DEBrowser: interactive differential expression analysis and visualization tool for count data , 2018, bioRxiv.

[26]  J. Carpten,et al.  Translating RNA sequencing into clinical diagnostics: opportunities and challenges , 2016, Nature Reviews Genetics.

[27]  Jos Kleinjans,et al.  Transcriptomic and metabolomic data integration , 2016, Briefings Bioinform..

[28]  Pablo Tamayo,et al.  Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[29]  A. Weber.  Discovering new biology through RNA-Seq , 2015 .

[30]  J. Knight,et al.  Allele-specific gene expression uncovered. , 2004, Trends in genetics : TIG.

[31]  D. Pookot,et al.  Novel, Selective Inhibitors of USP7 Uncover Multiple Mechanisms of Antitumor Activity In Vitro and In Vivo , 2020, Molecular Cancer Therapeutics.

[32]  Sven Rahmann,et al.  Genome analysis , 2022 .

[33]  Wei Shi,et al.  featureCounts: an efficient general purpose program for assigning sequence reads to genomic features , 2013, Bioinform..

[34]  A. Weber.  Discovering New Biology through Sequencing of RNA1 , 2015, Plant Physiology.

[35]  C. Thermes,et al.  Library preparation methods for next-generation sequencing: tone down the bias. , 2014, Experimental cell research.

[36]  M. Gerstein,et al.  RNA-Seq: a revolutionary tool for transcriptomics , 2009, Nature Reviews Genetics.

[37]  E. Dermitzakis,et al.  Expression quantitative trait loci: present and future , 2013, Philosophical Transactions of the Royal Society B: Biological Sciences.

[38]  S. Mandrup,et al.  iRNA-seq: computational method for genome-wide assessment of acute transcriptional regulation from total RNA-seq data , 2015, Nucleic acids research.

[39]  Fulvio Magni,et al.  Integration of Omics Approaches and Systems Biology for Clinical Applications , 2018 .

[40]  W. Huber,et al.  Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 , 2014, Genome Biology.

[41]  Alan J. Cross,et al.  Comprehensive assessment of multiple biases in small RNA sequencing reveals significant differences in the performance of widely used methods , 2018, bioRxiv.

[42]  Tanya Barrett,et al.  The Gene Expression Omnibus Database , 2016, Statistical Genomics.

[43]  J. Kirk,et al.  Systematic evaluation of RNA-Seq preparation protocol performance , 2019, BMC Genomics.

[44]  F. Baralle,et al.  Alternative splicing as a regulator of development and tissue identity , 2017, Nature Reviews Molecular Cell Biology.

[45]  Lan Lin,et al.  rMATS: Robust and flexible detection of differential alternative splicing from replicate RNA-Seq data , 2014, Proceedings of the National Academy of Sciences.

[46]  Davis J. McCarthy,et al.  Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation , 2012, Nucleic acids research.

[47]  J. Hadfield,et al.  RNA sequencing: the teenage years , 2019, Nature Reviews Genetics.

[48]  Thomas R. Gingeras,et al.  STAR: ultrafast universal RNA-seq aligner , 2013, Bioinform..

[49]  Hongmin Liu,et al.  USP7: Novel Drug Target in Cancer Therapy , 2019, Front. Pharmacol..

[50]  C. Amos,et al.  RNA-Seq Analysis of Differential Splice Junction Usage and Intron Retentions by DEXSeq , 2015, PloS one.

[51]  Günter P. Wagner,et al.  Measurement of mRNA abundance using RNA-seq data: RPKM measure is inconsistent among samples , 2012, Theory in Biosciences.

[52]  Steve Horvath,et al.  WGCNA: an R package for weighted correlation network analysis , 2008, BMC Bioinformatics.

[53]  Matti Pirinen,et al.  Assessing allele-specific expression across multiple tissues from RNA-seq read data , 2015, Bioinform..

[54]  Rasko Leinonen,et al.  The sequence read archive: explosive growth of sequencing data , 2011, Nucleic Acids Res..

[55]  Huiru Zheng,et al.  Review of applications of high-throughput sequencing in personalized medicine: barriers and facilitators of future progress in research and clinical application , 2019, Briefings Bioinform..

[56]  Yixing Han,et al.  Advanced Applications of RNA Sequencing and Challenges , 2015, Bioinformatics and biology insights.

[57]  Stephen Hartley,et al.  QoRTs: a comprehensive toolset for quality control and data processing of RNA-Seq experiments , 2015, BMC Bioinformatics.

[58]  Yang Wang,et al.  Cellular functions of long noncoding RNAs , 2019, Nature Cell Biology.

[59]  Gebert Lfr,et al.  Regulation of microRNA function in animals , 2019 .

[60]  Matthias Becker,et al.  Shiny-Seq: advanced guided transcriptome analysis , 2019, BMC Research Notes.

[61]  G. Cochrane,et al.  The International Nucleotide Sequence Database Collaboration , 2011, Nucleic Acids Res..

[62]  Lior Pachter,et al.  Near-optimal probabilistic RNA-seq quantification , 2016, Nature Biotechnology.

[63]  Zhandong Liu,et al.  An ultra-fast and scalable quantification pipeline for transposable elements from next generation sequencing data , 2018, PSB.

[64]  B. Janssen,et al.  The Use of Transcriptomics in Clinical Applications , 2018 .

[65]  David P. Kreil,et al.  A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control consortium , 2014, Nature Biotechnology.

[66]  M. DePristo,et al.  The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. , 2010, Genome research.