A survey of best practices for RNA-seq data analysis
Ana Conesa,Pedro Madrigal,Pedro Madrigal,Sonia Tarazona,David Gomez-Cabrero,Alejandra Cervera,Andrew McPherson,Michał Wojciech Szcześniak,Daniel J. Gaffney,Laura L. Elo,Xuegong Zhang,Ali Mortazavi +11 more
TL;DR: All of the major steps in RNA-seq data analysis are reviewed, including experimental design, quality control, read alignment, quantification of gene and transcript levels, visualization, differential gene expression, alternative splicing, functional analysis, gene fusion detection and eQTL mapping.
read more
Abstract: RNA-sequencing (RNA-seq) has a wide variety of applications, but no single analysis pipeline can be used in all cases. We review all of the major steps in RNA-seq data analysis, including experimental design, quality control, read alignment, quantification of gene and transcript levels, visualization, differential gene expression, alternative splicing, functional analysis, gene fusion detection and eQTL mapping. We highlight the challenges associated with each step. We discuss the analysis of small RNAs and the integration of RNA-seq with other functional genomics techniques. Finally, we discuss the outlook for novel technologies that are changing the state of the art in transcriptomics.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Integrating single-cell transcriptomic data across different conditions, technologies, and species.
TL;DR: An analytical strategy for integrating scRNA-seq data sets based on common sources of variation is introduced, enabling the identification of shared populations across data sets and downstream comparative analysis.
Integrative analysis of 111 reference human epigenomes
Anshul Kundaje,Wouter Meuleman,Jason Ernst,Angela Yen,Pouya Kheradpour,Zhizhuo Zhang,Jianrong Wang,Lucas D. Ward,Abhishek Sarkar,Gerald Quon,Matthew L. Eaton,Yi-Chieh Wu,Andreas R. Pfenning,Xinchen Wang,Melina Claussnitzer,Yaping Liu,Mukul S. Bansal,Soheil Feizi-Khankandi,Ah Ram Kim,Richard C Sallari,Nicholas A Sinnott-Armstrong,Laurie A. Boyer,Elizabeta Gjoneska,Li-Huei Tsai,Manolis Kellis +24 more
- 01 Feb 2015
TL;DR: In this article, the authors describe the integrative analysis of 111 reference human epigenomes generated as part of the NIH Roadmap Epigenomics Consortium, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression.
5K
Multi-omics approaches to disease.
TL;DR: This review provides an overview of omics technologies and methods for their integration across multiple omics layers and offers the opportunity to understand the flow of information that underlies disease.
Pathway enrichment analysis and visualization of omics data using g:Profiler, GSEA, Cytoscape and EnrichmentMap
Jüri Reimand,Jüri Reimand,Ruth Isserlin,Veronique Voisin,Mike Kucera,Christian Tannus-Lopes,Asha Rostamianfar,Lina Wadi,Mona Meyer,Judy M. Y. Wong,Chao Xu,Daniele Merico,Gary D. Bader +12 more
TL;DR: This protocol describes pathway enrichment analysis of gene lists from RNA-seq and other genomics experiments using g:Profiler, GSEA, Cytoscape and EnrichmentMap software, and describes innovative visualization techniques.
1.6K
RNA sequencing: the teenage years
TL;DR: Advances in RNA-sequencing technologies and methods over the past decade are discussed and adaptations that are enabling a fuller understanding of RNA biology are outlined, from when and where an RNA is expressed to the structures it adopts.
1.6K
References
Identifying differentially spliced genes from two groups of RNA-seq samples.
TL;DR: An NB-statistic method is developed that can detect differentially spliced genes between two groups of samples without using a prior knowledge on the annotation of alternative splicing without needing to infer isoform structure or to estimate isoform expression.
Defuse: An algorithm for gene fusion discovery in tumor rna-seq data
Andrew McPherson,Fereydoun Hormozdiari,Abdalnasser Zayed,Ryan Giuliany,Gavin Ha,Mark G. F. Sun,Malachi Griffith,Alireza Heravi Moussavi,Janine Senz,Nataliya Melnyk,Marina Pacheco,Marco A. Marra,Martin Hirst,Torsten O. Nielsen,S. Cenk Sahinalp,David G. Huntsman,Sohrab P. Shah +16 more
TL;DR: It is concluded that gene fusions are not infrequent events in ovarian cancer and that these events have the potential to substantially alter the expression patterns of the genes involved; gene fusion should therefore be considered in efforts to comprehensively characterize the mutational profiles of ovarian cancer transcriptomes.
InterPro in 2011: new developments in the family and domain prediction database
Sarah Hunter,Philip Jones,Alex L. Mitchell,Rolf Apweiler,Teresa K. Attwood,Alex Bateman,Thomas E. Bernard,David Binns,Peer Bork,Sarah W. Burge,Edouard de Castro,Penny Coggill,Matthew Corbett,Ujjwal Das,Louise C. Daugherty,Lauranne Duquenne,Robert D. Finn,Matthew Fraser,Julian Gough,Daniel H. Haft,Nicolas Hulo,Daniel Kahn,Elizabeth Kelly,Ivica Letunic,David M. Lonsdale,Rodrigo Lopez,Martin Madera,John Maslen,Craig McAnulla,Jennifer McDowall,Conor McMenamin,Huaiyu Mi,Prudence Mutowo-Muellenet,Nicola Mulder,Darren A. Natale,Christine A. Orengo,Sebastien Pesseat,Marco Punta,Antony F. Quinn,Catherine Rivoire,Amaia Sangrador-Vegas,Jeremy D. Selengut,Christian J. A. Sigrist,Maxim Scheremetjew,John Tate,Manjulapramila Thimmajanarthanan,Paul Thomas,Cathy H. Wu,Corin Yeats,Siew Yit Yong +49 more
TL;DR: An overview of new developments in the InterPro database and its associated software since 2009 is given, including updates to database content, curation processes and Web and programmatic interfaces.
Understanding gene regulatory mechanisms by integrating ChIP-seq and RNA-seq data: statistical solutions to biological problems.
Claudia Angelini,Valerio Costa +1 more
TL;DR: It is shown how integrating ChIP-seq and RNA-seq data can help to elucidate gene regulatory mechanisms and propose potential directions for statistical data integration.
Transcriptome genetics using second generation sequencing in a Caucasian population
Stephen B. Montgomery,Micha Sammeth,Maria Gutierrez-Arcelus,Radoslaw P. Lach,Catherine E. Ingle,James Nisbett,Roderic Guigó,Emmanouil T. Dermitzakis,Emmanouil T. Dermitzakis +8 more
TL;DR: This analysis shows that high throughput sequencing technologies reveal new properties of Genetic effects on the transcriptome and allow the exploration of genetic effects in cellular processes.