Literature DB >> 26674615

RNA-Seq workflow: gene-level exploratory analysis and differential expression.

Michael I Love1, Simon Anders2, Vladislav Kim3, Wolfgang Huber3.   

Abstract

Here we walk through an end-to-end gene-level RNA-Seq differential expression workflow using Bioconductor packages. We will start from the FASTQ files, show how these were aligned to the reference genome, and prepare a count matrix which tallies the number of RNA-seq reads/fragments within each gene for each sample. We will perform exploratory data analysis (EDA) for quality assessment and to explore the relationship between samples, perform differential gene expression analysis, and visually explore the results.

Entities:  

Keywords:  Bioconductor; RNA-seq; differential expression; gene expression; genomics; high-throughput sequencing; statistical analysis; visualization

Year:  2015        PMID: 26674615      PMCID: PMC4670015          DOI: 10.12688/f1000research.7035.1

Source DB:  PubMed          Journal:  F1000Res        ISSN: 2046-1402


  23 in total

1.  The human genome browser at UCSC.

Authors:  W James Kent; Charles W Sugnet; Terrence S Furey; Krishna M Roskin; Tom H Pringle; Alan M Zahler; David Haussler
Journal:  Genome Res       Date:  2002-06       Impact factor: 9.043

2.  Normalization of RNA-seq data using factor analysis of control genes or samples.

Authors:  Davide Risso; John Ngai; Terence P Speed; Sandrine Dudoit
Journal:  Nat Biotechnol       Date:  2014-08-24       Impact factor: 54.908

3.  ReportingTools: an automated result processing and presentation toolkit for high-throughput genomic analyses.

Authors:  Melanie A Huntley; Jessica L Larson; Christina Chaivorapol; Gabriel Becker; Michael Lawrence; Jason A Hackney; Joshua S Kaminker
Journal:  Bioinformatics       Date:  2013-09-29       Impact factor: 6.937

4.  STAR: ultrafast universal RNA-seq aligner.

Authors:  Alexander Dobin; Carrie A Davis; Felix Schlesinger; Jorg Drenkow; Chris Zaleski; Sonali Jha; Philippe Batut; Mark Chaisson; Thomas R Gingeras
Journal:  Bioinformatics       Date:  2012-10-25       Impact factor: 6.937

5.  EBSeq: an empirical Bayes hierarchical model for inference in RNA-seq experiments.

Authors:  Ning Leng; John A Dawson; James A Thomson; Victor Ruotti; Anna I Rissman; Bart M G Smits; Jill D Haag; Michael N Gould; Ron M Stewart; Christina Kendziorski
Journal:  Bioinformatics       Date:  2013-02-21       Impact factor: 6.937

6.  baySeq: empirical Bayesian methods for identifying differential expression in sequence count data.

Authors:  Thomas J Hardcastle; Krystyna A Kelly
Journal:  BMC Bioinformatics       Date:  2010-08-10       Impact factor: 3.169

7.  Differential expression analysis for sequence count data.

Authors:  Simon Anders; Wolfgang Huber
Journal:  Genome Biol       Date:  2010-10-27       Impact factor: 13.583

8.  voom: Precision weights unlock linear model analysis tools for RNA-seq read counts.

Authors:  Charity W Law; Yunshun Chen; Wei Shi; Gordon K Smyth
Journal:  Genome Biol       Date:  2014-02-03       Impact factor: 13.583

9.  HTSeq--a Python framework to work with high-throughput sequencing data.

Authors:  Simon Anders; Paul Theodor Pyl; Wolfgang Huber
Journal:  Bioinformatics       Date:  2014-09-25       Impact factor: 6.937

10.  edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.

Authors:  Mark D Robinson; Davis J McCarthy; Gordon K Smyth
Journal:  Bioinformatics       Date:  2009-11-11       Impact factor: 6.937

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  118 in total

1.  Fluent genomics with  plyranges and  tximeta.

Authors:  Stuart Lee; Michael Lawrence; Michael I Love
Journal:  F1000Res       Date:  2020-02-12

2.  Reduced proinsecticide activation by cytochrome P450 confers coumaphos resistance in the major bee parasite Varroa destructor.

Authors:  Spyridon Vlogiannitis; Konstantinos Mavridis; Wannes Dermauw; Simon Snoeck; Evangelia Katsavou; Evangelia Morou; Paschalis Harizanis; Luc Swevers; Janet Hemingway; René Feyereisen; Thomas Van Leeuwen; John Vontas
Journal:  Proc Natl Acad Sci U S A       Date:  2021-02-09       Impact factor: 11.205

3.  Changes in the genetic requirements for microbial interactions with increasing community complexity.

Authors:  Manon Morin; Emily C Pierce; Rachel J Dutton
Journal:  Elife       Date:  2018-09-13       Impact factor: 8.140

4.  Identifying a novel connection between the fungal plasma membrane and pH-sensing.

Authors:  Hannah E Brown; Kyla S Ost; Shannon K Esher; Kaila M Pianalto; Joseph W Saelens; Ziqiang Guan; J Andrew Alspaugh
Journal:  Mol Microbiol       Date:  2018-09-09       Impact factor: 3.501

5.  Integration of Transcriptomic Data Identifies Global and Cell-Specific Asthma-Related Gene Expression Signatures.

Authors:  Mengyuan Kan; Maya Shumyatcher; Avantika Diwadkar; Gabriel Soliman; Blanca E Himes
Journal:  AMIA Annu Symp Proc       Date:  2018-12-05

6.  Bayesian inference of gene expression states from single-cell RNA-seq data.

Authors:  Jérémie Breda; Mihaela Zavolan; Erik van Nimwegen
Journal:  Nat Biotechnol       Date:  2021-04-29       Impact factor: 54.908

Review 7.  Processing and Analysis of RNA-seq Data from Public Resources.

Authors:  Yazeed Zoabi; Noam Shomron
Journal:  Methods Mol Biol       Date:  2021

8.  Minority stress and leukocyte gene expression in sexual minority men living with treated HIV infection.

Authors:  Annesa Flentje; Kord M Kober; Adam W Carrico; Torsten B Neilands; Elena Flowers; Nicholas C Heck; Bradley E Aouizerat
Journal:  Brain Behav Immun       Date:  2018-03-13       Impact factor: 7.217

9.  Global analysis of LARP1 translation targets reveals tunable and dynamic features of 5' TOP motifs.

Authors:  Lucas Philippe; Antonia M G van den Elzen; Maegan J Watson; Carson C Thoreen
Journal:  Proc Natl Acad Sci U S A       Date:  2020-02-24       Impact factor: 11.205

10.  Sexual dimorphism and rapid turnover in gene expression in pre-reproductive seedlings of a dioecious herb.

Authors:  Guillaume G Cossard; Melissa A Toups; John R Pannell
Journal:  Ann Bot       Date:  2019-07-08       Impact factor: 4.357

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