Literature DB >> 30222249

RNA-seq: Basic Bioinformatics Analysis.

Fei Ji1,2, Ruslan I Sadreyev1,3.   

Abstract

Quantitative analysis of gene expression is crucial for understanding the molecular mechanisms underlying genome regulation. RNA-seq is a powerful platform for comprehensive investigation of the transcriptome. In this unit, we present a general bioinformatics workflow for the quantitative analysis of RNA-seq data and describe a few current publicly available computational tools applicable at various steps of this workflow. These tools comprise a pipeline for quality assessment and quantitation of RNA-seq data that starts from raw sequencing files and is focused on the identification and analysis of genes that are differentially expressed between biological conditions.
© 2018 by John Wiley & Sons, Inc. © 2018 John Wiley & Sons, Inc.

Entities:  

Keywords:  RNA-seq; bioinformatics; differentially expressed genes; quantitative analysis of gene expression

Mesh:

Year:  2018        PMID: 30222249      PMCID: PMC6168365          DOI: 10.1002/cpmb.68

Source DB:  PubMed          Journal:  Curr Protoc Mol Biol        ISSN: 1934-3647


  21 in total

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4.  RNA-SeQC: RNA-seq metrics for quality control and process optimization.

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Journal:  Bioinformatics       Date:  2012-04-25       Impact factor: 6.937

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

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Journal:  Bioinformatics       Date:  2014-09-25       Impact factor: 6.937

8.  Qualimap 2: advanced multi-sample quality control for high-throughput sequencing data.

Authors:  Konstantin Okonechnikov; Ana Conesa; Fernando García-Alcalde
Journal:  Bioinformatics       Date:  2015-10-01       Impact factor: 6.937

9.  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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8.  deltaTE: Detection of Translationally Regulated Genes by Integrative Analysis of Ribo-seq and RNA-seq Data.

Authors:  Sonia Chothani; Eleonora Adami; John F Ouyang; Sivakumar Viswanathan; Norbert Hubner; Stuart A Cook; Sebastian Schafer; Owen J L Rackham
Journal:  Curr Protoc Mol Biol       Date:  2019-12
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