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Keywords: Differential expression  or RNAseq 

and

Authors: Jared Simpson  or Eija Korpelainen  or Maria Victoria .  or Julie Sullivan  or Friederike Ehrhart  or nekrut  or Michael Love 


RNA-seq data analysis and differential expression

Bioconductor provides tools for the analysis and comprehension of high-throughput genomic data. Bioconductor uses the R statistical programming language, and is open source and open development. It has two releases each year, 1560 software packages, and an...

Keywords: RNAseq

RNA-seq data analysis and differential expression https://tess.elixir-europe.org/materials/rna-seq-data-analysis-and-differential-expression Bioconductor provides tools for the analysis and comprehension of high-throughput genomic data. Bioconductor uses the R statistical programming language, and is open source and open development. It has two releases each year, 1560 software packages, and an active user community. Bioconductor is also available as an AMI (Amazon Machine Image) and a series of Docker images. RNAseq
RNA-seq data analysis: from raw reads to differentially expressed genes

This course material introduces the central concepts, analysis steps and file formats in RNA-seq data analysis. It covers the analysis from quality control to differential expression detection, and workflow construction and several data visualizations are also practised. The material consists of...

Scientific topics: Sequencing, RNA, Data architecture, analysis and design, Bioinformatics

Keywords: Bioinformatics, Differential expression, Ngs, Rna seq

RNA-seq data analysis: from raw reads to differentially expressed genes https://tess.elixir-europe.org/materials/rna-seq-data-analysis-from-raw-reads-to-differentially-expressed-genes This course material introduces the central concepts, analysis steps and file formats in RNA-seq data analysis. It covers the analysis from quality control to differential expression detection, and workflow construction and several data visualizations are also practised. The material consists of 10-30 minute lectures intertwined with hands-on exercises, and it can be accomplished in a day. As the user-friendly Chipster software is used in the exercises, no prior knowledge of R/Bioconductor or Unix ir required, and the course is thus suitable for everybody. Our book RNA-seq data analysis: A practical approach (CRC Press) can be used as background reading. The following topics and analysis tools are covered: 1. Introduction to the Chipster analysis platform 2. Quality control of raw reads (FastQC, PRINSEQ) 3. Preprocessing (Trimmomatic, PRINSEQ) 4. Alignment to reference genome (TopHat2) 5. Alignment level quality control (RseQC) 6. Quantitation (HTSeq) 7. Experiment level quality control with PCA and MDS plots 8. Differential expression analysis (DESeq2, edgeR) -normalization -dispersion estimation -statistical testing -controlling for batch effects, multifactor designs -filtering -multiple testing correction 9. Visualization of reads and results -genome browser -Venn diagram -volcano plot -plotting normalized counts for a gene -expression profiles 10. Experimental design Sequencing RNA Data architecture, analysis and design Bioinformatics Bioinformatics, Differential expression, Ngs, Rna seq Bench biologists Life Science Researchers 2015-12-04 2017-10-09