RNA-seq data analysis : a practical approach
Nucleotide sequence Statistics geenit nukleotidit sekvenssianalyysi sekvensointi RNA sähkökirjat
Taylor & Francis
2015
EISBN 9781466595019
chapter 1. Introduction to RNA-seq.
chapter 2. Introduction to RNA-seq data analysis.
chapter 3. Quality control and preprocessing.
chapter 4. Aligning reads to reference.
chapter 5. Transcriptome assembly.
chapter 6. Quantitation and annotation-based quality control.
chapter 7. RNA-seq analysis framework in R and bioconductor.
chapter 8. Differential expression analysis.
chapter 9. Analysis of differential exon usage.
chapter 10. Annotating the results.
chapter 11. Visualization.
chapter 12. Small noncoding RNAs.
chapter 13. Computational analysis of small noncoding RNA sequencing data.
"RNA-seq offers unprecedented information about transcriptome, but harnessing this information with bioinformatics tools is typically a bottleneck. This self-contained guide enables researchers to examine differential expression at gene, exon, and transcript level and to discover novel genes, transcripts, and whole transcriptomes. Each chapter starts with theoretical background, followed by descriptions of relevant analysis tools. The book also provides examples using command line tools and the R statistical environment. For non-programming scientists, the same examples are covered using open source software with a graphical user interface"--Provided by publisher.
chapter 2. Introduction to RNA-seq data analysis.
chapter 3. Quality control and preprocessing.
chapter 4. Aligning reads to reference.
chapter 5. Transcriptome assembly.
chapter 6. Quantitation and annotation-based quality control.
chapter 7. RNA-seq analysis framework in R and bioconductor.
chapter 8. Differential expression analysis.
chapter 9. Analysis of differential exon usage.
chapter 10. Annotating the results.
chapter 11. Visualization.
chapter 12. Small noncoding RNAs.
chapter 13. Computational analysis of small noncoding RNA sequencing data.
"RNA-seq offers unprecedented information about transcriptome, but harnessing this information with bioinformatics tools is typically a bottleneck. This self-contained guide enables researchers to examine differential expression at gene, exon, and transcript level and to discover novel genes, transcripts, and whole transcriptomes. Each chapter starts with theoretical background, followed by descriptions of relevant analysis tools. The book also provides examples using command line tools and the R statistical environment. For non-programming scientists, the same examples are covered using open source software with a graphical user interface"--Provided by publisher.
