RNA-seq Data Analysis A Practical Approach Chapman & Hall/CRC Computational Biology Series
Auteurs : Korpelainen Eija, Tuimala Jarno, Somervuo Panu, Huss Mikael, Wong Garry
The State of the Art in Transcriptome Analysis RNA sequencing (RNA-seq) data offers unprecedented information about the transcriptome, but harnessing this information with bioinformatics tools is typically a bottleneck. RNA-seq Data Analysis: A Practical Approach enables researchers to examine differential expression at gene, exon, and transcript levels and to discover novel genes, transcripts, and whole transcriptomes.
Balanced Coverage of Theory and Practice.Each chapter starts with theoretical background, followed by descriptions of relevant analysis tools and practical examples. Accessible to both bioinformaticians and nonprogramming wet lab scientists, the examples illustrate the use of command-line tools, R, and other open source tools, such as the graphical Chipster software.
The Tools and Methods to Get Started in Your Lab. Taking readers through the whole data analysis workflow, this self-contained guide provides a detailed overview of the main RNA-seq data analysis methods and explains how to use them in practice. It is suitable for researchers from a wide variety of backgrounds, including biology, medicine, genetics, and computer science. The book can also be used in a graduate or advanced undergraduate course.
Introduction. Quality Control. Mapping, and Assembly. Differential Expression. Analysis of Small Non-Coding RNAs.
Eija Korpelainen, Jarno Tuimala, Panu Somervuo, Mikael Huss, Garry Wong
Date de parution : 11-2014
Ouvrage de 298 p.
15.6x23.4 cm
Thèmes de RNA-seq Data Analysis :
Mots-clés :
BAM File; RNA Seq Data; Introduction to RNA-seq data analysis; RNA Seq Read; RNA-seq data analysis methods; Reference Genome; RNA-seq analysis framework in R and Bioconductor; Paired End Read; Transcriptome Analysis; RNA Seq Data Analysis; Computational analysis of small noncoding RNA sequencing data; Data Set; differential expression at gene; exon; and transcript levels; Differential Expression Analysis; bioinformaticians and nonprogramming wet lab scientists; Bioconductor Packages; RNA Seq Experiment; MA Plot; FASTQ File; RNA Seq Study; Splice Junctions; Genome Browser; Entrez Gene Id; UCSC Genome Browser; Gene Id; Annotation Package; FASTA File; NA NA NA; Human Embryonic Stem Cells; Bed File; NA NA; De Bruijn Graph