RNA-seq Data Analysis: A Practical Approach (Chapman & Hall/CRC Mathematical and Computational Biology)

RNA-seq Data Analysis: A Practical Approach (Chapman & Hall/CRC Mathematical and Computational Biology)

By: Garry Wong (author), Eija Korpelainen (author), Mikael Huss (author), Jarno Tuimala (author), Panu Somervuo (author)Mixed Media

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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 Introduction to RNA-seq data analysis Quality control and preprocessing Aligning reads to reference and visualizing them in genomic context Transcriptome assembly Annotation-based quality control and quantitation of gene expression RNA-seq analysis framework in R and Bioconductor Differential expression analysis Analysis of differential exon usage Annotating the results Visualization Small non-coding RNAs Computational analysis of small noncoding RNA sequencing data

Product Details

  • ISBN13: 9781466595002
  • Format: Mixed Media
  • Number Of Pages: 322
  • ID: 9781466595002
  • weight: 620
  • ISBN10: 1466595000

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