The bench scientist’s guide to statistical analysis of RNA-seq data

Craig R. Yendrek, Elizabeth A. Ainsworth, Jyothi Thimmapuram

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

As a method for characterizing global changes in transcription, RNA-Seq is an attractive option because of the ability to quantify differences in mRNA abundance in response to various treatments and diseases, as well as to detect alternative splice variants and novel transcripts [1]. Compared to microarray techniques, RNA-Seq eliminates the need for prior speciesspecific sequence information and overcomes the limitation of detecting low abundance transcripts. In addition, early studies have demonstrated that RNA-Seq is very reliable in terms of technical reproducibility [2]. As a result, biologists studying an array of model and non-model organisms are beginning to utilize RNA-Seq analysis with ever growing frequency [3-7]. However, without experience using bioinformatics methods, the large number of choices available to analyze differential expression can be overwhelming for the bench scientist (see Table one in [8]).

Original languageEnglish (US)
Title of host publicationBioinformatics
Subtitle of host publicationThe Impact of Accurate Quantification on Proteomic and Genetic Analysis and Research
PublisherApple Academic Press
Pages1-20
Number of pages20
ISBN (Electronic)9781482246629
ISBN (Print)9781771880190
DOIs
StatePublished - Jan 1 2014

ASJC Scopus subject areas

  • Mathematics(all)
  • Biochemistry, Genetics and Molecular Biology(all)

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  • Cite this

    Yendrek, C. R., Ainsworth, E. A., & Thimmapuram, J. (2014). The bench scientist’s guide to statistical analysis of RNA-seq data. In Bioinformatics: The Impact of Accurate Quantification on Proteomic and Genetic Analysis and Research (pp. 1-20). Apple Academic Press. https://doi.org/10.1201/b16589