Corpus ID: 55430337

Introduction : overview of the RNA sequencing assay

@inproceedings{2018IntroductionO,
  title={Introduction : overview of the RNA sequencing assay},
  author={},
  year={2018}
}

• Published 2018

Gene expression is the fundamental level at which the result of various genetic and regulatory programs are observable. The measurement of transcriptome­wide gene expression has convincingly switched from microarrays to sequencing in a matter of years. RNA sequencing (RNA­seq) provides a quantitative and open system for profiling transcriptional outcomes on a large scale and therefore facilitates a large diversity of applications, including basic science studies, but also agricultural or clinical situations. In the past 10 years or so, much has been learned about the characteristics of the RNA­seq datasets as well as the performance of the myriad of methods developed. In this review, we give an overall view of the developments in RNA­seq data analysis, including experimental design, with an explicit focus on quantification of gene expression and statistical approaches for differential expression. We also highlight emerging data types, such as single­cell RNA­seq and gene expression profiling using long­read technologies. Abbreviations: DE ­ differential expression / differentially expressed DGE ­ differential gene expression DTE ­ differential transcript expression DTU ­ differential transcript usage GLM ­ generalized linear model DS ­ differential splicing LRT ­ likelihood ratio test TPM ­ transcripts per million EM ­ expectation maximization SJ ­ splice junction bp ­ base pairs NB ­ negative binomial ML ­ maximum likelihood MM ­ method of moments FDR ­ false discovery rate VB ­ variational Bayes LRTS ­ long­read transcript sequencing APL ­ approximate profile likelihood