Transcriptome Quantification and Differential Expression from NGS Data

Olga Glebova*, Yvette Temate-Tiagueu, Adrian Caciula, Sahar Al Seesi, Alexander Artyomenko, Serghei Mangul, James Lindsay, Ion I. Măndoiu, Alexander Zelikovsky

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter


Transcriptome quantification analysis is crucial to determine similar transcripts or unraveling gene functions and transcription regulation mechanisms. This chapter presents a novel simulated regression-based method for isoform frequency estimation from RNA-Seq reads. It presents SimReg, a novel regression-based algorithm for transcriptome quantification. Simulated data experiments demonstrate superior frequency estimation accuracy of SimReg comparatively to that of the existing tools, which tend to skew the estimated frequency toward supertranscripts. Gene expression is the process by which the genetic code (the nucleotide sequence) of a gene becomes a useful product. Important factors to consider while analyzing differentially expressed genes are normalization, accuracy of differential expression detection, and differential expression analysis when one condition has no detectable expression. RNA-Seq is an increasingly popular approach to transcriptome profiling that uses the capabilities of next-generation sequencing (NGS) technologies and provides better measurement of levels of transcripts and their isoforms.

Original languageEnglish (US)
Title of host publicationComputational Methods for Next Generation Sequencing Data Analysis
Number of pages27
ISBN (Electronic)9781119272182
ISBN (Print)9781118169483
StatePublished - Sep 6 2016
Externally publishedYes


  • Differential expression detection
  • Gene expression
  • Next-generation sequencing
  • RNA-Seq reads
  • Simulated regression-based method
  • Transcriptome quantification

ASJC Scopus subject areas

  • Computer Science(all)


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