Extracting novel hypotheses and findings from RNA-seq data

FEMS Yeast Res. 2020 Mar 1;20(2):foaa007. doi: 10.1093/femsyr/foaa007.

Abstract

Over the past decade, improvements in technology and methods have enabled rapid and relatively inexpensive generation of high-quality RNA-seq datasets. These datasets have been used to characterize gene expression for several yeast species and have provided systems-level insights for basic biology, biotechnology and medicine. Herein, we discuss new techniques that have emerged and existing techniques that enable analysts to extract information from multifactorial yeast RNA-seq datasets. Ultimately, this minireview seeks to inspire readers to query datasets, whether previously published or freshly obtained, with creative and diverse methods to discover and support novel hypotheses.

Keywords: GO term analysis; RNA-seq data analysis; lncRNA; phylostratigraphy; transcriptome; yeast.

Publication types

  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Data Analysis*
  • Datasets as Topic
  • Gene Expression Profiling
  • RNA, Fungal / genetics*
  • RNA-Seq / methods
  • RNA-Seq / statistics & numerical data*
  • Sequence Analysis, RNA / methods*
  • Sequence Analysis, RNA / statistics & numerical data*
  • Transcriptome
  • Yeasts / genetics*

Substances

  • RNA, Fungal