Extracting novel hypotheses and findings from RNA-seq data
Review article, 2020

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.

lncRNA

RNA-seq data analysis

yeast

transcriptome

GO term analysis

phylostratigraphy

Author

Tyler Doughty

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Eduard Kerkhoven

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

FEMS Yeast Research

1567-1356 (ISSN) 1567-1364 (eISSN)

Vol. 20 2 foaa007

Subject Categories

Bioinformatics (Computational Biology)

Bioinformatics and Systems Biology

Genetics

DOI

10.1093/femsyr/foaa007

PubMed

32009158

More information

Latest update

3/31/2020