Choosing reference genes for RT-qPCR for Fusarium graminearum plant infection (In Planta) and In Vitro growth studies based on transcriptomic data
- Subject Areas
- Agricultural Science, Microbiology, Molecular Biology, Mycology, Plant Science
- qPCR, housekeeping gene selection, reference gene selection, transcriptome data, plant infection, In Planta, In Vitro, RT-qPCR
- © 2019 Lin et al.
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2019. Choosing reference genes for RT-qPCR for Fusarium graminearum plant infection (In Planta) and In Vitro growth studies based on transcriptomic data. PeerJ Preprints 7:e27537v1 https://doi.org/10.7287/peerj.preprints.27537v1
Background. Choosing reference genes for RT-qPCR for the study of transcriptomic responses of target genes is often done using “standard” reference genes (housekeeping genes) selected before the genomic era. Now, published transcriptome data can be used to aid in this selection to avoid the selection of a reference gene that varies and obscure results.
Methods. We use transcriptome data for the model pathogen fungus Fusarium graminearum to select housekeeping genes for In Vitro and In Planta conditions. Transcriptome data was downloaded from a publicly available database. We selected a database where transcriptome chip data from many experiments using the same chip has been deposited divided the downloaded data into In Vitro and In Planta conditions based on the information about the experiments.
Results. We ranked the genes with the least variation (relative difference between maximum and minimum expression) across each dataset. Genes previously shown to perform well as reference genes for In Vitro conditions in a similar analysis as ours also performed well for In Vitro conditions in our dataset but worked less well for In Planta conditions. We found 5 reference genes that performed well under both In Planta conditions and In Vitro conditions.
Discussion. Even if these 5 reference genes performed well, for other (new) target conditions we recommend making a transcriptome analysis to select well performing reference genes for RT-qPCR if possible. Alternatively, select 2 of the 5 genes that we show here performed well under both In Planta and In Vitro conditions.
This is a submission to PeerJ for review.
Table for converting PlexDB used PROBE_SET codes to Broad and FGDBv32 gene FGSG-IDs as well as the Broad and FGDBv32 annotations for the entries
List of experiments as downloaded from PlexDB
Plot of variation values for Log2 maximum relative difference in expression times 100 against Log2 for average expression of each gene for the In Planta data
Each dot is one gene.
Plot of variation values for Log2 maximum relative difference in expression times 100 against Log2 for average expression of each gene for the In Vitro data
Each dot is one gene.