gene expression profile analysis is directly affected by the selected reference gene: the case of leaf-cutting atta sexdens
Clicks: 188
ID: 228225
2018
Article Quality & Performance Metrics
Overall Quality
Improving Quality
0.0
/100
Combines engagement data with AI-assessed academic quality
Reader Engagement
Emerging Content
30.0
/100
181 views
17 readers
Trending
AI Quality Assessment
Not analyzed
Abstract
Although several ant species are important targets for the development of molecular control strategies, only a few studies focus on identifying and validating reference genes for quantitative reverse transcription polymerase chain reaction (RT-qPCR) data normalization. We provide here an extensive study to identify and validate suitable reference genes for gene expression analysis in the ant Atta sexdens, a threatening agricultural pest in South America. The optimal number of reference genes varies according to each sample and the result generated by RefFinder differed about which is the most suitable reference gene. Results suggest that the RPS16, NADH and SDHB genes were the best reference genes in the sample pool according to stability values. The SNF7 gene expression pattern was stable in all evaluated sample set. In contrast, when using less stable reference genes for normalization a large variability in SNF7 gene expression was recorded. There is no universal reference gene suitable for all conditions under analysis, since these genes can also participate in different cellular functions, thus requiring a systematic validation of possible reference genes for each specific condition. The choice of reference genes on SNF7 gene normalization confirmed that unstable reference genes might drastically change the expression profile analysis of target candidate genes.
| Reference Key |
livramento2018insectsgene
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | ;Kalynka G. do Livramento;Natália C. Freitas;Wesley P. F. Máximo;Ronald Zanetti;Luciano V. Paiva |
| Journal | conference proceedings : annual international conference of the ieee engineering in medicine and biology society ieee engineering in medicine and biology society annual conference |
| Year | 2018 |
| DOI |
10.3390/insects9010018
|
| URL | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.