Transcriptomics Tips – Make the most of your data

In order to get meaningful and valuable insights from transcriptomics data, it is important to optimize your study to provide robust results at the data analysis stage. Here we share some tips that will help you to discover biologically relevant insights from your transcriptomics analysis.

Transcriptomics Tip 1 – Consider circadian effects

Be aware of circadian effects on gene expression. The circadian rhythm can heavily influence transcript level gene expression. Therefore, you should try to take all samples at the same time of day. If this is not possible, you can record the time so it can be accounted for statistically.

Tip 2 – Keep samples clean and cold

RNA is notoriously unstable and will degrade rapidly in warm or impure environments. For this reason you should use RNase-free reagents and ensure you keep your samples cold.

Tip 3 – Use internal calibration

It is a good idea to use internal calibration to detect global changes in gene expression. This is because spiking samples with a fixed amount of synthetic RNA or cells from another species before cell lysis allows you to detect whether total mRNA expression levels are changing in your samples; which can have important implications for the interpretation of your study.

Tip 4 – Use appropriate statistical tools

Use a statistical approach that makes use of borrowed variance information whilst accounting for multiple testing as well as the mean/variance relationship in RNA-Seq data.

Tip 5 – Account for confounding

It is important to account for confounding in your models. Particularly as batch effects and other sources of confounding are sometimes unavoidable. Therefore you should account for them in your models when necessary, to prevent them from biasing your results.

Tip 6 – Use functional enrichment analysis

We recommend using functional enrichment analysis to identify enriched pathways. When RNA-Seq generates lists of hundreds of differentially-expressed genes, you can use functional enrichment analysis to gain biologically-relevant insights; by determining whether any of these genes belong to common pathways.

Tip 7 – Avoid cherry-picking

RNA-Seq often generates lists of hundreds of differentially-expressed genes. When looking for leads, it can be tempting to trawl through these lists for your favourite genes.  However, in an exploratory study, it is better to take an unbiased approach and select genes based on their P-values or fold-changes.

Transcriptomics Tip 8 – Validate!

It is important to validate gene expression changes at the protein level. A common mistake in -omics research is to assume that changes in RNA expression correspond to immediate changes in protein abundance.  However, when using RNA-Seq to infer protein expression, it is vital to validate your findings by measuring protein levels directly, for example using proteomics.

Keeping the above tips in mind should help you to conduct robust research which provides suitable data for transcriptomics analysis. When you arrive at the analysis stage of your research, you may be interested in outsourcing the analysis to a bioinformatics provider. At Fios Genomics, we have a great deal of experience in this area. Our skilled bioinformaticians possess the biological knowledge necessary to understand and interpret your transcriptomics data to provide biologically relevant insights to further your research.

To discuss your next transcriptomics analysis project, contact us now!

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