RNA sequencing data analysis

RNA sequencing data analysis brings to light the intricate mechanisms of gene regulation

Transcriptome-wide analyses of gene expression are currently extremely popular among researchers studying gene regulation in biological systems ranging from single cells to tissues and complex microbiomes. Typically, our customers are interested in differential gene expression based on RNA sequencing, single-cell RNA sequencing or microRNA sequencing measurements, followed by pathway analysis and integration with other omics modalities, such as epigenomics.

Although gene expression analysis is the cornerstone of RNA sequencing data analysis, it does not have to stop there. We also love to explore some of the less obvious regulatory mechanisms and transcriptomic markers, such as

  • alternative splicing and alternative polyadenylation events,
  • allele-specific expression,
  • long non-coding RNA expression,
  • transposable element expression,
  • genetic variants,
  • post-transcriptional A-to-I editing events,
  • TCR and antibody sequences,
  • fusion genes,
  • novel transcripts,
  • ...and, seriously, this list never ends!

Combining expression data from protein-coding genes and microRNAs allows us to dive deeper yet into the regulatory networks governing biological processes in your cells. Furthermore, these data can be interpreted in the context of signalling pathways with our pathway analysis service.

Many of the above-mentioned analyses are also possible for single-cell RNA sequencing (scRNA-seq) data, depending on the used library preparation protocol. In addition, scRNA-seq data analysis allows for cataloguing cell types and uncovering differentiation trajectories at a scale and resolution unmatched by bulk RNA sequencing. We believe you never truly know your biological system until it is studied on a single-cell level!

For non-model organisms, and those with very dynamic genomes, i.e. microbes, we typically start RNA sequencing data analysis with assembling a transcriptome de novo and annotating it using homologues of related species and computational gene predictions.

A new reference transcriptome is an invaluable resource for your further research, and that of the entire reseach community. Once a high-quality reference transcriptome has been established, the door opens to most downstream analyses which are routinely used with model organisms.

Take a look under the hood

What exactly does your data go through in an expression analysis project? Our workflows for RNA-seq and single-cell RNA-seq data analysis give you a view of the process from quality control to very tailored downstream analyses.


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Bioinformatics buyer's guide