RNA sequencing analysis

RNA sequencing analysis allows you to study gene regulation beyond differentially expressed genes

Transcriptome-wide analyses of gene expression levels are currently extremely popular among researchers studying gene regulation in a wide variety of biological systems. Typically, our clients are interested in differential gene expression based on either RNA sequencing analysis or gene expression microarray data, followed by a pathway analysis.

Although gene expression analysis is the cornerstone of RNA sequencing analysis, it does not have to stop there. We also encourage our clients to explore some of the less-studied aspects of gene regulation, such as alternative splicing events, post-transcriptional A-to-I editing events, and long non-coding RNAs (lncRNAs).

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

For non-model organisms, RNA sequencing analysis offers significant benefits in assembling genomes as well as naturally assembling and annotating entire transcriptomes. The resulting high-quality gene models enable expression studies just like in any model organism.

To learn more about how Genevia's bioinformatics as a service (BaaS) works, click here to read more.

Why limit your study to expression levels when you can do so much more with your RNA-seq data – identify transcript isoforms, fusion genes, and lncRNAs, for example?

Reija Hieta
Reija Hieta Technology Specialist Genevia Technologies Oy

See examples of RNA sequencing analysis below:

  • Transcriptome assembly and annotation Find novel genes in exotic organisms or identify abnormalities in well-studied models
    • Whole transcriptome sequencing data enable computational assembly of short RNA-seq reads into entire transcripts, either de novo or based on a reference genome. We then annotate the identified transcripts with their location in the genome if one exists, and compare their sequences to transcript databases in order to identify them. For example, we can also study known motifs in the transcripts to predict their enzymatic function.


      • List of assembled transcript sequences
      • Transcript coordinates on a genome sequence
      • Annotated names and/or functions for each transcript
  • Expression analysis Understand gene regulatory changes resulting from a treatment or a condition
    • The most common type of study with RNA-seq data is gene (or microRNA) expression analysis. First, we identify which gene each sequencing read comes from, and then compute gene expression levels for all annotated genes. Differential expression levels between sample groups are then inferred using robust statistical testing. Co-expressed genes can be found using clustering approaches - gene groups often form the basis for more advanced analyses of gene regulation, for example.


      • Gene expression levels for all genes/isoforms in all samples
      • Differentially expressed genes between sample groups
      • Time-dependent genes in a time-series experiment
  • Pathway analysis Dig deeper into molecular mechanisms in order to help explain your phenotype
    • Interpreting the common biological themes in gene groups can be a daunting task. We summarize the differences between samples on the level of metabolic and signalling pathways or functional categories using pathway enrichment or gene set enrichment methods. Pathway activities can then be visualized to enable data exploration. This facilitates the understanding of results of an expression study and helps, for instance, in determining the mechanism of action of a drug compound.


      • Enriched metabolic and signalling pathways
      • Enriched functional categories
      • Visualizations of changes in key pathways
  • Alternative splicing analysis Limiting yourself to the gene level may leave the true changes undiscovered
    • RNA-sequencing data allow identification of novel splicing isoforms and quantification of expression levels for all transcript variants of a gene, given a high enough expression level and sufficient depth of sequencing. Typically we do not differentiate between gene isoforms in expression analysis due to practical reasons, but some of our projects are centered on investigating the expression rates for all splice variants, novel and known.


      • List of expressed transcript isoforms
      • List of previously unidentified splicing isoforms
      • Quantified expression levels for all isoforms
  • Fusion gene detection Be the first to report novel fusion genes in your cancer samples
    • Fusion genes, often caused by genomic rearrangements and leading to expression of dysregulated or fusion proteins, have a central role in the development of some cancers. We detect the expression of transcripts formed by two wild-type genes from normal RNA-sequencing data. The potential fusions will be ranked based on available sequence evidence and other data.


      • Evidence for existence of known fusion genes
      • List of novel fusion genes
      • Expression estimates for fusion partners
  • Novel transcript detection Identify novel genes based on disease/condition/development-specific expression
    • Not all genes - even in the human genome - have been recorded in databases to date. Using RNA-sequencing we can reveal transcription from genomic loci to which no genes have yet been annotated. Usually these are non-coding transcripts that may be extremely relevant tissue- or condition-specific regulators of gene expression.


      • Assembled and annotated expressed transcripts
      • List of previously unidentified transcripts
      • Expression estimates for all transcripts
  • GRO-seq A novel method that allows you to take the perfect snapshot of transcription
    • Available run-on assays identify the genes that are being transcribed at a certain point in time instead of genes that have been recently transcribed. This difference may be essential in studying gene regulation with ChIP-seq measurements, for example. Another difference to mRNA-seq is that the data come from primary transcripts instead of mature mRNAs. Our analysis of GRO-seq data results in quantification of expression levels and transcription start sites of the transcribed genes.


      • List of expressed genes
      • Expression estimates for all genes
      • Genomic coordinates for primary transcripts

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