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mRNA Sequencing for Differential Gene Expression Analysis

 
 
Use mRNA sequencing to:
  • Compare gene expression profiles obtained under different conditions
  • Study genetic profiles from transcript to pathway level

Overview

Considerations before starting an mRNA Sequencing project:

  • Poly(A) enrichment or ribo depletion?
  • Sequencing depth (sensitivity)?
  • Read length, single- or paired-end seq. (specificity)?
  • Replicates (confidence)?
  • Model organism or no reference genome available?
  • Library complexity?

Let us guide you – from design to analysis

Example projects using mRNA Sequencing:

  • Functional protein and pathway studies
  • Disease caused gene expression changes
  • Loss, gain and rescue of function experiments
  • Part of an omics charcterization
  • Functional changes due to species interactions
  • Discovery of new genes or non coding regulatory RNA
  • RNA variant detections
  • Drug testing

Applications related to mRNA Sequencing:

  • Reference transcriptome generation
  • Shotgun metatranscriptomics
  • Small RNA sequencing

Workflow

 
A typical workflow for a mRNA sequencing project is shown in the graphic below. Please note that our highly-modular processes allow you various entry and opting out options. If you outsource your entire NGS project to Microsynth or only parts of it is up to you.
 

 

For further reading and a detailed technical description, please download our Application Note Illumina RNA Sequencing (see related downloads).

Results

 
The results produced by our mRNA sequencing analysis module help answer four main questions of an mRNA Seq experiment.
  1. Are there different patterns of expression in an experiment? (see Figure 1)
  2. What are the top differentially expressed genes? (see Figure 2)
  3. What are the detailed statistics of all measured genes? (see Figure 3)
  4. Which pathways may influence the observed phenotypes? (complementary pathway analysis, see Figure 4)

 

Figure 1: This heatmap is based on the expression patterns of the samples and shows their similarity to each other. Thus helping clarify if the conditions used in the experiment lead to different patterns of expression.

 

Figure 3: For all measured genes detailed statistics such as log fold change and its significance are listed for further study.

Figure 2: The second heatmap shows the top upregulated and top downregulated genes from a pairwise comparison of two conditions (e.g., stressor vs control).

 

Figure 4: The optional pathway enrichment analysis helps identify differentially regulated pathways which in turn may explain observed phenotypes.