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mRNA Sequencing for Differential Gene Expression Analysis
Compare gene expression profiles obtained under different conditions
Study genetic profiles from transcript to pathway level
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
For further reading and a detailed technical description, please download our Application Note Illumina RNA Sequencing (see related downloads).
- Are there different patterns of expression in an experiment? (see Figure 1)
- What are the top differentially expressed genes? (see Figure 2)
- What are the detailed statistics of all measured genes? (see Figure 3)
- Which pathways may influence the observed phenotypes? (complementary pathway analysis, see Figure 4)
- Which of the possible alternative transcripts are expressed at the time of the experiment? (complementary alternative splicing analysis, see Figure 5)
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 5: The optional alternative splicing analysis helps identify the most probable transcript of a gene at the time of the experiment.
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.