• Manuals

Browse the manual

• Introduction
  • The concept of Biomedical Genomics Analysis
  • System requirements
  • Contact information
• QIAseq Panel Analysis
  • Data management
  • The Analyze QIAseq Panels guide
    • Import your reads
    • Start a QIAseq panel analysis
    • Upload to QCI Interpret
  • How to create a custom panel analysis workflow
    • Import QIAGEN Primers
  • Create a custom Trim adapter list (optional)
• Targeted DNA
  • The Identify QIAseq DNA Variants ready-to-use workflows
    • Output from the Identify QIAseq DNA Variants workflow
    • Quality Control for the Identify QIAseq DNA Variants workflow
  • The Identify TMB Status ready-to-use workflows
    • Output from the Identify TMB Status workflow
  • The Detect QIAseq MSI Status (beta) ready-to-use workflow
    • Output of the Detect QIAseq MSI Status workflow
  • Targeted DNA tools detailed description
    • Calculate TMB Score
    • Detect MSI Status (beta)
    • Generate MSI Baseline (beta)
    • Remove and Annotate with Unique Molecular Index
    • Calculate Unique Molecular Index Groups
    • Create UMI Reads
    • Remove Ligation Artifacts
    • Trim Primers of Mapped Reads
    • Annotate Variants with Unique Molecular Index Info
    • Prepare Guidance Variant Track
    • Import Gene-Pseudogene Table
• Targeted RNAscan
  • The Detect QIAseq RNAscan Fusions ready-to-use workflow
    • Output from the Detect QIAseq RNAscan Fusions workflow
    • Interpretation of the Detect QIAseq RNAscan Fusions results
  • Targeted RNAscan tools detailed description
    • Detect Fusion Genes
    • Refine Fusion Genes
    • Annotate Fusions with Known Fusion Information
    • QC for RNAscan Panels
    • Import Known Fusion Information Track
• Targeted RNA
  • The Quantify QIAseq RNA Expression ready-to-use workflow
    • Output from the Quantify QIAseq RNA Expression workflow
  • Targeted RNA tools detailed description
    • The Quantify QIAseq RNA tool
• UPX 3'
  • Demultiplex QIAseq UPX 3' reads
  • The Quantify QIAseq UPX 3' ready-to-use workflow
    • Output from the Quantify QIAseq UPX 3' workflow
• Targeted Methyl
  • The Detect QIAseq Methylation ready-to-use workflow
    • Output from the Detect QIAseq Methylation workflow
    • Finding differentially methylated regions
• QCI Interpret Integration
  • Configuration of the Upload to QCI Interpret tool
  • The Upload to QCI Interpret tool
• QIAseq miRNA Analysis
  • QIAseq miRNA Quantification
    • QIAseq miRNA Quantification outputs
  • QIAseq miRNA Differential Expression
  • QIAseq miRNA Expert Tools
    • Create UMI Reads for miRNA
    • Quantify miRNA
    • Annotate with RNAcentral Accession Numbers
    • Create Combined miRNA Report
• QIAGEN GeneRead DNA Panel Analysis
  • The QIAGEN GeneRead Panel Analysis workflow
    • Output from the QIAGEN GeneRead Panel Analysis
  • Trim Primers and their Dimers of Mapped Reads
• Ready-to-use workflows descriptions and guidelines
  • General Workflow
  • Somatic Cancer
  • Hereditary Disease
• Getting started
  • Import sequencing data
  • Prepare Raw Data
• Whole genome sequencing (WGS)
  • General Workflows (WGS)
    • Annotate Variants (WGS)
    • Identify Known Variants in One Sample (WGS)
  • Somatic Cancer (WGS)
    • Filter Somatic Variants (WGS)
    • Identify Somatic Variants from Tumor Normal Pair (WGS)
    • Identify Variants (WGS)
  • Hereditary Disease (WGS)
    • Filter Causal Variants (WGS-HD)
    • Identify Causal Inherited Variants in Family of Four (WGS)
    • Identify Causal Inherited Variants in Trio (WGS)
    • Identify Rare Disease Causing Mutations in Family of Four (WGS)
    • Identify Rare Disease Causing Mutations in Trio (WGS)
    • Identify Variants (WGS-HD)
• Whole exome sequencing (WES)
  • General Workflows (WES)
    • Annotate Variants (WES)
    • Identify Known Variants in One Sample (WES)
  • Somatic Cancer (WES)
    • Filter Somatic Variants (WES)
    • Identify Somatic Variants from Tumor Normal Pair (WES)
    • Identify Variants (WES)
    • Identify and Annotate Variants (WES)
  • Hereditary Disease (WES)
    • Filter Causal Variants (WES-HD)
    • Identify Causal Inherited Variants in Family of Four (WES)
    • Identify Causal Inherited Variants in Trio (WES)
    • Identify Rare Disease Causing Mutations in Family of Four (WES)
    • Identify Rare Disease Causing Mutations in Trio (WES)
    • Identify Variants (WES-HD)
    • Identify and Annotate Variants (WES-HD)
• Targeted amplicon sequencing (TAS)
  • General Workflows (TAS)
    • Annotate Variants (TAS)
    • Identify Known Variants in One Sample (TAS)
  • Somatic Cancer (TAS)
    • Filter Somatic Variants (TAS)
    • Identify Somatic Variants from Tumor Normal Pair (TAS)
    • Identify Variants (TAS)
    • Identify and Annotate Variants (TAS)
  • Hereditary Disease (TAS)
    • Filter Causal Variants (TAS-HD)
    • Identify Causal Inherited Variants in Family of Four (TAS)
    • Identify Causal Inherited Variants in Trio (TAS)
    • Identify Rare Disease Causing Mutations in Family of Four (TAS)
    • Identify Rare Disease Causing Mutations in Trio (TAS)
    • Identify Variants (TAS-HD)
    • Identify and Annotate Variants (TAS-HD)
• Whole Transcriptome Sequencing (WTS)
  • Annotate Variants (WTS)
  • Compare Variants in DNA and RNA
  • Identify Candidate Variants and Genes from Tumor Normal Pair
  • Identify Variants and Add Expression Values
  • Identify and Annotate Differentially Expressed Genes and Pathways
• Batching workflows
• Appendices
  • Legacy tools
    • Trim Primers of Mapped Single Reads
    • Trim Primers of Mapped Paired End Reads
  • Install and uninstall plugins
    • Install
    • Uninstall
• Bibliography

Identify and Annotate Differentially Expressed Genes and Pathways

The Identify and Annotate Differentially Expressed Genes and Pathways compares genes expression in different groups of samples and performs a gene ontology (GO) enrichment analysis on the differentially expressed genes to identify affected pathways. The workflow takes as input Gene Expression (GE) or Transcript Expression (TE) tracks that were generated using the RNA-Seq analysis tool. The samples must be associated to a metadata table.

To run the ready-to-use workflow:

        Toolbox | Ready-to-Use Workflows | Whole Transcriptome Sequencing () | Identify and Annotate Differentially Expressed Genes and Pathways ()

1. Double-click on the Identify and Annotate Differentially Expressed Genes and Pathways ready-to-use workflow to start the analysis. If you are connected to a server, you will first be asked where you would like to run the analysis.

2. Next, you will be asked to select the samples to analyze (figure 16.32). You can select several GE tracks or TE tracks generated by the RNA-Seq analysis tool, but not a combination of both.

   
   Figure 16.32: Select the GE or TE tracks to analyze.

3. Then select the reference data set that should be used to annotate variants (figure 16.33).

   
   Figure 16.33: Choose the relevant reference data set to annotate.

4. In the Differential Expression for RNA-Seq dialog, you can set up the experimental design associated with the data (figure 16.34):
   • Choose the metadata table that was associated to the GE or TE tracks used in the previous step.
   • Choose the factor (must be one of the metadata category) that should be used to test for differential expression.
   • It is possible to specify confounding factors, i.e., factors that are not of primary interest, but may affect gene expression.
   • The Comparisons panel determines the number and type of statistical comparison tracks output by the workflow (see http://resources.qiagenbioinformatics.com/manuals/clcgenomicsworkbench/current/index.php?manual=Output_Differential_Expression_tools.html for more details).

   
   Figure 16.34: Specify the experimental design desired for running the workflow.

5. In the last wizard step you can check the selected settings by clicking on the button labeled Preview All Parameters. In the Preview All Parameters wizard you can only check the settings, and if you wish to make changes you have to use the Previous button from the wizard to edit parameters in the relevant windows.

6. Choose to Save your results and click on the button labeled Finish.

The following outputs are generated:

1. PCA for RNA-Seq plot () Projects a high-dimensional dataset (where the number of dimensions equals the number of genes or transcripts) onto two or three dimensions.
2. Statistical Comparison () The information can be accessed in two different ways:
   • Open as a track, hold shift and hover over a feature. A tooltip will appear with information about gene name, results of statistical tests, and expression values.
   • Open the track in table format by clicking on the table icon in the lower left side of the View Area.
3. Track List Differentially Expressed Genes and Pathways () A collection of tracks presented together. Shows the human reference sequence, annotation tracks for genes, coding regions CDS, mRNA, and statistical comparison tracks (see figure 16.35).
4. Heat Map for RNA-Seq () A two dimensional heat map of expression values. Each column corresponds to one sample, and each row corresponds to a feature (a gene or a transcript). The samples and features are both hierarchically clustered.
5. Venn Diagram () To compare the overlap of differentially expressed genes or transcripts in two or more statistical comparison tracks.
6. Expression Browser () To inspect gene and transcript expression level counts and statistics for many samples at the same time.
7. GO Enrichment Analysis () A table showing the results of the GO enrichment analysis. The table includes GO terms, a description of the affected function/pathway, the number of genes in each function/pathway, the number of affected genes within the function/pathway, and p-values.

Figure 16.35: The Track List allows comparison of the expression comparison tracks with the reference sequence and different annotation tracks.

Please refer to the relevant sections of the http://resources.qiagenbioinformatics.com/manuals/clcgenomicsworkbench/current/index.php?manual=RNA_Seq_Analysis.html for additional information on the different output mentioned above.

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