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Ingenio Diagnostics
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Ingenio Diagnostics is a proud member of Genesis Drug Discovery & Development (GD3), a fully integrated CRO providing services to support drug discovery programs of our clients from target discovery through IND filing and managing Phase I-III clinical trials.

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Functional Profiling

Differential expression analysis of changing and treated datasets can lead to key clues and conclusions for a number of different experiments and research. Our RNA sequencing capabilities allow us to assess these transcriptome-level differences as well as report back vital information, such as alternative splicing, gene fusions and mutations. Specialized analysis such as xenograft model separation and mutational burden are available upon request.

Capabilities include:

  • mRNA Sequencing
  • Total RNA Sequencing
  • Exome Capture RNA Sequencing
  • Targeted Panel RNA Sequencing (Custom or Ordered)
  • Small RNA Sequencing
  • Gene Expression Profiling

Sample Submissions Guidelines

Total RNA

  • Optical Density (260/280): 1.8-2.2
  • RNA Integrity Number (RIN): ≥4.0
  • Minimum Quantity: 500 ng
  • Best Result Quantities: At least 40 µl of >50 ng/µl solution
  • Buffer: Nuclease-free water
  • Shipped with dry ice
Frozen Virus

Ingenio NGS Run Analysis Report

Section 1: Workflow Summary

Samples were processed according to the workflow below. PolyA-tail selection was used to separate extracted mRNA for reverse transcription to cDNA. Samples were ligated with Illumina TruSeq RNA CD barcode adapters and assessed for quality via Agilent TapeStation and Thermofisher Qubit prior to sequencing.

Workflow Summary Illustration
Figure 1.1: RNA Sequencing Process.

Section 2: Sequencing Overview

Following the wetlab workflow, samples were sequenced on an Illumina NextSeq550 instrument and demultiplexed into Fastq files using Local Run Manager bcl2fastq v.1.8.4. Fastq files were assessed for read type, quantity and quality before proceeding with additional downstream analysis.

Table 2.1: Sample Sequencing Statistics.
Sample RNA Type Prevalence Illustration
Figure 2.1: Breakdown of RNA types, as well as a measure of contamination rate (rRNA).

Section 3: Sample Quality Review

FastQC v0.11.5 was run to assess sample quality and produce reviewable sample run metrics. Presented below are charts summarizing sample Phred quality score over the length of the reads, the general distribution of Phred quality scores per sample and the general distribution of GC content % per sample.

Per Base Sequence Quality Illustration
Figure 3.1: Per Base Sequence Quality.
Per Sequence Quality Scores Illustration
Figure 3.2: Per Sequence Quality Scores.
Per Sequence GC Content Illustration
Figure 3.3: Per Sequence GC Content.

Section 4: Alignment Overview

Using CutAdapt v3.2, sample reads with low quality or length were removed and the remaining reads were trimmed for Illumina read-end issues. Trimmed reads were aligned to the Mus musculus GRCm39 reference genome with STAR v.2.7.8a. Resulting BAM (Binary Alignment Map) files were assessed for alignment metrics.

Table 4.1: Alignment Statistics.
Read QC per sample Illustration
Figure 4.1: Read QC per sample.
Mapping rate per sample Illustration
Figure 4.2: Mapping rate per sample.

Section 5: Differential Expression Overview

Following alignment review, read counts on a gene level were assembled using featureCounts v1.6. Sample data was grouped according to the provided specifications and compared for differences in expression with DESeq2 v1.2.4.0. Only genes with p.adj < 0.05 and | log2FoldChange |> 1 were considered significant. Additional enrichment/pathway analyses were also performed to assess larger-scale functional changes.

Ingenio NGS Differential Expression Analysis Report

Section 1: Experimental Design

The samples below were included as a part of this analysis.

Table 1.1: Assignment of samples to treatment groups.

Section 2: Sample Similarity

Differences within and between comparisons groups were assessed to judge duplicate/group integrity and strength of comparison.

Comparison Samples Illustration
Figure 2.1: Principle component analysis of comparison samples. The two components of each sample that represent the most variance were graphed to simplify and display the difference between the samples.

Section 3: Differential Expression Analysis

Following alignment, treatment grouping reads were counted with featureCounts v1.6 and analyzed for differences in expression with DESeq2 v1.2.4.0. Displayed below are the summarized results of the differential expression analysis.

Table 3.1: DESeq2 output table. Generally, genes with padj < 0.05 can be considered sufficiently differentially expressed.
Volcano Plot Illustration
Figure 3.1: Volcano plot. Log adjusted p-values and fold change were graphed and significant genes were marked as upregulated or downregulated.
Heatmap Plot Illustration
Figure 3.2: Heatmap plot of top 30 differentially expressed genes, per sample.

Section 4: GO Analysis

Differentially expressed genes were organized into Gene Ontology groups and tested for significance as a whole. Displayed below are the significant results of the GO analysis.

Table 4.1: GO Output Table.
Go Pathways Illustration
Figure 4.1: Graph of top 30 altered GO pathways.

Section 5: KEGG Analysis

Differentially expresssed genes were clustered by pathway via the KEGG database and tested for significance per pathway. Displayed below are the significant results of the KEGG analysis.

Table 5.1: KEGG Output Table.
KEGG Pathways Illustration
Figure 5.1: Graph of all altered KEGG pathways.