Interleukin-17A (IL-17A) is increasingly recognized for its role in osteoarthritis (OA), contributing to inflammation and joint degeneration. A recent study investigates how IL-17A and related cytokines affect gene expression in primary human chondrocytes and synovial fibroblasts derived from patients with end-stage knee OA. Public access to this dataset offers a unique opportunity to reproduce and explore the findings using modern, no-code bioinformatics tools. This analysis demonstrates how g.nome® from Almaden Genomics and Omics Playground from BigOmics Analytics facilitate streamlined, reproducible RNA-seq reanalysis.
Dataset Overview: Cytokine Response in OA-Derived Cells
The dataset originates from the publication titled "Interleukin-17A causes osteoarthritis-like transcriptional changes in human osteoarthritis-derived chondrocytes and synovial fibroblasts in vitro" (GEO Accession: GSE171952). The study includes 48 RNA-seq samples representing two cell types—chondrocytes and synovial fibroblasts—isolated from six OA patients. Each cell type was treated with IL-17A, IL-17AF, IL-17F, or control medium, revealing cytokine-specific gene expression profiles relevant to OA pathophysiology.
Data Retrieval: Automated Import of Public RNA-seq FASTQs
Using the SRA Run Selector and project accession number SRP314543, the associated SRR accession numbers are identified. These are compiled in a text file (SRR_Acc_List.txt) and uploaded to g.nome.
Within the g.nome interface, the Fetch Public FASTQs workflow automates the download and formatting process. After pasting in the SRR IDs and specifying parameters (ftp method and rnaseq as the pipeline), the workflow generates a sample sheet formatted for use in downstream analysis. This step eliminates the need for manual file handling or scripting.
Preprocessing: Quality Control and Count Generation
The RNA-seq Preprocessing workflow is initiated using the sample sheet generated previously. The reference genome is set to GRCh38, and the pipeline executes standard preprocessing steps.
Output files include:
- multiqc_report.html – Aggregated quality metrics from FASTQC, Cutadapt, Picard, and STAR.
- merged.gene_counts.csv – Gene-level count data across all samples.
- template_samples.csv – A metadata template prefilled with sample identifiers, editable for downstream grouping and annotation.
This automated preprocessing workflow ensures consistent data quality while enabling users to remain focused on biological interpretation rather than technical execution.
Downstream Analysis: Interactive Visualization with Omics Playground
To begin tertiary analysis, the RNA-seq Analyze feature in g.nome is used. The organism is set to Human, and users upload both the merged count file and the annotated sample metadata (osteo_samples.csv). Analysis is launched directly into Omics Playground.
Within the Omics Playground interface, users select analysis parameters, including:
- Normalization method
- Group comparisons for differential expression
- Optional enrichment, clustering, and biomarker analyses
The resulting dashboard provides an interactive environment for exploring biological signals within the dataset—without requiring any coding or command-line tools.
Conclusion: Reproducible, Accessible Bioinformatics Workflows
This use case illustrates how publicly available data can be reanalyzed using modern platforms designed for reproducibility and accessibility. With g.nome and Omics Playground, complex RNA-seq pipelines are transformed into guided workflows with intuitive interfaces, enabling users to move efficiently from raw sequencing files to biological insight.
To view the complete analysis and explore the results in more detail, visit the companion blog post here.
From Workflow to Visualization — Seamlessly
With the power of g.nome and Omics Playground, turning your RNA-seq outputs into interactive, insight-rich visualizations has never been easier. No uploads. No configuration headaches. Just a few clicks.
Click the “Analyze in Omics Playground” button directly from g.nome, and a guided pop-up experience walks you through the final steps of data preparation—right inside the platform.
Step-by-step guidance includes:
1. Review Counts
Preview and confirm the expression matrix automatically generated from your workflow output.
2. Review Samples
Check sample names and ensure your metadata is correctly assigned.
3. Create Comparisons
Define the conditions you want to compare—or use the Auto-detect option to generate suggested groupings.
4. QC & Batch Correction
Fine-tune your data quality with tools for normalization, outlier removal, batch-effect correction, and more.
5. Compute
Name your dataset, add a description, and launch the final analysis.
Once configured, Omics Playground opens instantly—ready with powerful, interactive visualizations including PCA plots, clustering, differential expression, gene set enrichment, and biomarker discovery.
Learn more about BigOmics.