TranscriptomeForge is a modular and reproducible bulk RNA-seq analysis workflow built with R, Bioconductor, DESeq2, and Python/Streamlit.
The current implementation demonstrates an end-to-end transcriptomic analysis of TCGA Breast Invasive Carcinoma (TCGA-BRCA) samples, covering data acquisition, preprocessing, quality control, differential expression analysis, gene annotation, functional enrichment, visualization, automated HTML reporting, and interactive result exploration.
TranscriptomeForge follows this workflow:
TCGA-BRCA RNA-seq Data
↓
Data Acquisition
↓
Count Matrix Preparation
↓
Low-count Gene Filtering
↓
Variance-Stabilizing Transformation
↓
Quality Control
├── Library Size
├── Principal Component Analysis
└── Sample Correlation
↓
DESeq2 Differential Expression
├── Upregulated Genes
└── Downregulated Genes
↓
Gene Annotation
↓
Functional Enrichment
├── GO Biological Process
├── GO Cellular Component
├── GO Molecular Function
└── KEGG Pathways
↓
Biological Visualization
↓
Automated HTML Report
↓
Streamlit Dashboard
TranscriptomeForge was developed to:
- Build a reproducible bulk RNA-seq analysis workflow.
- Retrieve and prepare TCGA RNA-seq data.
- Perform systematic transcriptomic quality control.
- Identify significantly differentially expressed genes.
- Annotate Ensembl gene identifiers with gene symbols and Entrez IDs.
- Perform GO and KEGG functional enrichment.
- Generate publication-oriented visualizations.
- Produce structured result tables.
- Automatically generate an HTML analysis report.
- Provide interactive result exploration through Streamlit.
The demonstration analysis uses RNA-seq data from:
The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA)
| Group | Samples |
|---|---|
| Primary tumor | 10 |
| Solid-tissue normal | 10 |
| Total | 20 |
- Organism: Homo sapiens
- Reference genome: GRCh38 / hg38
- Data type: Gene Expression Quantification
- Workflow: STAR - Counts
Large raw TCGA datasets and serialized intermediate objects are excluded from GitHub using .gitignore.
| Step | Script | Function |
|---|---|---|
| 1 | scripts/00_setup.R |
Project and package setup |
| 2 | scripts/01_download_tcga.R |
TCGA-BRCA data acquisition |
| 3 | scripts/02_prepare_data.R |
Count preparation and filtering |
| 4 | scripts/03_quality_control.R |
QC, PCA, VST, sample correlation |
| 5 | scripts/04_differential_expression.R |
DESeq2 differential expression |
| 6 | scripts/05_annotate_genes.R |
Gene annotation |
| 7 | scripts/05_visualization.R |
Heatmaps and gene-expression plots |
| 8 | scripts/06_enrichment_analysis.R |
GO and KEGG enrichment |
| 9 | scripts/07_generate_report.R |
Automated HTML reporting |
| 10 | app.py |
Interactive Streamlit dashboard |
| Analysis | Result |
|---|---|
| Genes before filtering | 60,660 |
| Genes retained after filtering | 23,117 |
| Genes removed | 37,543 |
| Retention percentage | 38.11% |
| Upregulated genes | 3,690 |
| Downregulated genes | 3,527 |
| Not significant | 15,900 |
| Total significant DEGs | 7,217 |
| Gene symbols mapped | 18,958 |
| Entrez IDs mapped | 18,958 |
| Significant GO terms | 1,502 |
| Significant KEGG pathways | 50 |
Differential-expression significance criteria:
Adjusted p-value < 0.05
|log2FoldChange| > 1
Quality-control analysis includes:
- Library-size assessment
- Detected-gene counts
- Variance-stabilizing transformation
- Principal component analysis
- Sample-to-sample correlation
Differential expression was performed using DESeq2 for:
Tumor vs Normal
Representative result files:
results/differential_expression/TCGA_BRCA_all_DE_results.csv
results/differential_expression/TCGA_BRCA_upregulated_genes.csv
results/differential_expression/TCGA_BRCA_downregulated_genes.csv
Ensembl identifiers were cleaned and mapped using org.Hs.eg.db.
Annotation includes:
- Ensembl ID
- Gene symbol
- Entrez ID
- Gene name
- log2 fold change
- adjusted p-value
- significance category
Representative annotated genes include:
- CLEC3B
- LYVE1
- COL10A1
- CD209
- PDE2A
- GDF10
- HSPB7
- MMP11
The heatmap shows strong expression differences between tumor and normal samples for the most significant genes.
Representative genes include:
- COL10A1
- MMP11
- CLEC3B
- LYVE1
- GDF10
- HSPB7
Functional enrichment was performed separately for upregulated and downregulated genes using clusterProfiler.
| Analysis | Significant terms |
|---|---|
| GO BP upregulated | 141 |
| GO CC upregulated | 43 |
| GO MF upregulated | 16 |
| GO BP downregulated | 1,062 |
| GO CC downregulated | 93 |
| GO MF downregulated | 147 |
| Analysis | Significant pathways |
|---|---|
| KEGG upregulated | 6 |
| KEGG downregulated | 44 |
Major enriched processes include:
- Nuclear division
- Chromosome segregation
- Organelle fission
- DNA replication
- Cell-cycle checkpoint signaling
- Meiotic cell-cycle processes
Major enriched processes include:
- Angiogenesis
- Blood circulation
- Vascular development
- Muscle-system processes
- Chemotaxis
- G protein-coupled receptor signaling
Major pathways include:
- Cell cycle
- Fanconi anemia pathway
- Viral carcinogenesis
- Neutrophil extracellular trap formation
- Systemic lupus erythematosus
- Alcoholism
Some KEGG disease labels arise because they share genes involved in chromatin regulation, DNA damage, immune processes, and proliferation. They should not be interpreted as direct evidence of those diseases in the samples.
Major pathways include:
- PPAR signaling
- cAMP signaling
- Calcium signaling
- Hormone signaling
- Complement and coagulation cascades
- Cytokine-cytokine receptor interaction
- Retinol metabolism
- Lipid metabolism
The tumor samples showed strong transcriptional activation of biological programs related to:
- cell-cycle progression,
- chromosome segregation,
- nuclear division,
- DNA replication,
- mitotic checkpoint regulation,
- and DNA-damage responses.
Several highly upregulated genes, including COL10A1, MMP11, and related extracellular-matrix genes, are associated with tumor-stromal interactions and tissue remodeling.
Downregulated genes were enriched for angiogenesis, vascular development, blood circulation, lipid metabolism, hormone signaling, and immune-related pathways.
Because this implementation uses a relatively small subset of TCGA-BRCA samples, the findings should be interpreted as a reproducible workflow demonstration rather than a population-level clinical study.
TranscriptomeForge includes an interactive Streamlit dashboard with the following sections:
- Overview
- Quality Control
- Differential Expression
- Functional Enrichment
- Key Gene Expression
- Final HTML Report
Run the dashboard with:
streamlit run app.pyThen open:
http://localhost:8501
TranscriptomeForge/
│
├── R/
│ ├── de_functions.R
│ ├── download_tcga.R
│ ├── enrichment_functions.R
│ ├── load_data.R
│ ├── plot_functions.R
│ ├── qc_functions.R
│ └── utils.R
│
├── config/
│ └── config.yaml
│
├── data/
│ ├── metadata/
│ ├── processed/
│ └── raw/
│
├── reports/
│ └── TCGA_BRCA_report.Rmd
│
├── results/
│ ├── differential_expression/
│ ├── enrichment/
│ ├── figures/
│ ├── qc/
│ └── tables/
│
├── scripts/
│ ├── 00_setup.R
│ ├── 01_download_tcga.R
│ ├── 02_prepare_data.R
│ ├── 03_quality_control.R
│ ├── 04_differential_expression.R
│ ├── 05_annotate_genes.R
│ ├── 05_visualization.R
│ ├── 06_enrichment_analysis.R
│ └── 07_generate_report.R
│
├── app.py
├── requirements.txt
├── requirements_R.txt
├── TranscriptomeForge.Rproj
├── LICENSE
└── README.md
Clone the repository:
git clone https://github.com/Pratik-2002-ux/TranscriptomeForge.git
cd TranscriptomeForgepip install -r requirements.txtRequired R and Bioconductor packages are listed in:
requirements_R.txt
Important packages include:
- TCGAbiolinks
- SummarizedExperiment
- DESeq2
- AnnotationDbi
- org.Hs.eg.db
- clusterProfiler
- enrichplot
- ggplot2
- pheatmap
- rmarkdown
- knitr
- yaml
Run the scripts sequentially inside R or RStudio:
source("scripts/00_setup.R")
source("scripts/01_download_tcga.R")
source("scripts/02_prepare_data.R")
source("scripts/03_quality_control.R")
source("scripts/04_differential_expression.R")
source("scripts/05_annotate_genes.R")
source("scripts/05_visualization.R")
source("scripts/06_enrichment_analysis.R")
source("scripts/07_generate_report.R")
source()is an R command and should not be pasted directly into a zsh/bash terminal.
Individual R scripts can also be executed from a system terminal using Rscript.
Example:
Rscript scripts/03_quality_control.RGenerated results are organized under:
results/
├── differential_expression/
├── enrichment/
├── figures/
├── qc/
└── tables/
The automated HTML report is generated locally at:
output/html/TCGA_BRCA_analysis_report.html
Generated HTML files are excluded from Git tracking.
- TCGAbiolinks
- DESeq2
- SummarizedExperiment
- AnnotationDbi
- org.Hs.eg.db
- clusterProfiler
- enrichplot
- ggplot2
- pheatmap
- rmarkdown
- knitr
- Streamlit
- pandas
- Pillow
- TCGA
- GDC
- Gene Ontology
- KEGG
- Git
- GitHub
- RStudio
- VS Code
TranscriptomeForge separates:
- source code,
- project configuration,
- metadata,
- intermediate data,
- quality-control outputs,
- statistical results,
- figures,
- reports,
- and application components.
Large raw TCGA datasets and serialized intermediate R objects are excluded through .gitignore.
This keeps the repository lightweight while retaining the scripts, metadata, representative results, and documentation required to reproduce the workflow.
The current implementation uses:
- 10 tumor samples
- 10 normal samples
This is intended as a reproducible portfolio and workflow demonstration.
The results should not be interpreted as definitive clinical conclusions.
Results may depend on:
- sample selection,
- filtering criteria,
- differential-expression thresholds,
- software versions,
- annotation database versions,
- and pathway database versions.
TranscriptomeForge is intended for research and educational use and is not intended for clinical diagnosis.
Potential extensions include:
- Complete TCGA-BRCA cohort analysis
- Additional TCGA cancer types
- Molecular subtype analysis
- Gene Set Enrichment Analysis
- Survival analysis
- Immune-cell deconvolution
- WGCNA
- Pathway-activity scoring
- Machine-learning classification
- Multi-omics integration
- Docker/container support
- Nextflow or Snakemake integration
- Automated testing
- Cloud deployment
Pratik Ramchandra Chaudhari
M.Tech Biomedical Engineering
National Institute of Technology Rourkela
Bioinformatics • Computational Biology • Transcriptomics • Genomics • Biological Data Analysis
TranscriptomeForge is distributed under the MIT License.
See the LICENSE file for details.
If you use TranscriptomeForge in academic or research work, please cite this GitHub repository.
A permanent software citation can be associated with a future versioned and archived release.
TranscriptomeForge v1.0 — Completed
From RNA-seq counts to reproducible biological interpretation.