End-to-end RNA-Seq workflows for reproducible transcriptomics analysis, interpretation, and reporting.
The RNA-Seq System is the flagship implementation of the Omics Systems Architecture. It demonstrates how raw sequencing reads can be transformed into biologically meaningful insights through a structured and reproducible analytical workflow.
Biological Focus
RNA-Seq enables the study of:
gene expression patterns
differential expression
transcript abundance
biological pathways
functional interpretation
The goal is not simply to identify statistically significant genes, but to support transparent and defensible biological conclusions.
Why RNA-Seq?
RNA-Seq serves as the flagship Omics System Build because it contains many of the core analytical concepts shared across modern sequencing-based studies, including quality assessment, feature generation, statistical modeling, interpretation, and reproducible reporting.
As a result, the RNA-Seq System provides a foundation for understanding the broader Omics Systems framework.
Relationship to the Omics Systems Architecture
All Omics System Builds share a common analytical foundation.
Biological Question
↓
Experimental Design
↓
Data Generation
↓
Omics Data Processing
↓
Quality Control
↓
Feature Generation
↓
Domain-Specific Analysis
↓
Statistical Inference
↓
Biological Interpretation
↓
Reproducible Reporting
The RNA-Seq System extends this architecture by transforming sequencing reads into gene expression measurements that can be explored, statistically evaluated, biologically interpreted, and reported within a reproducible analytical framework.
RNA-Seq System Architecture
Code
flowchart TD A[FASTQ Files] B[Quality Assessment] C[Read Processing] D[Quantification or Alignment] E[Count Matrix Generation] F[Exploratory Analysis] G[Differential Expression] H[Functional Interpretation] I[Reproducible Reporting] A --> B B --> C C --> D D --> E E --> F F --> G G --> H H --> I
flowchart TD
A[FASTQ Files]
B[Quality Assessment]
C[Read Processing]
D[Quantification or Alignment]
E[Count Matrix Generation]
F[Exploratory Analysis]
G[Differential Expression]
H[Functional Interpretation]
I[Reproducible Reporting]
A --> B
B --> C
C --> D
D --> E
E --> F
F --> G
G --> H
H --> I
System Components
Quality Assessment
Quality assessment evaluates sequencing quality, adapter contamination, library complexity, and overall data integrity before downstream analysis begins.
Read Processing
Read processing prepares sequencing reads for quantification or alignment through activities such as adapter removal, quality trimming, filtering, and organization of analysis-ready FASTQ files.
Quantification and Alignment
Quantification and alignment transform sequencing reads into transcript-level or gene-level abundance estimates.
Typical tools include:
Salmon
STAR
featureCounts
Count Matrix Generation
The count matrix is the central analytical object for downstream RNA-Seq analysis. It connects samples, genes, expression measurements, and experimental metadata.
Exploratory Analysis
Exploratory analysis evaluates sample-level structure before formal differential expression testing.
Common analyses include:
sample clustering
principal component analysis (PCA)
outlier detection
batch effect assessment
sample metadata validation
Differential Expression Analysis
Differential expression analysis identifies genes associated with experimental conditions or biological contrasts.
Typical tools include:
DESeq2
Functional Interpretation
Functional interpretation translates statistical findings into biological understanding through pathway analysis, gene set analysis, and biological context evaluation.
Reproducible Reporting
Reproducible reporting connects workflow decisions, code, outputs, interpretation, and conclusions in a transparent analytical document.
Typical tools include:
Quarto
GitHub
reproducible computational environments
Core Technologies
The RNA-Seq System may integrate:
FastQC
MultiQC
Salmon
STAR
featureCounts
DESeq2
Quarto
GitHub
These technologies support the workflow, but the primary focus of the RNA-Seq System is analytical reasoning, interpretation, and reproducibility.
Expected Outputs
A complete RNA-Seq System should produce:
quality control summaries
processed or validated sequencing inputs
transcript-level or gene-level quantification outputs
count matrices
sample-level exploratory plots
differential expression results
functional interpretation summaries
reproducible analytical reports
Status
Active flagship build
The RNA-Seq System serves as the primary reference implementation of the Omics Systems Architecture and provides the foundation for understanding how the broader ecosystem approaches reproducible biological data analysis.
The RNA-Seq System illustrates the Omics Systems approach to transcriptomics analysis.
Rather than treating quality control, quantification, differential expression, interpretation, and reporting as separate activities, the system connects them into a single analytical framework.