Classic RNA-Seq Workflow

Status: ✅ Complete / Fundamental Base | Tech Stack: Nextflow, Bash Scripting, R/Python

Problem Solved / Scientific Goal

The goal is to establish and reproduce the standard RNA-Seq workflow. This project serves as a methodological foundation on which more complex architectures, like Digital RNA-Seq, have been built.

The challenge here is ensuring that every step (QC → Alignment → Quantification) is executable in a robust and reproducible manner. It's about establishing the *protocol* for transcriptomic data.

Architecture Implemented

The architecture is a linear, modular, and essential workflow:

  1. QC & Preprocessing: Initial quality control on FASTQ files.
  2. Alignment and Quantification: Use of canonical tools (e.g., STAR/Salmon) to align data and count (feature count matrix).
  3. Statistical Analysis: After obtaining the count matrix, we proceed to the true bioinformatics phase in R/Python for differential gene expression (DEG) analysis.

The use of Nextflow ensures this linear flow is executed robustly, managing dependencies between steps.

Results & Takeaways

  1. Fundamental Principle: The RNA-Seq pipeline is the *benchmark* for every transcriptomic analysis. Understanding this flow allows you to diagnose more complex problems, knowing exactly which step might have failed.
  2. Core Modularization: Each single step (QC, Alignment, Quantification) must live in a separate and well-tested module to allow future *upgrades* without breaking the rest of the chain.

Tech Stack & Tools Used

  • Nextflow Workflow Management
  • Docker Containerization
    • STAR/Salmon (Alignment) R/Python (Statistical Analysis) Bash Scripting

GitHub Repository

Check out the code: rnaseq on GitHub