onedimkurt

onedimkurt / rnaseq-hoxa1-knockdown-pipeline

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A complete, reproducible bulk RNA-seq pipeline from raw FASTQ files to differential gene expression — built on a real published dataset, with every command tested and verified on macOS.

bioinformatics-pipeline bioinformatics-scripts python-script rnaseq-pipeline shell-scripts
14
6
100% credibility
Found Mar 09, 2026 at 10 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
AI Summary

A detailed, reproducible tutorial guiding users through bulk RNA-seq analysis of a published HOXA1 knockdown dataset in HeLa cells, from raw data processing to differential expression visualization.

How It Works

1
📚 Discover the Guide

You find a complete step-by-step guide to analyze how genes behave in treated cells using real experiment data.

2
🛠️ Prepare Your Workspace

You create a dedicated folder on your computer and set up the safe space needed for the analysis.

3
📥 Gather Experiment Data

You download the raw files from the cell experiment to start working with real biological data.

4
🧹 Clean and Check Data

You inspect the data quality and trim away any messy parts to make it ready for deeper insights.

5
🔗 Map to Genes

You line up the cleaned data against the human gene blueprint and count activity for each gene.

6
📊 Find Changes and Visualize

You compare activity between treated and normal cells, spotting differences and creating colorful charts like scatter plots and heatmaps.

🎉 See Gene Discoveries

You get clear results showing which genes turned up or down due to the cell treatment, ready to explore or share.

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AI-Generated Review

What is rnaseq-hoxa1-knockdown-pipeline?

This is a complete GitHub tutorial for bulk RNA-seq analysis, taking raw FASTQ files from a real published HOXA1 knockdown dataset (GSE37704) all the way to differential gene expression results and plots like PCA, volcano, and heatmaps. Download the complete GitHub repo, set up a conda environment, and run copy-paste bash commands plus a Jupyter notebook to get publication-ready outputs—no guesswork on tool chains like HISAT2 alignment, featureCounts, or PyDESeq2 stats. Built for macOS with 16-24GB RAM, it handles paired-end data end-to-end.

Why is it gaining traction?

Unlike scattered GitHub complete projects or half-baked scripts, this stands out as a fully tested, command-by-command pipeline verified on a real dataset, with MultiQC reports and expected metrics (97% alignment, 77-83% assignment rates). Developers grab it for the zero-friction reproducibility—conda env export, gitignore for big files, and phase-by-phase commits make it a plug-and-play complete GitHub guide. The Jupyter analysis delivers instant viz without R hassles.

Who should use this?

Bioinformaticians or wet-lab researchers dipping into bulk RNA-seq who hate reinventing QC, trimming, or DE workflows. PhD students analyzing their first FASTQ files from GEO/SRA, or computational biologists validating pipelines on human cell line data like HeLa HOXA1 knockdown. Skip if you're on high-throughput clusters needing STAR or custom scaling.

Verdict

Solid starter for hands-on RNA-seq learning—docs are flawless, but 10 stars and 1.0% credibility score mean it's early-stage; fork and contribute to mature it. Use if you want a complete, battle-tested tutorial today.

(198 words)

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