Welcome to BioPipeline — your bioinformatics home base.

What You Get

• Structured Course: Linux basics → R/Python → RNA-seq → Single-cell → WES/WGS, step by step

• Real Datasets: Each module comes with actual GEO datasets for hands-on practice

• Community Q&A: Ask questions anytime — mentors and peers are here to help

• Resource Updates: Latest tool recommendations, SCI paper bioinformatics method walkthroughs

Recommended Learning Path

1. Start with the Linux and R fundamentals modules in the Bioinformatics Course

2. Pick the analysis type relevant to your thesis (transcriptomics / genomics / single-cell)

3. Post questions in the community — include error screenshots for faster help

Let's get your thesis done! 💪

After 3 years of bioinformatics consulting and helping 50+ grad students finish their theses, here are the most common mistakes I see:

1. Installing Seurat/DESeq2 before learning Linux

Don't jump straight into analysis. Spend one week learning the command line and conda environment management — it'll save you 3 months down the road.

2. Using Excel for 100K-row expression matrices

Excel caps at 1,048,576 rows, and it silently converts gene names to dates (MARCH1 → March 1st). Use R's data.table or Python's pandas instead.

3. Copy-pasting parameters without checking your own data

Every dataset is different. PCA plot looks off? Check for batch effects first. Too few DEGs? Review your group design and sample size.

4. Pretty plots with no biological meaning

Anyone can make volcano plots and heatmaps. What matters is whether your enrichment analysis tells a coherent story. Reviewers care about logic, not aesthetics.

5. Can't write the Methods section

Many students finish the analysis but can't clearly describe the software, parameters, and thresholds they used. Start documenting from step one.

If any of this sounds familiar, BioPipeline can help — beginner-friendly courses to learn it yourself, or hand off the entire analysis to us.