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All Tutorials
Step-by-step guides for life scientists learning NGS data analysis — no prior programming experience required. Pick a series and start from Part 1.
Most Popular
12 tutorials
Single-Cell RNA-seq
From FASTQ to cell clusters, trajectory analysis, cell-cell communication and beyond.
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27 tutorials
Bulk RNA-seq
The complete pipeline: QC, alignment, DEGs, visualization, pathways, and advanced methods.
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14 tutorials
Epigenetics
ChIP-seq, ATAC-seq, CUT&RUN, Hi-C, and DNA methylation analysis from scratch.
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10 tutorials
Genomics & WGS/WES
Whole genome/exome sequencing, variant calling, somatic mutations, CNVs, and GWAS.
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9 tutorials
Scientific Programming
HPC/Slurm, Docker, databases, and environment management for NGS workflows.
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Quick reads
Quick Tips
Focused, practical guides on specific tasks — when you just need the answer fast.
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Full Tutorial Index
🔬 Single-Cell RNA-seq
Complete Beginner’s Guide
- Part 1: From FASTQ to Count Matrix
- Part 2: Quality Control and Cell Filtering
- Part 3: Integration and Clustering
- Part 4: Cell Type Identification
- Part 5: Differential Expression, Proportion Testing & Pathway Analysis
- Part 6: Seurat and SingleCellExperiment Objects
- Part 7: Trajectory and Pseudotime Analysis (Monocle 3)
- Part 7-2: Trajectory Analysis Using Slingshot
- Part 8: PDX Models — Human-Mouse Mixed Samples
- Part 9: Cell-Cell Communication — CellChat Analysis
- Part 10: Cell-Cell Communication — NicheNet Analysis
- Part 11: Copy Number Variation Analysis — CopyKAT
- Part 12: Gene Co-expression Networks — hdWGCNA Analysis
🧬 Bulk RNA-seq
Core RNA-seq Workflow
- Part 1: Environment Setup
- Part 2: From FASTQ to Counts — Best Practices
- Part 3: From Count Table to DEGs — Best Practices
- Part 4: Publication-Ready Figures
- Part 5: From DEGs to Pathways — Best Practices
Statistical Methods & Best Practices
- Part 19: Understanding RNA-Seq Gene Expression Normalization
- Part 20: Comparing limma, DESeq2, and edgeR
- Part 21: Batch Effects & Covariates Adjustment
Co-expression Networks
- WGCNA: Gene Co-expression Network Analysis
- GENIE3: Gene Regulatory Network Analysis
- Clustering: Hierarchical and K-means Methods
- Master Regulator Analysis: RegEnrich and RTN
Clinical & Biological Applications
Transcript-Level Analysis
- Part 8: Alternative Splicing Analysis
- Part 9: RNA Editing Analysis
- Part 10: Isoform Analysis
- Part 11: Transcript-Level Alternative Splicing
Non-Coding RNA
- Part 13: Circular RNA-seq Analysis
- Part 13-2: Advanced CircRNA Detection with CIRI3
- Part 14: Small RNA-Seq Analysis
- Part 15: miRNA-seq Analysis
- Part 15-2: UMI-Based miRNA-Seq Analysis
Structural Variations & Special Applications
🧫 Epigenetics
ChIP-seq
- Part 1: From FASTQ to Peaks with HOMER
- Part 2: Visualizing ChIP-seq Data
- Part 3: Differential Binding Analysis and Motif Discovery
- Part 4: From FASTQ to Peaks with MACS2
- Part 5: Reproducibility with IDR Analysis
ATAC-seq
- Part 1: From FASTQ to Peaks
- Part 2: Differential Binding Analysis with DiffBind
- Part 3: Footprinting Analysis
- Part 4: ATAC-seq and RNA-seq Integration
CUT&RUN / CUT&Tag
Hi-C
DNA Methylation
🧪 Genomics & WGS/WES
Whole Genome / Exome Sequencing
- Part 1: From Raw Reads to High-Quality Variants (GATK)
- Part 2A: Matched Tumor-Normal Mutation Calling (Mutect2)
- Part 2B: Unmatched Sample Mutation Calling Strategies
- Part 3: Annotating SNVs and Mutations
- Part 4: Visualizing and Interpreting Somatic Mutations
- Part 5: Identifying Disease- or Patient-Specific Variants
- Part 6: Identifying Germline Copy Number Variants
- Part 6-2: Tumor Copy Number Variants with CNVkit
- Whole Exome Sequencing: Variants, Mutations, and CNVs
- GWAS: From Raw Variants to Disease-Associated Loci (PLINK)
💻 Scientific Programming
High Performance Computing
- HPC Job Submission: A Beginner’s Guide to Slurm
- HPC Data Management: Storage, Transfer, and Sharing
- Portable NGS Environments with Docker
- Single-Cell Analysis Environment with Pixi
- Run Jupyter Lab, RStudio, VS Code on HPC with Pixi
Databases & Data Access
⚡ Quick Tips
