Applied Bioinformatics
A two-day, hands-on workshop that takes you from raw FASTQ files to a finished variant and RNA-seq analysis. Eight modules, nine Jupyter notebooks, runnable shell pipelines, and a synthetic chr22 capstone dataset.
8
Modules
9
Notebooks
5
Pipeline scripts
1
Capstone dataset
What you'll leave with
- ▸Run a complete short-read pipeline: QC → trim → align → call → annotate → interpret.
- ▸Read and manipulate FASTA, FASTQ, SAM/BAM, VCF, BED, and GFF3 files with confidence.
- ▸Drive command-line tools (BWA, SAMtools, GATK, Trimmomatic, fastp) from reproducible shell and Python scripts.
- ▸Run a differential expression analysis end-to-end with DESeq2.
- ▸Diagnose pipeline failures from FastQC and MultiQC reports.
Who it's for
- ·PhD students and postdocs entering bioinformatics from a wet-lab or stats background.
- ·Data scientists rotating into a genomics team.
- ·Research software engineers who need to read and own existing pipelines.
What you need
- ▸Just a web browser. Every module — the shell, the notebooks, the visualizations — runs right here on this site. Nothing to install, no accounts, no setup before you start.
- ·No prior bioinformatics experience required.
- ·Reading-level Python helps, but the live notebooks walk you through every cell.
- ·Optional, for later: want to run the full pipelines on your own hardware? The conda_env.yml in Downloads rebuilds the toolset locally.
Day 1 · Raw data to aligned reads
Day 1 slides ↗Linux CLI for Bioinformatics
Pipes, grep, awk, and the shell scripting that powers every genomics pipeline.
Sequence Data Formats
FASTA, FASTQ, SAM/BAM, VCF, BED, GFF — the file formats genomics runs on.
Quality Control + Read Trimming
FastQC, MultiQC, Trimmomatic, fastp — diagnose and clean raw reads.
Read Alignment to Reference Genome
BWA-MEM2, SAMtools, and the mechanics of mapping short reads to a reference.
Day 2 · From aligned reads to biology
Day 2 slides ↗BAM Processing + Variant Calling
Picard, GATK4 HaplotypeCaller, BQSR, and variant filtering strategies.
RNA-seq: Quantification + Differential Expression
HISAT2, featureCounts/Salmon, and DESeq2 for differential gene expression.
Visualization + Pathway Analysis
IGV, matplotlib, Biopython, and pathway-level interpretation.
Capstone Project
End-to-end mini-analysis on a synthetic dataset: QC → align → call → annotate → interpret.
Downloads
Everything you need to learn is already on the module pages — these are extras for going deeper, teaching the workshop, or rebuilding the pipelines on your own machine.
Book this workshop for your lab or team
Public cohorts, private corporate training, and self-paced licenses available. Custom modules on single-cell, ATAC-seq, or long-read sequencing on request.