Computer and Data Fundamentals for Biologists
Build a clear bioinformatics project layout, inspect compressed FASTQ files, and verify downloaded data with checksums and gzip integrity tests.
THE TUTORIAL LIBRARY
Explore bioinformatics.
From foundations to advanced analysis.
New here? Start with the foundations
Biological Data Formats: A Practical Field Guide
Identify what FASTA, FASTQ, SAM/BAM, VCF, GTF/GFF, and count matrices contain, then inspect their structure and limits before analysis.
Quality Control Fundamentals
A practical sequencing QC workflow: verify FASTQ files, inspect read and alignment metrics, investigate outliers, and document sample-level decisions without universal cutoffs.
Git and GitHub for Bioinformatics
Create a local Git repository, review commits and ignore rules, record code provenance, and decide how to share bioinformatics code and large data safely.
Python Fundamentals for Bioinformatics
Build a small Python workflow that reads FASTA records, validates a chosen DNA alphabet, and writes length and GC-fraction summaries to CSV.
R and Tidyverse Fundamentals
Practice reshaping a small gene-count table, checking sample identifiers, and producing a clearly labeled library-size QC table and plot.
Statistics for Bioinformatics
Plan a two-group bioinformatics comparison: identify independent biological replicates, inspect distributions, estimate a Welch mean difference with uncertainty, and control false discoveries across features.
Experimental Design and Batch Effects
Plan biological replication and balanced processing; validate sample metadata and decide when batch can be modeled.
Reference Genomes and Annotation Databases
Choose a compatible genome assembly and annotation release, validate their files and contigs, and record the exact references for reproducible analysis.
Data Visualization Fundamentals
Choose and interpret QC plots, PCA, UMAP, heatmaps, and volcano plots with explicit preprocessing and cautious biological conclusions.
Reproducible Project Structure
Build a bioinformatics project layout that separates inputs, code, and outputs, and records checksums, software versions, and run provenance.
Research Reporting and Interpretation
Document omics data provenance, software and reference versions, figure legends, and the limits of biological interpretation with a practical reporting workflow.
Introduction to Bioinformatics
Connect a biological question to suitable data and analysis methods, then plan reproducible checks and cautious interpretation.
Modern Bioinformatics Method Decision Guide
A conceptual decision guide for matching bioinformatics methods to study questions, data inputs, assumptions, and validation checks; it compares single-cell, spatial, isoform, trajectory, and donor-aware approaches without a dataset or runnable pipeline.
Command-Line Navigation for Bioinformatics Projects
Practice shell navigation and safe file handling by building a small bioinformatics project tree, checking paths, and distinguishing empty placeholders from compressed sequence data.
Command-line text processing for bioinformatics
Use grep, sed, cut, awk, sort, and seqkit to inspect and reshape FASTA, FASTQ, and tabular files while recognizing when line-based commands are unsafe.
Summarize FASTQ and Annotation Files with Bash
Write a Bash script that validates common four-line FASTQ and tab-separated annotation inputs, streams compressed files, and reports read-length or per-chromosome summaries.
Micromamba Environments for Bioinformatics
Install Micromamba, manage isolated bioinformatics environments, use Bioconda channels deliberately, export and test environment specifications, and diagnose common setup failures.
Connecting to HPC
Connect to an institutional HPC system with SSH, protect and register a key, transfer files with explicit local and remote paths, and diagnose common access problems.
Basic Slurm Commands
Inspect Slurm partitions and job states, submit a small batch smoke test, and diagnose common queue and log problems while adapting commands to your cluster's policies.
Writing Slurm Job Scripts
Write Slurm batch scripts for a cluster: request resources, direct logs, submit and monitor jobs, and choose between task launches and job arrays. Examples require adaptation to local cluster policy and configuration.
HPC Troubleshooting and Support
Diagnose Slurm job failures by reading accounting records and logs, checking environment and resource evidence, and preparing a privacy-conscious support request.
Reproducible bioinformatics workflows with Snakemake and Nextflow
Compare Snakemake and Nextflow workflow models, build synthetic file-copy examples in both tools, and review reproducibility checks for bioinformatics pipelines.
Docker and Apptainer for Bioinformatics
Compare Docker and Apptainer for bioinformatics, then trace a BWA mapping command through image selection, bind mounts, HPC execution, and reproducibility checks.
16S Amplicon Profiling and Prokka Genome Annotation
Choose between 16S rRNA amplicon profiling and prokaryotic genome annotation, then inspect the inputs, outputs, and limits of each workflow.
Metagenomic Assembly and Read Mapping
Walk through paired-end metagenomic assembly with metaSPAdes, optional host-read screening, read mapping with BWA and SAMtools, and cautious coverage interpretation.
Kraken2 and Bracken: A Shotgun Metagenomics Walkthrough
Classify paired shotgun reads with Kraken2, estimate rank-specific abundances with Bracken, and interpret both outputs in context of database and sample-preparation limits.
Metatranscriptomics: From Reads to Functional Profiles
Follow paired reads through QC, rRNA classification and host depletion, then interpret microbial functional profiles with attention to input contracts, normalization, reference limits and biological uncertainty.
Metatranscriptomic Pathway Analysis with HUMAnN 3
Use HUMAnN 3 to profile microbial gene families and MetaCyc pathways from quality-controlled metatranscriptomic reads, join sample tables, and interpret pathway abundance and coverage with appropriate caveats.
Evolutionary Phylogeny
Align homologous sequences, infer phylogenetic trees with IQ-TREE, MEGA-CC, or FastTree, and assess support, rooting, and model-dependent limits.
Phylogenomics with OrthoFinder
Prepare protein FASTA inputs for OrthoFinder, inspect orthogroup and species-tree results, and choose markers and downstream tree methods with their assumptions in view.
Whole Exome Sequencing (WES): Variant Calling & VAF
A practical single-sample WES variant-calling walkthrough covering alignment, GATK calling and filtering, reference-build liftover, VAF calculation, and interpretation of QC evidence and limitations.
scRNA-seq Basics
A practical introduction to single-cell RNA-seq quality control and clustering with parallel Seurat and Scanpy examples, including input checks and dataset-specific interpretation.
Choosing and evaluating scRNA-seq integration: Harmony, RPCA, and CCA
Reproduce a public PBMC PCA–Harmony comparison with paired LISI diagnostics, inspect a rare-population caveat, and keep RPCA/CCA claims clearly scoped.
Single-Cell RNA-seq Downstream Analysis
Choose and interpret downstream single-cell RNA-seq analyses: ligand–receptor inference, transcription-factor activity scoring, and clear visualizations, with attention to inputs, replicates, and limits.
Single-cell trajectory inference: PAGA, DPT, Slingshot and Monocle3
Compare PAGA with diffusion pseudotime in Scanpy and branching trajectory workflows in R. Choose a defensible root, inspect inferred lineages, and interpret pseudotime as an ordering rather than measured time.
Pseudobulk Differential Expression for scRNA-seq
A sample-level pseudobulk workflow for single-cell RNA-seq: aggregate raw counts by biological sample, fit a DESeq2 model, check diagnostics, and interpret the limits of the comparison.
Assessing scRNA-seq integration with LISI and silhouette
Use LISI and silhouette scores to assess batch mixing and cluster structure in single-cell RNA-seq embeddings, with practical cautions about labels, embeddings, and interpretation.
Visualizing single-cell data with R plotting packages
A practical guide to plotting Seurat single-cell data with SCpubr, dittoSeq, and ggplot2, including input checks, scale choices, sample composition, and interpretation.
TCR/BCR Repertoire Analysis
Walk through single-cell TCR repertoire summaries and Seurat annotation, with explicit clone-definition and interpretation limits.
Cell-Cell Communication
A cautious R walkthrough for ranking ligand–receptor hypotheses with LIANA, checking inputs, and deciding when CellChat pathway analysis adds value.
Choosing and Checking Cell-Type Annotation Methods
Run SingleR and Seurat label transfer on public PBMC data, inspect real disagreements and RNA markers, and reproduce the saved results with checked inputs.
AI-Assisted Cell-Type Annotation: Models, Limits, and Review
Compare CellTypist, CELLama, and SingleR by method and evidence. Run a CellTypist prediction on compatible normalized data, then review labels against markers, reference fit, and uncertainty.
Inferring Copy Number Variation (inferCNV)
Prepare inferCNV inputs, run its R workflow, inspect expression-derived copy-number patterns, and assess their limits.
Bulk RNA-seq cell-type deconvolution with MuSiC
Learn how MuSiC uses annotated single-cell RNA-seq references to estimate cell-type proportions in bulk RNA-seq, and how to check inputs, interpret estimates, and assess reference limitations.
Multi-Omics: CITE-seq & WNN Integration
Prepare matched RNA and antibody-derived tag count matrices, preprocess each assay in Seurat, build a weighted-neighbor graph, and interpret modality weights with appropriate caveats.
Spatial transcriptomics with R and Python: a Visium workflow
A practical Visium workflow in Seurat and Squidpy/Scanpy: load counts with tissue coordinates, normalize and visualize features, cluster expression profiles, and interpret spatial patterns with quality and platform limits in view.
Long-Read Sequencing: choosing a workflow
Compare PacBio HiFi and Oxford Nanopore long-read data, choose an analysis route for genome or transcript questions, and document the assumptions that shape QC, alignment, assembly, and isoform interpretation.
GitHub Education and Copilot Student for Bioinformatics
Apply for GitHub Education, activate Copilot Student if eligible, and practice reviewing AI-generated bioinformatics code with a synthetic TSV and explicit failure checks.
Using Cursor and Aider for Bioinformatics
Install Cursor and Aider, work in a version-controlled bioinformatics project, and review AI-proposed code changes before testing them.
Choosing and Safely Evaluating a Zotero MCP Server
A conceptual guide to selecting and safely evaluating a specific Zotero MCP server: inspect its access path, permissions, and data flow, then verify a read-only test against a disposable collection. No MCP server installation or live Zotero test is included.
NotebookLM for literature review: a source-grounded workflow
Plan a traceable NotebookLM literature review, check cited claims against source passages, and optionally connect an MCP client while accounting for third-party API and credential risks.
Deterministic Retrieval with gget
Use gget to retrieve Ensembl-linked gene metadata and sequence records, save the results, and assess what they can support.
Retrieve an AlphaFold DB model and assess pLDDT
Retrieve a precomputed AlphaFold DB model by UniProt accession, preserve the source-file provenance, and inspect per-residue pLDDT with a small Python workflow.
Evaluating External scGPT & Geneformer Embeddings
Use a standard-library evaluator to check externally generated scGPT or Geneformer embedding exports with donor-held-out kNN; first run a deliberately synthetic software check. This lesson includes no model output, inference, or biological accuracy claim.
Prompt Engineering for Seurat and Scanpy: A Reproducible Workflow
Build a precise prompt for single-cell analysis, review the resulting Seurat or Scanpy code, and check its assumptions, outputs, and statistical limitations.
Bounded Count-Matrix QC and CPM Workflows
Run deterministic, local RNA-seq count-matrix QC and optional CPM workflows in R or Python, with synthetic fixtures, provenance records, and human review; the separate optional Python provider demo is not needed for the core exercises.
Designing Auditable AI Workflows for Bioinformatics
Design an auditable AI-assisted bioinformatics workflow with explicit inputs, evidence provenance, bounded retries, human review gates, and failure paths.
What do the learning-format labels mean?
- Hands-on lab
- Follow a bounded exercise using supplied fixtures or a stated public-data route, then inspect its outputs.
- Conceptual guide
- Understand scientific choices, assumptions, and interpretation; runnable code is not the main outcome.
- Advanced template
- Adapt code and setup to compatible inputs and your own environment, then check the outputs.
- Reference
- Look up formats, commands, or troubleshooting checks.
These labels describe the learning format, not validation status. Not every biological workflow has been run end to end. Check the lesson’s prerequisites and outputs; a teaching example is not a validated research or clinical pipeline.
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