Fundamental Skills in Bioinformatics Course

Lesson 1 • Setting Up a Bioinformatics Environment

Guides installation of command-line tools, package managers, and virtual environments. Ensures every student has a reproducible workspace before tackling computational exercises.

Lesson 2 • Major Biological Databases

Introduces primary repositories such as NCBI, UniProt, and Ensembl. Students gain practical skills for querying, retrieving, and citing database records.

Lesson 3 • What Is Bioinformatics

Defines bioinformatics as the intersection of biology, computer science, and statistics. Establishes the discipline's scope and motivates computational approaches to biological questions.

Lesson 4 • File Formats in Bioinformatics

Covers FASTA, FASTQ, GFF, VCF, and related formats essential for data exchange. Prepares students to handle raw data files in all subsequent analytical chapters.

Lesson 1 • Python Basics for Biologists

Covers variables, data types, loops, and functions using biological examples. Provides the scripting foundation required for all computational analyses in the course.

Lesson 2 • Data Handling with Pandas

Introduces DataFrames for tabular genomic and expression data manipulation. Enables efficient filtering, merging, and summarizing of large biological datasets.

Lesson 3 • Bash Scripting and Pipeline Automation

Teaches shell scripting to chain bioinformatics tools into reproducible pipelines. Reduces manual steps and prepares students for workflow management in later chapters.

Lesson 4 • Manipulating Biological Sequences in Python

Uses Biopython to parse, search, and transform sequence records programmatically. Directly applies Python skills to FASTA and GenBank files introduced in Chapter 1.

Lesson 1 • Alignment-Based Functional Annotation

Uses alignment results to infer gene function, orthologs, and conserved domains. Integrates similarity search outputs with database annotations from Chapter 1.

Lesson 2 • Principles of Sequence Alignment

Explains global and local alignment algorithms, scoring matrices, and gap penalties. Provides the theoretical basis for evaluating all alignment outputs in this chapter.

Lesson 3 • BLAST and Similarity Searching

Covers BLAST variants, statistical significance, and database selection strategies. Students run searches programmatically and parse results using Biopython BLAST parsers.

Lesson 4 • Profile and Hidden Markov Models

Explains profile HMMs for sensitive remote homology detection using HMMER. Builds on MSA concepts to enable database-scale protein family searches.

Lesson 1 • Variant Annotation and Interpretation

Annotates variants with functional consequences using ANNOVAR and SnpEff. Links variant effects to gene models and population frequency databases.

Lesson 2 • Variant Calling and Genotyping

Calls SNPs and indels from BAM files using GATK HaplotypeCaller and FreeBayes. Produces VCF files annotated with genotype likelihoods and quality filters.

Lesson 3 • Read Quality Control and Trimming

Applies FastQC and Trimmomatic to assess and improve raw read quality. Ensures clean input data for accurate alignment and variant calling in subsequent sections.

Lesson 4 • NGS Technology and Data Overview

Surveys short-read and long-read sequencing platforms and their output characteristics. Contextualizes quality metrics and error profiles that drive downstream processing decisions.

Lesson 1 • RNA-Seq Experimental Design

Covers replication, batch effects, and library preparation choices that affect analysis. Proper design decisions made here directly impact statistical power in later sections.

Lesson 2 • Functional Enrichment Analysis

Performs GO and KEGG pathway enrichment on differentially expressed gene sets. Translates statistical gene lists into interpretable biological processes and pathways.

Lesson 3 • Normalization and Quality Assessment

Applies TPM, RPKM, and DESeq2 normalization to correct for sequencing depth and composition. Diagnostic plots reveal outlier samples before statistical testing.

Lesson 4 • Differential Expression Analysis

Uses DESeq2 and edgeR to identify statistically significant expression changes. Applies multiple testing correction and fold-change thresholds to produce reliable gene lists.

Lesson 1 • Protein Structure Prediction

Applies homology modeling and AlphaFold2 to predict three-dimensional protein structures. Evaluates model quality using DOPE scores, Ramachandran plots, and pLDDT metrics.

Lesson 2 • Protein Structure Fundamentals

Reviews primary through quaternary structure levels and introduces PDB file format. Provides structural vocabulary needed to interpret prediction and docking results.

Lesson 3 • Protein-Protein Interaction Networks

Constructs and analyzes PPI networks using STRING data and Cytoscape visualization. Identifies hub proteins and functional modules relevant to disease and pathway biology.

Lesson 4 • Molecular Docking Basics

Introduces ligand-receptor docking using AutoDock Vina to predict binding poses. Connects structural knowledge to drug discovery and functional annotation applications.

Lesson 1 • Comparative Genomics Applications

Identifies core and pan-genomes, gene family expansions, and horizontal gene transfer events. Integrates phylogenetic and genomic data to answer evolutionary and functional questions.

Lesson 2 • Bayesian Phylogenetic Inference

Introduces Bayesian methods and MCMC sampling using BEAST and MrBayes. Produces time-calibrated trees with posterior probability support for evolutionary hypotheses.

Lesson 3 • Phylogenetic Tree Construction

Applies distance-based, maximum parsimony, and maximum likelihood methods to build trees. Uses IQ-TREE and RAxML to produce bootstrap-supported phylogenies from MSA input.

Lesson 4 • Whole-Genome Alignment and Synteny

Aligns whole genomes with MUMmer and identifies syntenic blocks across species. Reveals large-scale genomic rearrangements and conserved chromosomal segments.

Lesson 1 • Containerization with Docker and Singularity

Packages bioinformatics environments into Docker and Singularity containers for portability. Eliminates dependency conflicts that undermine reproducibility across computing systems.

Lesson 2 • High-Performance Computing Basics

Submits and monitors jobs on HPC clusters using SLURM and PBS schedulers. Scales computationally intensive analyses from Chapter 4 and Chapter 5 to cluster resources.

Lesson 3 • Workflow Management with Snakemake

Builds rule-based pipelines in Snakemake to automate multi-step genomic analyses. Applies Bash and Python skills from Chapter 2 within a scalable workflow framework.

Lesson 4 • Principles of Reproducible Research

Defines reproducibility, replicability, and provenance in computational biology contexts. Motivates systematic documentation practices that underpin all professional bioinformatics work.