Bioinformatician Course

Lesson 1 • Command-Line Fundamentals

Teaches Linux shell navigation, file manipulation, and scripting basics. These skills underpin every tool and pipeline used throughout the course.

Lesson 2 • Introduction to Biological Databases

Surveys major sequence, structure, and annotation repositories. Students gain hands-on retrieval skills essential for every subsequent analysis chapter.

Lesson 3 • Python and R for Bioinformatics

Introduces Python and R syntax, data structures, and bioinformatics libraries. Provides the scripting foundation for custom analyses in later chapters.

Lesson 4 • Biology Essentials for Bioinformaticians

Covers DNA, RNA, protein structure, and the central dogma as computational targets. Establishes biological context needed to interpret downstream analytical results.

Lesson 1 • Multiple Sequence Alignment

Introduces progressive and iterative MSA methods including ClustalW, MUSCLE, and MAFFT. Accurate MSA is prerequisite for phylogenetics and motif discovery.

Lesson 2 • Pairwise Alignment Algorithms

Covers Needleman-Wunsch global and Smith-Waterman local alignment algorithms. Students implement and compare both to understand trade-offs for different biological questions.

Lesson 3 • BLAST and Heuristic Search

Teaches BLAST variants, parameter tuning, and result interpretation. Heuristic search is the most widely used tool in daily bioinformatics practice.

Lesson 4 • Motif Discovery and Pattern Matching

Covers position weight matrices, MEME suite, and regular expression-based pattern search. Motif analysis connects sequence features to functional annotation.

Lesson 1 • Pipeline Automation with Workflow Managers

Introduces Snakemake and Nextflow for reproducible, scalable NGS pipelines. Automation reduces manual errors and enables reuse across projects.

Lesson 2 • Read Quality Control

Teaches FastQC metrics, adapter trimming with Trimmomatic and Cutadapt, and quality filtering. QC failures at this stage propagate errors through the entire pipeline.

Lesson 3 • NGS Technology Overview

Surveys short-read, long-read, and single-cell sequencing platforms and their error profiles. Platform awareness guides tool selection in every downstream step.

Lesson 4 • Post-Alignment Processing

Covers SAMtools sorting, indexing, duplicate marking, and base quality score recalibration. These steps are mandatory before variant calling or quantification.

Lesson 1 • Variant Annotation and Prioritization

Covers ANNOVAR, VEP, and SnpEff for functional annotation and population frequency lookup. Annotation transforms raw variants into biologically interpretable findings.

Lesson 2 • Copy Number Variation Analysis

Teaches read-depth normalization, segmentation, and CNV calling with CNVkit. CNV detection completes the genomic alteration landscape alongside SNVs and SVs.

Lesson 3 • Somatic Variant Calling

Teaches tumor-normal paired calling with Mutect2 and somatic filtering strategies. Somatic variants drive cancer genomics and precision oncology applications.

Lesson 4 • Germline Variant Calling

Covers GATK HaplotypeCaller, variant genotyping, and joint calling strategies. Germline calling is the foundation for population and disease genetics analyses.

Lesson 1 • Structural Genome Annotation

Covers ab initio prediction with AUGUSTUS, evidence-based annotation, and MAKER pipelines. Structural annotation defines the gene catalog for all functional analyses.

Lesson 2 • Repeat Masking and Genome Polishing

Teaches RepeatMasker, Medaka, and Pilon for repeat annotation and error correction. Masking and polishing improve downstream gene prediction accuracy.

Lesson 3 • Assembly Quality Assessment

Covers N50, BUSCO completeness, and QUAST metrics for evaluating assembly quality. Quality metrics guide iterative improvement before annotation.

Lesson 4 • Functional Annotation of Gene Models

Assigns function via homology search, InterPro domain scanning, and GO term mapping. Functional annotation links gene models to biological knowledge bases.

Lesson 1 • Alternative Splicing and Isoform Analysis

Introduces rMATS, SUPPA2, and long-read isoform sequencing for splicing quantification. Splicing variation adds a regulatory layer beyond gene-level expression.

Lesson 2 • Functional Enrichment Analysis

Covers GO term enrichment, KEGG pathway analysis, and GSEA. Enrichment analysis translates gene lists into biological process narratives.

Lesson 3 • Differential Expression Analysis

Teaches DESeq2 and edgeR normalization, dispersion estimation, and hypothesis testing. Differential expression is the primary deliverable of most RNA-seq projects.

Lesson 4 • Read Quantification Methods

Compares alignment-based counting with HTSeq and featureCounts against pseudo-alignment with Salmon and Kallisto. Quantification method affects downstream statistical results.

Lesson 1 • Molecular Evolution Analysis

Covers dN/dS ratio calculation, positive selection tests, and codon models with PAML. Selection analysis reveals genes under adaptive or purifying evolutionary pressure.

Lesson 2 • Whole-Genome Synteny Analysis

Introduces MCScan, MUMmer, and dot-plot visualization for genome-scale synteny. Synteny reveals chromosomal rearrangements and conserved regulatory blocks.

Lesson 3 • Phylogenetic Tree Construction

Teaches distance, parsimony, maximum likelihood, and Bayesian methods using IQ-TREE and MrBayes. Tree topology and branch lengths encode evolutionary history.

Lesson 4 • Pangenome Analysis

Covers core, accessory, and unique genome partitioning using Roary and Minigraph-Cactus. Pangenomics captures population-level genomic diversity beyond a single reference.

Lesson 1 • Deep Learning for Sequence Modeling

Introduces CNNs, RNNs, and transformer architectures for promoter, splice, and protein function prediction. Deep learning captures long-range sequence dependencies beyond classical methods.

Lesson 2 • Supervised Learning for Genomics

Applies random forests, SVMs, and gradient boosting to variant pathogenicity and splice site prediction. Supervised models learn labeled biological patterns for classification and regression.

Lesson 3 • Model Evaluation and Benchmarking

Covers ROC-AUC, precision-recall, calibration, and benchmark dataset design. Rigorous evaluation prevents overfitting and ensures models generalize to new biological data.

Lesson 4 • Unsupervised Learning and Clustering

Covers k-means, hierarchical clustering, and UMAP for expression and single-cell data. Unsupervised methods reveal hidden structure without requiring labeled training data.