Bioinformatics and Omics Data Analysis Course

Lesson 1 • Biological Databases and Resources

Surveys primary sequence, annotation, and pathway databases. Students gain practical navigation skills for NCBI, Ensembl, and UniProt.

Lesson 2 • Introduction to Omics Disciplines

Defines genomics, transcriptomics, proteomics, and metabolomics. Establishes the biological rationale for multi-omics integration.

Lesson 3 • Command-Line Essentials for Bioinformatics

Introduces Linux shell navigation, file manipulation, and scripting basics. These skills underpin every computational workflow in the course.

Lesson 4 • Python and R for Omics Analysis

Establishes programming competency in Python and R for data manipulation and visualization. Students write scripts to parse and summarize omics datasets.

Lesson 1 • Heuristic Alignment and BLAST

Covers BLAST algorithm mechanics and parameter tuning for large-scale searches. Students perform database searches and interpret E-values and bit scores.

Lesson 2 • Short-Read Genome Assembly

Introduces de Bruijn graph assembly for short reads using SPAdes and Velvet. Students assemble a bacterial genome and evaluate assembly statistics.

Lesson 3 • Long-Read and Hybrid Assembly

Covers Nanopore and PacBio long-read assembly with Flye and Canu. Hybrid strategies combining short and long reads improve contiguity.

Lesson 4 • Pairwise Sequence Alignment Algorithms

Explains Needleman-Wunsch and Smith-Waterman algorithms with scoring matrices. Provides the algorithmic foundation for all alignment-based analyses.

Lesson 5 • Genome Annotation

Applies ab initio and evidence-based annotation using AUGUSTUS and Prokka. Annotated genomes are the reference for all downstream omics analyses.

Lesson 1 • Sequencing Technologies Overview

Compares Illumina, Nanopore, and PacBio platforms by read length, error rate, and throughput. Platform choice directly shapes downstream processing decisions.

Lesson 2 • Post-Alignment Processing and QC

Covers base quality score recalibration and coverage analysis using GATK and Mosdepth. These steps ensure alignment files meet variant-calling quality standards.

Lesson 3 • Quality Control of Raw Reads

Uses FastQC and MultiQC to assess per-base quality, adapter content, and duplication. QC metrics guide trimming decisions and flag failed libraries.

Lesson 4 • Read Trimming and Filtering

Applies Trimmomatic and Fastp to remove adapters and low-quality bases. Clean reads reduce mapping artifacts and improve variant calling accuracy.

Lesson 1 • Structural Variant Detection

Detects deletions, duplications, inversions, and translocations using Manta and LUMPY. Structural variants often have larger phenotypic effects than SNPs.

Lesson 2 • Variant Annotation and Prioritization

Annotates variants with functional impact using ANNOVAR and VEP. Prioritization frameworks reduce thousands of variants to clinically relevant candidates.

Lesson 3 • SNP and Indel Calling with GATK

Applies GATK HaplotypeCaller and GenotypeGVCFs for germline variant discovery. Students follow best-practice workflows to generate high-confidence VCF files.

Lesson 4 • Copy Number Variation Analysis

Quantifies CNVs from read-depth signals using CNVkit and GATK CNV. CNV profiles are essential for cancer genome characterization.

Lesson 1 • Differential Expression Analysis

Applies DESeq2 and edgeR to identify statistically significant expression changes. Students interpret fold changes, p-values, and FDR-adjusted results.

Lesson 2 • Functional Enrichment Analysis

Performs GO and KEGG pathway enrichment on DEG lists using clusterProfiler. Enrichment analysis translates gene lists into biological process narratives.

Lesson 3 • Alternative Splicing and Isoform Analysis

Detects differential splicing events using rMATS and quantifies isoforms with StringTie. Splicing variation adds regulatory complexity beyond gene-level expression.

Lesson 4 • Read Alignment and Quantification

Aligns RNA-seq reads with STAR and quantifies transcripts using featureCounts and Salmon. Accurate quantification is the foundation of differential expression analysis.

Lesson 1 • ChIP-Seq Data Processing

Processes ChIP-seq reads through alignment, peak calling with MACS2, and QC. ChIP-seq maps protein-DNA interactions genome-wide.

Lesson 2 • Motif Discovery and Annotation

Identifies transcription factor binding motifs in peaks using HOMER and MEME-ChIP. Motif analysis links regulatory regions to specific transcription factors.

Lesson 3 • Integrating Epigenomics with Transcriptomics

Combines ChIP-seq, ATAC-seq, and RNA-seq to build regulatory models using deepTools and R. Integration reveals how chromatin state drives expression changes.

Lesson 4 • ATAC-Seq Chromatin Accessibility

Analyzes ATAC-seq data to map open chromatin regions using HMMRATAC and deepTools. Accessible regions indicate active regulatory elements.

Lesson 1 • Differential Protein Abundance Analysis

Applies limma and Perseus for statistical testing of protein abundance differences. Results are filtered by fold change and FDR thresholds for biological relevance.

Lesson 2 • Metabolic Pathway and Network Analysis

Maps identified metabolites to KEGG and HMDB pathways using MetaboAnalyst. Network analysis reveals perturbed metabolic modules.

Lesson 3 • Mass Spectrometry Fundamentals

Explains ionization, mass analyzers, and fragmentation relevant to omics workflows. Understanding instrument output is essential for correct data interpretation.

Lesson 4 • Metabolomics Data Processing

Processes LC-MS metabolomics data with XCMS for peak detection and alignment. Accurate feature extraction is the prerequisite for metabolite identification.

Lesson 1 • Deep Learning Applications in Omics

Introduces autoencoders and graph neural networks for omics representation learning. Deep models capture non-linear patterns beyond classical statistical methods.

Lesson 2 • Principles of Multi-Omics Integration

Introduces early, late, and mixed integration frameworks and their trade-offs. Choosing the right strategy depends on data availability and biological question.

Lesson 3 • Supervised Machine Learning for Omics

Trains random forest, SVM, and elastic net classifiers on omics feature matrices. Students evaluate models with cross-validation and interpret feature importance.

Lesson 4 • Biomarker Discovery and Validation

Applies multi-omics models to discover and validate disease biomarkers. Students assess biomarker robustness using independent cohorts and statistical frameworks.

Lesson 5 • Dimensionality Reduction Methods

Applies PCA, UMAP, and MOFA for multi-omics dimensionality reduction and visualization. Reduced representations reveal sample clustering and latent biological factors.