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Course Outline
Foundational Concepts & Biological Data Architecture
- Core bioinformatics domains: genomics, transcriptomics, proteomics, and structural biology
- Data formats and standards: FASTA, GenBank, EMBL, PDB, FASTQ, and tabular metadata
- Database ecosystems: centralized repositories, API access, and data integration strategies
- Algorithmic thinking in biology: how computational models represent biological molecules and interactions
- Practical lab: Database navigation, format conversion, and metadata extraction exercises with live quizzes
Sequence Alignments & Homology Mapping
- Principles of sequence alignment: global vs. local, substitution matrices (BLOSUM, PAM), and gap penalties
- Multiple sequence alignment workflows: Clustal Omega, MUSCLE, and progressive alignment strategies
- Aligning and visualizing results: Jalview, alignment scoring, conservation analysis, and motif identification
- Practical lab: Aligning coding and non-coding sequences, interpreting conservation patterns, and validating alignment quality
BLAST & Its Applications
- BLAST algorithm mechanics: seed-and-extend, heuristic search, and statistical significance (E-value, bit score)
- BLAST variants: nucleotide, protein, tblastn, megablast, and PSI-BLAST for iterative discovery
- Translating BLAST outputs: identifying homologs, inferring function, and mapping to functional domains
- Practical lab: Running targeted BLAST searches, filtering results, extracting functional annotations, and concept validation quizzes
Translation Tools & Codon Analysis
- Genetic code translation: ORF finding, start/stop codon recognition, and frame detection
- Codon usage bias, GC content, and mRNA stability implications for expression systems
- Translation optimization: codon adaptation indices, restriction site avoidance, and synthetic gene design principles
- Practical lab: ORF prediction, codon bias analysis, and translation optimization exercises with alignment validation
Primer Designing & Experimental Planning
- Primer design fundamentals: length, Tm, GC clamp, dimer/hairpin avoidance, and amplicon size constraints
- Primer evaluation metrics: specificity scoring, cross-reactivity screening, and secondary structure prediction
- Software workflows: Primer3, OligoAnalyzer, and in silico PCR validation against reference genomes
- Practical lab: Designing targeted primers for a given gene, evaluating performance metrics, and troubleshooting common design failures
Epitope Prediction & Immunoinformatics Workflows
- Types of epitopes: linear vs. conformational, B-cell vs. T-cell epitopes, and MHC binding prediction
- Prediction algorithms: NetMHC, BepiPred, IEDB tool integration, and score interpretation thresholds
- Translating predictions into experimental validation: peptide synthesis, binding assays, and antibody development pipelines
- Practical lab: Submitting sequences to epitope prediction servers, filtering high-confidence hits, and mapping epitope clusters to protein domains
Secondary Structure Prediction & Folding Dynamics
- Protein structure levels and folding principles: hydrogen bonding, hydrophobic collapse, and β-sheet/α-helix formation
- Prediction methodologies: Chou-Fasman, GOR, neural network-based predictors, and template-free modeling
- Interpreting output: confidence scores, region-level flexibility, and functional domain mapping
- Practical lab: Running structure predictors on target proteins, visualizing secondary structure elements, and correlating predictions with experimental data
Phylogenetic Analysis & Evolutionary Insights
- Tree construction principles: distance-based, maximum parsimony, maximum likelihood, and Bayesian methods
- Alignment-to-tree pipelines: masking, trimming, substitution models, and bootstrapping for confidence estimation
- Tree visualization and annotation: rooting, clade interpretation, outgroup selection, and functional trait mapping
- Practical lab: Building a phylogenetic tree from aligned sequences, evaluating bootstrap support, and annotating clades with biological metadata
Integrated Workflows, Troubleshooting & Capstone Application
- Pipeline design: chaining tools, managing dependencies, and automating repetitive bioinformatics tasks
- Common pitfalls: database version drift, parameter misconfiguration, overfitting predictions, and cross-referencing errors
- Algorithm evaluation: recognizing tool limitations, when to switch predictors, and validating computational results against wet-lab data
- Capstone: Participants select a biological question, retrieve data, run a targeted analysis pipeline, interpret results, and present findings with troubleshooting documentation and tool selection rationale
- Open review, concept reinforcement, and resource distribution for continued independent study
Requirements
Fundamental biological knowledge regarding proteins, RNA, and DNA.
21 Hours
Testimonials (2)
practical applications from simple to complex
TUDOR DAMIAN - Institutul National de Sanatate Publica
Course - Basics of Bioinformatics
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