Chapter 1: Introduction to Bioinformatics
- Lesson 1: What is Bioinformatics?
- Lesson 2: Historical Perspective and Applications
- Lesson 3: Importance of Bioinformatics in Modern Biology
- Lesson 4: Key Databases and Resources
- Lesson 5: Overview of the Course
Chapter 2: Biological Databases
- Lesson 1: Introduction to Biological Databases
- Lesson 2: Nucleotide Sequence Databases (GenBank, EMBL, DDBJ)
- Lesson 3: Protein Sequence Databases (UniProt, PDB)
- Lesson 4: Specialized Databases (KEGG, Pfam, Reactome)
- Lesson 5: Database Search Techniques
Chapter 3: Sequence Alignment and Analysis
- Lesson 1: Introduction to Sequence Alignment
- Lesson 2: Pairwise Sequence Alignment (Needleman-Wunsch, Smith-Waterman)
- Lesson 3: Multiple Sequence Alignment (ClustalW, MUSCLE)
- Lesson 4: Scoring Matrices (PAM, BLOSUM)
- Lesson 5: Practical Applications of Sequence Alignment
Chapter 4: Genome Sequencing and Assembly
- Lesson 1: DNA and RNA Sequencing Technologies
- Lesson 2: Whole Genome Sequencing (WGS) and Shotgun Sequencing
- Lesson 3: Genome Assembly Techniques (De Novo & Reference-Based)
- Lesson 4: Quality Control and Error Correction
- Lesson 5: Applications of Genome Sequencing
Chapter 5: Phylogenetics and Evolutionary Analysis
- Lesson 1: Basics of Molecular Evolution
- Lesson 2: Phylogenetic Tree Construction Methods (Neighbor-Joining, Maximum Likelihood)
- Lesson 3: Bootstrapping and Statistical Confidence
- Lesson 4: Comparative Genomics
- Lesson 5: Software for Phylogenetic Analysis (MEGA, PhyML)
Chapter 6: Structural Bioinformatics
- Lesson 1: Introduction to Protein Structure
- Lesson 2: Protein Structure Databases (PDB, SCOP, CATH)
- Lesson 3: Molecular Visualization Tools (PyMOL, Chimera)
- Lesson 4: Homology Modeling and Protein Folding
- Lesson 5: Applications in Drug Discovery
Chapter 7: Functional Genomics
- Lesson 1: Introduction to Functional Genomics
- Lesson 2: Transcriptomics and RNA-Seq Analysis
- Lesson 3: Gene Expression Databases (GEO, ArrayExpress)
- Lesson 4: DNA Microarrays and Functional Studies
- Lesson 5: Applications of Functional Genomics
Chapter 8: Proteomics and Metabolomics
- Lesson 1: Introduction to Proteomics
- Lesson 2: Mass Spectrometry in Proteomics
- Lesson 3: Protein-Protein Interactions and Networks
- Lesson 4: Basics of Metabolomics and Its Applications
- Lesson 5: Data Analysis Tools in Proteomics and Metabolomics
Chapter 9: Systems Biology and Network Analysis
- Lesson 1: Introduction to Systems Biology
- Lesson 2: Gene Regulatory Networks
- Lesson 3: Protein-Protein Interaction Networks
- Lesson 4: Metabolic Pathways and Flux Analysis
- Lesson 5: Applications of Systems Biology
Chapter 10: Computational Tools and Bioinformatics Programming
- Lesson 1: Introduction to Bioinformatics Tools
- Lesson 2: Basics of Python and R for Bioinformatics
- Lesson 3: Introduction to Libraries and Frameworks for Bioinformatics
- Lesson 4: Command Line Tools for Bioinformatics
- Lesson 5: Workflow Automation in Bioinformatics
Chapter 11: Galaxy | A Web-Based Platform for Bioinformatics
- Lesson 1: Introduction to Galaxy and Its Role in Bioinformatics
- Lesson 2: Setting Up and Accessing Galaxy: Local vs. Cloud-Based Instances
- Lesson 3: Galaxy Workflow System: Creating and Managing Workflows
- Lesson 4: Data Handling in Galaxy: Importing, Organizing, and Preprocessing
- Lesson 5: Using Galaxy for Genomic Analysis: Sequence Alignment and Variant Calling
- Lesson 6: RNA-Seq and ChIP-Seq Analysis in Galaxy
- Lesson 7: Installing and Managing Tools in Galaxy Tool Shed
- Lesson 8: Automating Analyses: Running Batch Jobs and Using Histories
- Lesson 9: Integrating Custom Scripts and External Tools into Galaxy
- Lesson 10: Case Studies: Practical Applications of Galaxy in Bioinformatics
Chapter 12: Bioconductor | R-Based Framework for Bioinformatics
- Lesson 1: Introduction to Bioconductor and Its Ecosystem
- Lesson 2: Installing and Setting Up Bioconductor in R
- Lesson 3: Working with Genomic Data in Bioconductor
- Lesson 4: Expression Analysis with Bioconductor Packages (e.g., limma, edgeR, DESeq2)
- Lesson 5: RNA-Seq Analysis and Visualization in Bioconductor
- Lesson 6: ChIP-Seq and Epigenomics Data Analysis
- Lesson 7: Pathway and Functional Enrichment Analysis using Bioconductor
- Lesson 8: Single-Cell RNA-Seq Analysis with Bioconductor
- Lesson 9: Integrating Bioconductor with Machine Learning and AI
- Lesson 10: Case Studies: Real-World Applications of Bioconductor
Chapter 13: BioPerl | Perl-Based Bioinformatics Toolkit
- Lesson 1: Introduction to BioPerl and Its Importance in Bioinformatics
- Lesson 2: Installing and Configuring BioPerl
- Lesson 3: Parsing and Manipulating Biological Sequence Data
- Lesson 4: Working with GenBank, FASTA, and Other Bioinformatics File Formats
- Lesson 5: Performing Sequence Alignments with BioPerl Modules
- Lesson 6: Analyzing Phylogenetic Data using BioPerl
- Lesson 7: Accessing and Querying Biological Databases using BioPerl
- Lesson 8: Next-Generation Sequencing (NGS) Data Processing in BioPerl
- Lesson 9: Automating Bioinformatics Pipelines with BioPerl
- Lesson 10: Case Studies and Advanced Applications of BioPerl
Chapter 14: BioPython | Python Toolkit for Bioinformatics
- Lesson 1: Introduction to BioPython and Its Applications
- Lesson 2: Installing and Setting Up BioPython
- Lesson 3: Working with Biological Sequences in BioPython
- Lesson 4: Parsing and Handling Sequence File Formats (FASTA, GenBank, etc.)
- Lesson 5: Performing Sequence Alignments using BioPython
- Lesson 6: Accessing Biological Databases and Fetching Sequences
- Lesson 7: Analyzing Phylogenetic Data and Evolutionary Trees
- Lesson 8: Structural Bioinformatics and Molecular Modeling with BioPython
- Lesson 9: Machine Learning and AI in Bioinformatics with BioPython
- Lesson 10: Case Studies: Real-World Use Cases of BioPython
Chapter 15: BioRuby | Ruby Library for Bioinformatics
- Lesson 1: Introduction to BioRuby and Its Applications in Bioinformatics
- Lesson 2: Installing and Configuring BioRuby
- Lesson 3: Handling Biological Sequences and File Formats with BioRuby
- Lesson 4: Performing Sequence Alignments and Phylogenetic Analysis
- Lesson 5: Fetching and Analyzing Data from Online Databases using BioRuby
- Lesson 6: Working with NGS Data and BioRuby Workflows
- Lesson 7: Protein Structure Analysis and Molecular Modeling in BioRuby
- Lesson 8: Developing Custom Bioinformatics Applications using BioRuby
- Lesson 9: Integrating BioRuby with Web Services and Cloud Computing
- Lesson 10: Case Studies and Advanced Applications of BioRuby
Chapter 16: BioJS | JavaScript Library for Bioinformatics Visualization
- Lesson 1: Introduction to BioJS and Its Role in Bioinformatics
- Lesson 2: Installing and Setting Up BioJS for Web Applications
- Lesson 3: Working with Biological Data Formats in BioJS
- Lesson 4: Sequence Visualization and Annotation using BioJS
- Lesson 5: Creating Interactive Phylogenetic Trees and Graphs
- Lesson 6: Genome Browsers and Structural Visualization with BioJS
- Lesson 7: Integrating BioJS with Other Bioinformatics Tools
- Lesson 8: Web-Based Bioinformatics Applications using BioJS
- Lesson 9: Advanced Customization and Plugin Development in BioJS
- Lesson 10: Case Studies: Real-World Applications of BioJS
Chapter 17: BLAST | Basic Local Alignment Search Tool
- Lesson 1: Introduction to BLAST and Its Importance in Bioinformatics
- Lesson 2: Understanding BLAST Algorithm and Scoring Systems
- Lesson 3: Performing Nucleotide and Protein Sequence Alignments
- Lesson 4: Interpreting BLAST Results and Statistical Significance
- Lesson 5: Using BLAST for Comparative Genomics and Evolutionary Studies
- Lesson 6: Running BLAST Locally and on Cloud-Based Servers
- Lesson 7: Customizing BLAST Searches and Filtering Results
- Lesson 8: Creating Custom BLAST Databases for Specialized Analyses
- Lesson 9: Automating BLAST Workflows Using Scripts and APIs
- Lesson 10: Case Studies: Real-World Applications of BLAST
Chapter 18: EMBOSS | European Molecular Biology Open Software Suite
- Lesson 1: Introduction to EMBOSS and Its Applications in Bioinformatics
- Lesson 2: Installing and Configuring EMBOSS for Local and Cloud Use
- Lesson 3: Sequence Analysis and Manipulation with EMBOSS Tools
- Lesson 4: Performing Multiple Sequence Alignments Using EMBOSS
- Lesson 5: Phylogenetic and Evolutionary Analysis with EMBOSS
- Lesson 6: Protein Structure and Function Prediction Using EMBOSS
- Lesson 7: Analyzing Restriction Enzymes and Motifs with EMBOSS
- Lesson 8: Working with Databases and Fetching Sequences Using EMBOSS
- Lesson 9: Integrating EMBOSS with Other Bioinformatics Pipelines
- Lesson 10: Case Studies: Real-World Applications of EMBOSS
Chapter 19: SAMtools | Toolkit for High-Throughput Sequence Analysis
- Lesson 1: Introduction to SAMtools and Its Role in NGS Analysis
- Lesson 2: Installing and Setting Up SAMtools for Sequence Processing
- Lesson 3: Understanding BAM, SAM, and CRAM File Formats
- Lesson 4: Sorting, Indexing, and Filtering Sequence Reads with SAMtools
- Lesson 5: Variant Calling and SNP Detection Using SAMtools
- Lesson 6: Quality Control and Read Depth Analysis with SAMtools
- Lesson 7: Handling Structural Variants and Genomic Rearrangements
- Lesson 8: Integrating SAMtools with Other Bioinformatics Pipelines
- Lesson 9: Automating High-Throughput Sequence Analysis with SAMtools
- Lesson 10: Case Studies: Practical Applications of SAMtools in Genomics
Chapter 20: Nextflow | Workflow Management System for Bioinformatics
- Lesson 1: Introduction to Nextflow and Its Importance in Bioinformatics
- Lesson 2: Installing and Configuring Nextflow for Workflow Automation
- Lesson 3: Writing Basic Nextflow Scripts and Understanding DSL Syntax
- Lesson 4: Managing Pipelines and Task Execution in Nextflow
- Lesson 5: Parallel Computing and Cluster Integration in Nextflow
- Lesson 6: Working with Containers: Docker and Singularity in Nextflow
- Lesson 7: Integrating Nextflow with Cloud-Based Platforms (AWS, GCP, Azure)
- Lesson 8: Debugging and Optimizing Bioinformatics Pipelines in Nextflow
- Lesson 9: Best Practices for Reproducible and Scalable Workflows
- Lesson 10: Case Studies: Real-World Applications of Nextflow in Bioinformatics
Chapter 1: Next-Generation Sequencing (NGS) Data Analysis
- Lesson 1: Overview of NGS Technologies
- Lesson 2: Preprocessing of Raw Sequencing Data
- Lesson 3: Alignment Algorithms (Bowtie, BWA, STAR)
- Lesson 4: Variant Calling and Annotation
- Lesson 5: RNA-Seq Data Analysis
Chapter 2: Computational Drug Discovery
- Lesson 1: Basics of Drug Discovery and Design
- Lesson 2: Molecular Docking and Virtual Screening
- Lesson 3: QSAR Models and Pharmacophore Mapping
- Lesson 4: Computer-Aided Drug Design (CADD) Tools
- Lesson 5: Case Studies in Drug Discovery
Chapter 3: Epigenomics and Chromatin Biology
- Lesson 1: DNA Methylation and Histone Modifications
- Lesson 2: ChIP-Seq and ATAC-Seq Data Analysis
- Lesson 3: Non-Coding RNAs and Their Roles
- Lesson 4: Epigenetic Data Visualization
- Lesson 5: Applications in Disease Research
Chapter 4: Metagenomics and Microbiome Analysis
- Lesson 1: Introduction to Metagenomics
- Lesson 2: Shotgun Metagenomics and 16S rRNA Sequencing
- Lesson 3: Functional Annotation of Microbiomes
- Lesson 4: Comparative Metagenomics
- Lesson 5: Host-Microbiome Interactions
Chapter 5: Structural Bioinformatics and Molecular Simulations
- Lesson 1: Protein Structure Prediction (AlphaFold, Rosetta)
- Lesson 2: Molecular Dynamics Simulations
- Lesson 3: Protein-Ligand Binding Studies
- Lesson 4: Advanced Homology Modeling Techniques
- Lesson 5: Structural Bioinformatics in Drug Design
Chapter 6: Pan-Genomics and Comparative Genomics
- Lesson 1: Concept of Pan-Genomes
- Lesson 2: Comparative Genomics Across Species
- Lesson 3: Identification of Core and Accessory Genes
- Lesson 4: Gene Gain and Loss Analysis
- Lesson 5: Practical Applications in Microbiology
Chapter 7: Machine Learning Applications in Bioinformatics
- Lesson 1: Introduction to Machine Learning in Bioinformatics
- Lesson 2: Feature Selection and Data Preprocessing
- Lesson 3: Dimensionality Reduction in Biological Data
- Lesson 4: Supervised and Unsupervised Learning in Genomics
- Lesson 5: Supervised Learning for Disease Prediction and Biomarker Discovery
- Lesson 6: Unsupervised Learning for Clustering Biological Data
- Lesson 7: Model Interpretability and Validation in Biological Data
- Lesson 8: Deep Learning in Genomics, Drug Discovery and Proteomics
- Lesson 9: Case Studies and Practical Applications
Chapter 8: Synthetic Biology and Genome Engineering
- Lesson 1: Introduction to Synthetic Biology
- Lesson 2: CRISPR-Cas Systems and Genome Editing
- Lesson 3: Computational Tools for Gene Design
- Lesson 4: Synthetic Circuit Design and Implementation
- Lesson 5: Applications in Medicine and Biotechnology
Chapter 9: Single-Cell Analysis Techniques
- Lesson 1: Introduction to Single-Cell Biology
- Lesson 2: Technologies for Single-Cell Sequencing (scRNA-seq, scATAC-seq, scDNA-seq)
- Lesson 3: Preprocessing and Quality Control of Single-Cell Data
- Lesson 4: Clustering and Dimensionality Reduction for Single-Cell Data
- Lesson 5: Single-Cell Trajectory and Pseudotime Analysis
- Lesson 6: Applications of Single-Cell Analysis in Disease Research
Chapter 10: Computational Approaches for Epigenomics
- Lesson 1: Introduction to Epigenetics and Epigenomics
- Lesson 2: DNA Methylation Analysis and Computational Tools
- Lesson 3: Chromatin Accessibility and ATAC-seq Data Analysis
- Lesson 4: Histone Modifications and ChIP-seq Analysis
- Lesson 5: Integration of Epigenomics Data with Gene Expression Data
- Lesson 6: Epigenomic Biomarkers and Disease Associations
Chapter 11: Systems Biology and Multi-Omics Data Integration
- Lesson 1: Introduction to Systems Biology
- Lesson 2: Multi-Omics Data Types and Challenges
- Lesson 3: Computational Tools for Multi-Omics Integration
- Lesson 4: Network-Based Approaches for Systems Biology
- Lesson 5: Case Studies in Multi-Omics Data Analysis
- Lesson 6: Applications in Personalized Medicine
Chapter 12: Advanced Algorithms for Sequence Alignment and Assembly
- Lesson 1: Overview of Sequence Alignment Algorithms
- Lesson 2: Advanced Pairwise Alignment Techniques (Smith-Waterman, Needleman-Wunsch)
- Lesson 3: Multiple Sequence Alignment and Progressive Methods
- Lesson 4: Genome Assembly Strategies: De Novo vs. Reference-Based
- Lesson 5: Long-Read Sequencing and Hybrid Assembly Approaches
- Lesson 6: Scalable and Parallel Computing for Genomic Data
Chapter 13: Genome-Wide Association Studies (GWAS) and Complex Trait Analysis
- Lesson 1: Introduction to GWAS and Population Genetics
- Lesson 2: Study Design and Data Preparation for GWAS
- Lesson 3: Statistical Models and Approaches in GWAS
- Lesson 4: Handling Population Stratification and Confounders
- Lesson 5: Post-GWAS Analysis: Fine Mapping and Functional Interpretation
- Lesson 6: GWAS in the Era of Polygenic Risk Scores
Chapter 14: Network Biology and Pathway Analysis
- Lesson 1: Basics of Biological Networks (Gene, Protein, and Metabolic Networks)
- Lesson 2: Network Construction and Topological Analysis
- Lesson 3: Identifying Key Regulators and Hubs in Biological Networks
- Lesson 4: Pathway Enrichment Analysis and Functional Interpretation
- Lesson 5: Disease Network Analysis and Drug Target Identification
- Lesson 6: Integrating Network Biology with Multi-Omics Data
Chapter 15: Advanced Statistical Methods for Bioinformatics
- Lesson 1: High-Dimensional Statistical Inference for Genomics
- Lesson 2: Bayesian Statistics and Probabilistic Models in Bioinformatics
- Lesson 3: Multiple Testing Corrections and False Discovery Rate (FDR)
- Lesson 4: Survival Analysis in Genomic Data Studies
- Lesson 5: Hidden Markov Models for Biological Sequence Analysis
- Lesson 6: Advanced Hypothesis Testing in Large-Scale Biological Data
Chapter 16: High-Performance Computing (HPC) in Bioinformatics
- Lesson 1: Basics of HPC and Cloud Computing
- Lesson 2: Parallel Computing for Genomics
- Lesson 3: Workflow Automation with Snakemake and Nextflow
- Lesson 4: Distributed Data Storage for Large-Scale Analysis
- Lesson 5: Cloud-Based Bioinformatics Platforms
Chapter 17: Ethical, Legal, and Social Issues in Bioinformatics
- Lesson 1: Ethics in Genomic Research
- Lesson 2: Data Privacy and Security in Bioinformatics
- Lesson 3: Open Science and Data Sharing Policies
- Lesson 4: Intellectual Property and Patents in Bioinformatics
- Lesson 5: Future Trends and Challenges
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