Computational Genomics
( 25 Modules )

Module #1
Introduction to Computational Genomics
Overview of the field, importance, and applications of computational genomics

Module #2
Genomics and Genome Analysis
Basics of genomics, genome structure, and genome analysis

Module #3
Bioinformatics Tools and Databases
Introduction to bioinformatics tools and databases, such as NCBI, Ensembl, and UCSC Genome Browser

Module #4
Sequence Alignment and Assembly
Algorithms and tools for sequence alignment and assembly, including BLAST, BWA, and SPAdes

Module #5
Genome Annotation and Gene Finding
Methods and tools for genome annotation and gene finding, including gene prediction and functional annotation

Module #6
RNA-seq Data Analysis
Introduction to RNA-seq data analysis, including quality control, alignment, and expression analysis

Module #7
ChIP-seq Data Analysis
Introduction to ChIP-seq data analysis, including peak calling and motif analysis

Module #8
Variant Detection and Genotyping
Methods and tools for variant detection and genotyping, including SNP calling and structural variant detection

Module #9
Genomic Data Visualization
Introduction to genomic data visualization, including genome browsers and visualization tools

Module #10
Computational Phylogenetics
Introduction to computational phylogenetics, including phylogenetic tree construction and analysis

Module #11
Genome Evolution and Comparative Genomics
Introduction to genome evolution and comparative genomics, including genome annotation and synteny analysis

Module #12
Functional Genomics and Gene Regulation
Introduction to functional genomics and gene regulation, including gene expression analysis and regulatory network inference

Module #13
Epigenomics and Epigenetic Analysis
Introduction to epigenomics and epigenetic analysis, including ChIP-seq and ATAC-seq data analysis

Module #14
Cancer Genomics and Computational Oncology
Introduction to cancer genomics and computational oncology, including mutation analysis and cancer driver detection

Module #15
Statistical and Machine Learning Methods in Genomics
Introduction to statistical and machine learning methods in genomics, including supervised and unsupervised learning

Module #16
Computational Methods for Gene-Environment Interactions
Introduction to computational methods for gene-environment interactions, including eQTL and mQTL analysis

Module #17
Personalized Medicine and Genomic Medicine
Introduction to personalized medicine and genomic medicine, including pharmacogenomics and genomic diagnosis

Module #18
Ethical Considerations in Computational Genomics
Ethical considerations in computational genomics, including data privacy and sharing

Module #19
Cloud Computing and High-Performance Computing in Genomics
Introduction to cloud computing and high-performance computing in genomics, including AWS and HPC clusters

Module #20
Computational Methods for Single-Cell Genomics
Introduction to computational methods for single-cell genomics, including scRNA-seq data analysis

Module #21
Computational Methods for Genome Editing
Introduction to computational methods for genome editing, including CRISPR-Cas9 and genome engineering

Module #22
Synthetic Biology and Genome Design
Introduction to synthetic biology and genome design, including genome-scale engineering and biosystems design

Module #23
Computational Methods for Metagenomics
Introduction to computational methods for metagenomics, including 16S rRNA gene analysis and metagenome assembly

Module #24
Computational Methods for pangenomics
Introduction to computational methods for pangenomics, including pangenome graph construction and analysis

Module #25
Course Wrap-Up & Conclusion
Planning next steps in Computational Genomics career

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Computational Genomics ( 25 Modules )

Computational Genomics
( 25 Modules )