Bioinformatics for Genomic Data Analysis
( 25 Modules )

Module #1
Introduction to Bioinformatics and Genomics
Overview of bioinformatics, genomics, and their importance in modern biology and medicine.

Module #2
Genomic Data Types and File Formats
Introduction to common genomic data types (e.g. FASTA, FASTQ, SAM, BAM) and file formats.

Module #3
Unix/Linux Essentials for Bioinformatics
Basic Unix/Linux commands and tools essential for bioinformatics analysis.

Module #4
Introduction to Sequence Analysis
Basic sequence analysis concepts and tools (e.g. BLAST, pairwise alignment).

Module #5
Multiple Sequence Alignment
Methods and tools for multiple sequence alignment (e.g. MUSCLE, MAFFT).

Module #6
Genome Assembly and Annotation
Introduction to genome assembly (e.g. Velvet, SOAPdenovo) and annotation (e.g. Genemark, Augustus) tools.

Module #7
SNP and Variant Calling
Methods and tools for identifying single nucleotide polymorphisms (SNPs) and other variants (e.g. SAMtools, BCFtools).

Module #8
High-Throughput Sequencing Technologies
Overview of high-throughput sequencing technologies (e.g. Illumina, PacBio, Oxford Nanopore).

Module #9
RNA-Seq Data Analysis
Introduction to RNA-Seq data analysis, including read mapping, quantification, and differential expression analysis.

Module #10
ChIP-Seq Data Analysis
Introduction to ChIP-Seq data analysis, including peak calling and motif analysis.

Module #11
Gene Expression Analysis
Introduction to gene expression analysis, including differential expression, clustering, and pathway analysis.

Module #12
Genomic Feature Identification and Annotation
Methods and tools for identifying and annotating genomic features (e.g. genes, promoters, enhancers).

Module #13
Functional Enrichment Analysis
Introduction to functional enrichment analysis using tools like Gene Ontology, KEGG, and Reactome.

Module #14
Phylogenetics and Phylogenomics
Introduction to phylogenetics and phylogenomics, including tree building and genome-scale phylogenetic analysis.

Module #15
Comparative Genomics
Introduction to comparative genomics, including genome alignment, synteny, and gene family analysis.

Module #16
Cancer Genomics
Introduction to cancer genomics, including somatic mutation analysis and tumor evolution.

Module #17
Genomic Data Visualization
Introduction to genomic data visualization tools (e.g. Circos, UCSC Genome Browser, IGV) and best practices.

Module #18
Bioinformatics Pipelines and Workflows
Introduction to bioinformatics pipelines and workflows using tools like Nextflow, Snakemake, and Galaxy.

Module #19
Cloud Computing for Bioinformatics
Introduction to cloud computing for bioinformatics, including Amazon Web Services, Google Cloud, and Microsoft Azure.

Module #20
Containerization and Virtualization
Introduction to containerization (e.g. Docker) and virtualization (e.g. VirtualBox) for bioinformatics.

Module #21
Data Management and Quality Control
Best practices for data management and quality control in bioinformatics, including data storage, compression, and validation.

Module #22
Computational Skills for Bioinformatics
Introduction to programming languages and tools essential for bioinformatics (e.g. Python, R, SQL).

Module #23
Genomic Data Integration and Systems Biology
Introduction to genomic data integration and systems biology, including network analysis and machine learning.

Module #24
Ethics and Regulatory Considerations
Ethical and regulatory considerations in bioinformatics, including data privacy, security, and sharing.

Module #25
Course Wrap-Up & Conclusion
Planning next steps in Bioinformatics for Genomic Data Analysis career

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Bioinformatics for Genomic Data Analysis ( 25 Modules )

Bioinformatics for Genomic Data Analysis
( 25 Modules )