Analyzing Biological Data with Bioinformatics Tools
( 24 Modules )

Module #1
Introduction to Bioinformatics
Overview of bioinformatics, its significance, and applications in biological research

Module #2
Biological Data Types and Formats
Types of biological data (genomic, transcriptomic, proteomic), file formats (FASTA, FASTQ, CSV), and data quality control

Module #3
Linux Command Line Essentials for Bioinformatics
Basic Linux command line skills for bioinformatics:navigating, editing, and manipulating files

Module #4
Sequence Alignment Fundamentals
Introduction to sequence alignment, types of alignments (global, local, pairwise), and scoring schemes

Module #5
Pairwise Sequence Alignment Tools
Practical application of pairwise sequence alignment tools (BLAST, EMBOSS, Smith-Waterman)

Module #6
Multiple Sequence Alignment Tools
Practical application of multiple sequence alignment tools (MAFFT, MUSCLE, ClustalW)

Module #7
Phylogenetic Analysis Fundamentals
Introduction to phylogenetics, tree construction methods, and tree visualization

Module #8
Phylogenetic Tree Inference Tools
Practical application of phylogenetic tree inference tools (RAxML, BEAST, MrBayes)

Module #9
Genome Assembly and Annotation
Introduction to genome assembly, annotation tools ( Velvet, SPAdes, Prokka), and annotation databases

Module #10
Genome Feature Prediction and Functional Annotation
Predicting gene features (gene finding, repeat detection), functional annotation tools (InterProScan, BLAST2GO)

Module #11
RNA-seq Data Analysis
Introduction to RNA-seq data analysis, including read mapping, quantification, and differential expression

Module #12
RNA-seq Data Analysis Tools
Practical application of RNA-seq data analysis tools (STAR, HISAT, DESeq2, edgeR)

Module #13
ChIP-seq Data Analysis
Introduction to ChIP-seq data analysis, including peak calling and differential binding analysis

Module #14
ChIP-seq Data Analysis Tools
Practical application of ChIP-seq data analysis tools (MACS, PeakRanger, DiffBind)

Module #15
Protein Structure Prediction and Analysis
Introduction to protein structure prediction, tools (Phyre2, I-TASSER), and structure analysis

Module #16
Machine Learning in Bioinformatics
Introduction to machine learning concepts, supervised and unsupervised learning in bioinformatics

Module #17
Deep Learning in Bioinformatics
Introduction to deep learning concepts, applications in bioinformatics (protein structure prediction, gene expression analysis)

Module #18
Bioconductor and R for Bioinformatics
Introduction to Bioconductor, R programming, and bioinformatics packages (e.g., GenomicRanges, DESeq2)

Module #19
Biological Pathway Analysis
Introduction to biological pathway analysis, tools (KEGG, Reactome, Pathway Studio)

Module #20
Data Visualization in Bioinformatics
Introduction to data visualization concepts, tools (Matplotlib, Seaborn, Plotly), and bioinformatics applications

Module #21
High-Performance Computing in Bioinformatics
Introduction to high-performance computing, parallel computing, and bioinformatics applications

Module #22
Cloud Computing in Bioinformatics
Introduction to cloud computing, cloud-based bioinformatics platforms (AWS, Google Cloud, Azure)

Module #23
Bioinformatics Pipelines and Workflow Management
Introduction to pipeline management, workflow management tools (Snakemake, Nextflow, Apache Airflow)

Module #24
Course Wrap-Up & Conclusion
Planning next steps in Analyzing Biological Data with Bioinformatics Tools career

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Analyzing Biological Data with Bioinformatics Tools ( 24 Modules )

Analyzing Biological Data with Bioinformatics Tools
( 24 Modules )