Advanced Topics in Bioinformatics
( 25 Modules )

Module #1
Introduction to Advanced Bioinformatics
Overview of advanced bioinformatics topics and their applications

Module #2
Next-Generation Sequencing Technologies
In-depth exploration of NGS platforms, library preparation, and sequencing chemistries

Module #3
Advanced Genomic Data Analysis
Hands-on experience with command-line tools for NGS data analysis, including FASTX-Toolkit and BEDTools

Module #4
variant Calling and Annotation
Variant detection using samtools, bcftools, and annotation with ANNOVAR and SnpEff

Module #5
Structural Variant Detection
Detection of structural variants using paired-end and split-read methods with tools like DELLY and SVcaller

Module #6
RNA-seq Analysis
Quantification of gene expression using STAR, kallisto, and Salmon, and differential expression analysis with DESeq2 and edgeR

Module #7
ChIP-seq Analysis
Peak calling and motif analysis using MACS, PEAKS, and HOMER

Module #8
Cancer Genomics
Analysis of cancer genomes using tools like MuSiC, ABSolute, and MutSigCV

Module #9
Epigenetics and Epigenomics
Introduction to epigenetics, ChIP-seq, and methylation analysis using tools like BS-Seeker and methylKit

Module #10
Functional Genomics and Gene Regulation
Analysis of gene regulation using tools like GenomicFeatures, EnsEMBL, and UCSC Genome Browser

Module #11
Protein Structure Prediction
In-depth exploration of protein structure prediction methods, including ab initio and template-based modeling

Module #12
Advanced Protein Function Prediction
Prediction of protein function using tools like InterProScan, PFAM, and Gene Ontology

Module #13
Metagenomics and Microbiome Analysis
Analysis of metagenomic data using tools like QIIME, mothur, and MetaPhlAn

Module #14
Systems Biology and Network Analysis
Introduction to systems biology, network analysis, and visualization using tools like Cytoscape and NetworkX

Module #15
Machine Learning in Bioinformatics
Introduction to machine learning concepts and their applications in bioinformatics using scikit-learn and PyTorch

Module #16
Deep Learning in Bioinformatics
Advanced topics in deep learning for bioinformatics, including convolutional neural networks and recurrent neural networks

Module #17
Cloud Computing in Bioinformatics
Introduction to cloud computing platforms, such as AWS and Google Cloud, for large-scale bioinformatics analysis

Module #18
Best Practices for Bioinformatics Analysis
Discussion of best practices for bioinformatics analysis, including data quality control, reproducibility, and version control

Module #19
Bioinformatics Tools for Data Visualization
Introduction to data visualization tools, including Circos, Plotly, and Bokeh

Module #20
Single-Cell Omics Analysis
Analysis of single-cell RNA-seq data using tools like Seurat, Scanpy, and Monocle

Module #21
Spatial Omics Analysis
Analysis of spatial transcriptomics data using tools likeSpatialDE and ST

Module #22
Immunogenomics and T-Cell Receptor Repertoire Analysis
Analysis of T-cell receptor repertoire data using tools like TRUST and TCRfinder

Module #23
Advanced Topics in Phylogenomics
In-depth exploration of phylogenomic analysis, including genome assembly, annotation, and phylogenetic reconstruction

Module #24
Transcriptomics and Alternative Splicing Analysis
Analysis of transcriptomics data using tools like HISAT, StringTie, and STAR

Module #25
Course Wrap-Up & Conclusion
Planning next steps in Advanced Topics in Bioinformatics career

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Advanced Topics in Bioinformatics ( 25 Modules )

Advanced Topics in Bioinformatics
( 25 Modules )