Offered: Fall 2025 (current)
Genomics: genome assembly, annotation, comparative genomics, pan-genomics; Transcriptomics: RNA-seq analysis, differential gene expression, alternative splicing, single-cell transcriptomics; Proteomics: mass spectrometry data analysis, protein structure prediction, protein-protein interactions, post-translational modifications; Metagenomics: microbial community analysis, taxonomic profiling, functional analysis, environmental genomics; Systems Biology: network analysis, pathway modeling, metabolic modeling, simulation of biological systems; Machine Learning in Bioinformatics: deep learning applications, predictive modeling, feature selection, data integration; Structural Bioinformatics: molecular dynamics simulations, protein folding, drug design, virtual screening; Population Genetics: population structure analysis, phylogenetics, evolutionary genomics, genome-wide association studies (GWAS); Clinical Bioinformatics: personalized medicine, biomarker discovery, disease prediction, pharmacogenomics; Data Integration and Visualization: database management, data mining, interactive visualization tools, web-based resources; Advanced Algorithms in Bioinformatics: string algorithms, graph algorithms, optimization techniques, high-performance computing; Ethical and Legal Issues in Bioinformatics: data privacy, data sharing, intellectual property, responsible use of genomic data.
The core objectives of this course are to:
To guide students in developing a deep understanding and practical proficiency in the analysis of genomics, transcriptomics, proteomics, and metagenomics data
To equip students with the knowledge and skills to effectively apply machine learning algorithms, including deep learning, to solve complex biological problems
To enable students to construct, analyze, and interpret biological networks, pathways, and metabolic models, promoting a systems-level understanding of biology
To provide students with the necessary expertise to perform molecular dynamics simulations, protein folding predictions, and virtual screening for drug design
To instruct students on the principles and applications of population genetics and clinical bioinformatics, including GWAS and personalized medicine
To provide students with the tools and techniques necessary for effective database management, data mining, and interactive visualization of large-scale biological datasets
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