Computational Functional Genomics: Analyzing Genomic Data

Biological data is growing exponentially, making computational skills essential for modern life science. Understanding how to translate biological questions into computational solutions is the key to unlocking genomic discoveries. This text-only course guides you through the foundational concepts of functional genomics and systems biology from a computational perspective. You will transition from understanding basic genome structure to analyzing modern high-throughput sequencing and expression data using structured statistical methods. What you will learn: - Understand the core principles of genome structure, transcriptional regulation, and systems biology. - Analyze high-throughput sequencing data, comparing classic microarrays with modern RNA-seq workflows. - Apply statistical data analysis, predictive modeling, and causal inference to biological datasets. - Explore single-cell genomics concepts and modern computational frameworks for cellular analysis. - Design computational experiments that accurately model gene expression and chromatin accessibility. - Interpret biological pathways and networks using structured computational approaches. The course begins with essential biological terminology and foundational data structures before moving into practical statistical modeling and modern genomic analysis workflows. You will learn through clear written explanations, conceptual breakdowns, and step-by-step code demonstrations. Designed for biological researchers, computer science students, and curious beginners looking to enter the field of bioinformatics, this course requires no prior specialized computational experience. Start exploring the power of computational biology today.