Computational Genomics

Course Description

Computational genomics is a dynamic field that merges the power of computational science with genomic research to analyze and interpret complex biological data. It encompasses the use of computational strategies to understand the structure, function, and evolution of genomes. By leveraging statistical models, algorithms, and both DNA and RNA sequencing data, computational genomics provides insights into the molecular mechanisms of genes and their regulatory networks. This interdisciplinary approach has revolutionized our ability to predict gene function, understand genetic variations, and explore the vast biological datasets generated by high-throughput genomic technologies. As a result, computational genomics is pivotal in advancing personalized medicine, improving crop yields, and unraveling the mysteries of evolutionary biology.


  • Introduction and Applications:
    • How Genomics relates to Math, Engineering, Statistics, Stochastic Processes, Estimation, Information Theory, Optimization, Signal Processing, Machine Learning, Control
    • Applications in Drug Design and Personalized Medicine, Synthetic Biology, and Criminology
  • Intro to Molecular Biology and Genetics:
    • Basics of Genetics
    • DNA and Protein Structure
  • Gene Expression and Microarrays
  • DNA Sequencing technologies
  • Suffix trees, Local and Global Alignment Dynamic Programming,
  • Hidden Markov Model, Multiple Sequence Alignment
  • Genome Structure, Variants, SNPs
  • Sequence Assembly.
  • Basics of System Biology, Pathways and Regulation Networks
  • Clustering and Classification for Microarrays
  • Learning for Gene-disease Association
  • Molecular Evolution, Phylogenetic Trees, Population Genetics
  • Synthetic Biology


  • Bioinformatics and Functional Genomics, J. Pevsner, Wiley (2009)
  • Biological Sequence Analysis – Probabilistic Models of Proteins and Nucleic Acids, R. Durbin, S. Eddy, A. Krogh, and G. Mitchison, Cambridge University Press (2005)
  • Bioinformatics, Sequence and Genome Analysis, D. W. Mount, Cold Spring Harbor Laboratory Press Bookstore (2004)
  • Algorithms on Strings, Trees, and Sequences, D. Gusfield, Cambridge University Press (1997)