The field of computational biology is rich in algorithmic challenges, stemming from the sheer size of massive datasets, and also the noisy nature of biological signals. For example, the human genome contains 3 billion letters, 25 thousand genes, and several thousand regulatory signals governing the gene usage. The genes themselves interact in complex ways, forming dense regulatory networks, shaped by relentless evolutionary forces. In this course, we will address several algorithmic challenges in computational biology. We will study the principles of algorithmic design for biological datasets, analyze existing algorithms, and apply these to real datasets. The lectures will cover:

1: Assembly of Complete Genomes from Short Reads
2: Gene Identification in Vast Genomic Regions
3: Regulatory Motif Discovery
4: Genome Alignment and Comparison
5: Reconstruction of Regulatory Networks
6: Inference of Evolutionary Mechanisms