A hands-on lab companion to computational genomics. Students build from the foundations — DNA as information, the central dogma, genomes and chromosomes — through assignments that extend in-class work, paper readings, and an in-class midterm, plus wet-lab sessions at the New York Genome Center. The emphasis is on connecting computation to real sequencing workflows and the broader ethical, legal, and social context of genomics.

An introduction to computational genomics: the algorithms and statistics that turn high-throughput sequence data into biological insight. Topics range from read mapping, variant calling, assembly, and RNA-/single-cell/ChIP-seq to evolution across species, variation within a species, and genetic mapping — drawing on efficient indexing, dynamic programming, graphs, hidden Markov models, dimensionality reduction, clustering, coalescent models, and MCMC. Advanced undergraduate/graduate, cross-listed in Computer Science; weekly problem sets, a midterm, and a final project. Prerequisites: independent programming (able to read Python) and a basic course in probability and statistics — biology background is provided.