MIT CompBio Lecture 01 - Introduction

MIT Computational Biology: Genomes, Networks, Evolution, Health Prof. Manolis Kellis http://compbio.mit.edu/6.047/ Fall 2018 Covers the computational foundations and research frontiers of computational biology. Advanced algorithmic techniques for rapid genome analysis and interpretation, data integration, epigenomics, comparative genomics, regulatory genomics, single-cell biology, deep learning, bayesian networks, pattern finding, and dissecting diseaes mechanisms. Genomes: Biological sequence analysis, hidden Markov models, gene finding, comparative genomics, RNA structure, sequence alignment, hashing. Networks: Gene expression, clustering/classification, EM/Gibbs sampling, motifs, Bayesian networks, Deep Learning, Epigenomics, Single-cell Genomics. Evolution: Gene/species trees, phylogenomics, coalescent, personal genomics, population genomics, human ancestry, recent selection, disease mapping. Health: Genetic association mapping, common/rare variants, GWAS, PheWAS, multi-trait mapping, causality/mediation, EHR mining, cancer genomics, CRISPR. In addition to the technical material in the course, the term project provides practical experience (1) writing an NIH-style research proposal, (2) reviewing peer proposals, (3) planning and carrying out independent research, (4) presenting research results orally in a conference setting, and (5) writing results in a journal-style scientific paper. Slides for Lecture 1: https://stellar.mit.edu/S/course/6/fa...