Course: BBS 741
Spring 2015 (February - May)
Course schedule and lecture list can be found on the Blackboard Vista (BLS) website http://www.worcester.umassonline.net
Instructors: Jeffrey Bailey, Daniel Caffrey, Manuel Garber, Oliver Rando, Barbara Tabak, Zhiping Weng and Konstantin Zeldovich
The Advanced Topics in Bioinformatics course covers several important areas of modern bioinformatics and computational biology. The course is aimed not only at students specializing in bioinformatics, but also general biology students who would like to utilize bioinformatics tools in their daily research. The course will begin with an overview of modern sources of bioinformatics data, including high-throughput sequencing and microarrays, followed by a thorough presentation of sequence search and alignment algorithms, and the structure of the eukaryotic genome. Next, we will introduce population genetics - spanning from molecular phylogenetics to natural selection, with an emphasis on analyzing genomic datasets. The biophysical section of the course will include discussions of protein structure and folding, as well as the physical architecture of the genome in vivo, and the relations between sequences and structures for proteins and DNA. The course will include ten lectures, followed by work on individual or group research projects, presented in lieu of the final exam. Some experience with computers /programming / statistics is desirable, but the necessary tools will be introduced and explained as needed.
Specific topics will include:
UNIX, PERL1, PERL2, UCSC/Galaxy, Genes and Regulatory Elements, Genome-wide profiling of gene expression and protein localization analysis, Physics of biomacromolecules, concepts of protein folding, Whole-proteome biophysics:amino acid compositions, thermostability, mutational robustness, and the near-optimality of the genetic code, Genome evolution, Intro to population genetics, Analyzing genomic data: from phylogenetics to natural selection, Mining the genome with Ensembl Perl API and Plotting and visualization of genomic data in R.