Master's Degree in Bioinformatics Online Curriculum

This course is a high-content introduction to scripting and programming with applications in bioinformatics. It is appropriate for students with little previous programming experience. The course covers the fundamentals of working with Linux systems, using bioinformatics tools, and manipulating biological data files. The focus will be on scripting with Bash and Python. The course will also touch on topics such as how to interact programmatically with SQL databases and RESTful web services, and how to work with distributed compute systems to perform large calculations.

This course covers concepts of classic genetics, from Mendelian inheritance to quantitative and complex traits, associations and population genetics. It addresses the anatomy and function of genomes from humans and model organisms, and how individual components form signaling pathways. Using the Human Genome Projects as an example, sequencing and mapping technologies are covered. Basic sequence analysis methods are introduced, along with techniques to navigate genome browsers and other relevant databases. Cloning and methods for genetic manipulation, including CRISPR, are introduced.

This course provides a foundation in biological sequence analysis, including methods for handling next-generation sequencing data. Topics include genomic assembly and variant detection using short reads, methods for homology detection, functional annotation of sequences, and use of databases and visualization tools.

This course is an advanced mathematics and applied statistics course that will introduce students to data analysis methods and statistical testing. It provides a foundation for Biological Data Mining and Modeling (RBIF 112) and Design and Analysis of Microarray Experiments (RBIF114). The course covers R (a statistical programming language) to introduce students to descriptive and inferential statistics, basics of programming, common data structures and analysis techniques. The course covers methods important to data analysis such as t-tests, chi-squared analysis, Mann-Whitney tests, correlation and regression, ANOVA, LDA, PCA, tests of significance, and Fisher’s exact test.

The development of new bioinformatics tools typically involves some form of data modeling, prediction or optimization. This course introduces various modeling, prediction, and machine learning techniques including linear and nonlinear regression, principal component analysis, support vector machines, self-organizing maps, neural networks, set enrichment, Bayesian networks, and model-based analysis.

Microarrays are routinely used in genomic studies to detect changes in mRNA expression levels and have been key in developing biomarkers for several diseases. These experiments have fundamental statistical and data processing challenges associated with them. This course covers the statistical aspects of experimental design, biological and technical replicates, preprocessing, quality assessment, parametric and non-parametric statistical tests, multiple-hypothesis testing, P-value correction and false discovery rates, visualization techniques (e.g. heatmaps, volcano plots), and biological significance (e.g. functional annotation, pathways, hypergeometric tests, gene set enrichment). The course also covers the increasing role of molecular profiling in disease treatment, particularly in oncology.

In this intensive course students will investigate the interrelationships existing amongst protein sequence, structure, and function through the lens of a structural bioinformaticist. Topics covered range from analysis of protein structure to domain classification, phylogeny, structural modeling, interaction site prediction, kinetics and thermodynamics of biomolecular interactions, and structure-based drug design. Throughout the course students will be exposed to software tools utilized by structural bioinformaticists in their daily work.

There are high expectations for bioinformatics to contribute to drug discovery. This course explores issues faced during drug discovery and development. Topics include the drug discovery process, its major players and its origins; scientific principles behind drug properties and actions; target product profiles; disease and drug target selection, sources of drug-like molecules; assays and screening; medicinal chemistry; pharmacology; toxicology; and clinical trials.

Computational systems biology is a field that aims to provide an integrative, system-level understanding of biology through the modeling of experimental data. The course covers interacting systems by defining the basic structures of the biological network that allow a living cell to maintain homeostasis under different conditions and perturbations.

This course covers modeling at the molecular level, with a focus on topics relevant to protein-ligand binding and cheminformatics. The first half of the course will cover topics in basic macromolecular structure and thermodynamics relevant to prediction and analysis of macromolecular interactions, and includes crystallography, energetics of hydrogen-bonding and hydrophobic interactions, and structure-based docking. The second half of the course will introduce the basics of cheminformatics, covering chemical structures, chemical descriptors, and methods for clustering and similarity-searching for compounds.

This course covers methods in statistical genetics used to detect disease or quantitative trait loci in experimental and human populations. Basic concepts in Genetics, Genomics and Genetic Epidemiology are reviewed, with an emphasis on the statistical and practical issues involved in genetic analysis. Both linkage and association approaches will be covered, with a focus on applications in the human genome wide association (GWAS) setting for both SNPs and CNVs. Approaches to extracting and enriching GWAS through genotype imputation, GSEA, meta-analysis and genetics of gene expression analysis will also be covered, along with topics relevant to pharmacogenetics and techniques to analyze next generation sequencing data in a population setting.

This course introduces students to basic research in computational biology. The student and instructor will propose a novel research project with the goal of publishing their findings in a peer-reviewed journal. The scope of the project will be well defined and will be completed within two terms. The instructor will oversee all aspects of the project and will provide appropriate scientific guidance and mentorship. The student will perform the research and will present their research to faculty at the end of each term. Research topics include but are not limited to scientific programming, software development, genome analysis, structural bioinformatics, evolution, drug discovery, and systems biology.

The field of Bioinformatics is continually evolving. New biologic research as well as legal, ethical, and regulatory habits and practices around the world are subject to rapid and potentially wide-reaching change. New technologies are continually introduced that may foster new research capabilities. This Bioinformatics Special Topics course facilitates the introduction of cutting-edge practices and technologies as they are introduced in the industry.