Bioinformatics and Systems Biology Track

Highly Recommended Courses

Statistics in Biology - BIOL-GA 2030, 4 points, which is a pre-requisite for several other bioinformatic courses. Advanced course on techniques of statistical analysis and experimental design that are useful in research and in the interpretation of biology literature. Students will be trained to use the programming package R.
Applied Genomics: Introduction to Bioinformatics and Network Modeling - BIOL-GA 1130, 4 points. Introduces fundamental methods of analyzing large data sets from genomics experiments. Through a combination of lectures, hands-on computational training, and in-depth discussions of current scientific papers, students learn the conceptual foundations of basic analytical methods, the computational skills to implement these methods, and the reasoning skills to read critically the primary literature in genomics.
Programming for Biologists - BIOL-GA.1007, 4 points. Provides introductory theory and hands-on training in bioinformatics. Students are introduced to the Linux operating system and basic computer programming skills (Perl and Bioconductor). Topics covered: biological databases, pairwise and multiple sequence alignment, BLAST and related algorithms, sequence motifs, Hidden Markov Models, gene expression analysis, and resources for functional associations (gene ontology, pathways and networks).
Biological Databases and Datamining - BIOL-GA 1009, 4 points. The course is divided into three sections: 1) Introduction to MySQL and R. 2) Introduction to different data types, and 3) Machine learning methods for data mining. Students will learn to create their own database using MySQL and SQLite containing different types of biological data. Students will also learn to mine the heterogeneous biological data using machine-learning methods such as Support Vector Machines and Multiple Regressions. We will apply these methods on experimental data in order to classify and prediction gene function and regulation.
Genomics of Human Populations - BIOL-GA 1132, 4 points. This course covers topics in the field of human population genetics including human ancestry and admixture, human demography, linkage disequilibrium, genome-wide association studies (GWAS), genetic architecture of human traits and diseases, natural selection in the human genome, and application of population genomics to the study of cancer and disease. The course includes lecture and recitation components with the latter geared to teaching students basic skills in population genomic data analysis.
Statistics and Machine Learning for Genomics - BIOL-GA 2031, 4 points. Next‐generation sequencing has led to the rise of large and noisy biological datasets, which require increasingly advanced analytical methods to glean biological insights. This course aims to enable students to analyze diverse types of genomic data, ranging from studies focused on human genetics (i.e. Genome‐wide association studies), to functional genomics (i.e. ChIP‐seq or RNA_seq, extending even to the single cell level) To accomplish this, we will review the theory and implementation behind key concepts and methods in statistical learning, and apply these to genomic datasets.
Research - BIOL-GA 3303-3304, 1-6 points. Students in this track also typically take research credits, since hands-on experience in bioinformatics/genomics is especially important. Each student must complete a research paper under the supervision of a Biology faculty member, however, students may seek approval from the Director of the Master’s Program, for a main sponsor who is outside the Biology Department.