Genomics

This course introduces students with a background in the life sciences to the basic computing concepts of the UNIX operating system, relational databases, structured programming, object-oriented programming, and the Internet. Included is an introduction to SQL and the Python scripting language. The course emphasizes relevance to molecular biology and bioinformatics. It is intended for students with no computer programming background but with a solid knowledge of molecular biology. Prerequisites: 410.601 Biochemistry, 410.602 Molecular Biology. SCI

Large-scale DNA sequencing efforts have resulted in increasingly large numbers of DNA sequences being deposited in public databases. Assigning annotations, such as exon boundaries, repeat regions, and other biologically relevant information accurately in the feature tables of these sequences requires a significant amount of human intervention. This course instructs students on computer analytical methods for gene identification, promoter analysis, and introductory gene expression analysis using software methods. Additionally, students are introduced to comparative genomics and proteomic analysis methods. Students will become proficient in annotating large genomic DNA sequences. This course covers customizing genome browsers with novel data. Next-generation sequence analysis is covered through sequence quality control and assembly and analysis of ChIP-seq and RNA-seq data. Students complete two large sequence analysis projects during the course. Prerequisites: 410.601 Biochemistry; 410.602 Molecular Biology; 410.633 Introduction to Bioinformatics or equivalent. SCI

The recent revolution in DNA sequencing technologies has transformed biology within a few short years, decreasing the cost and difficulty of sequencing dramatically to the point where the “$1,000 human genome” is in sight. Armed with complete genome sequences, biologists need to identify the genes encoded within and the variation in these genes between individuals, assign functions to the genes, and put these into functional and metabolic pathways. This course will provide an overview of next-generation sequencing technologies in the historical context of DNA sequencing, the pros and cons of each technology, and the bioinformatics techniques used with this sequence information, beginning with quality control assessment, genome assembly, and annotation. Prerequisites: 410.602 Molecular Biology, 410.633 Introduction to Bioinformatics, 410.634 Practical Computer Concepts for Bioinformatics. SCI

This course will introduce students to various methods for analyzing and interpreting transcriptomics data generated from technologies such as oligonucleotides or two-channel microarrays, qRT-PCR, and RNA sequencing. Topics will include scaling/normalization, outlier analysis, and missing value imputation. Students will learn how to identify differentially expressed genes and correlate their expression with clinical outcomes such as disease activity or survival with relevant statistical tests; methods to control for multiple testing will also be presented. An introduction to linear and nonlinear dimensionality reduction methods and both supervised and unsupervised clustering and classification approaches will be provided. Open source tools and databases for biological interpretation of results will be introduced. Assignments and concepts will make use of publicly available datasets, and students will compute and visualize results using the statistical software R. Prerequisites: 410.601 Biochemistry, 410.602 Molecular Biology, 410.645 Biostatistics, 410.634 Practical Computer Concepts for Bioinformatics, or an undergraduate computer programming course. SCI

Alterations to the genome are the basis of cancer development, but not all mutations cause cancer. Cancer genomics is the study of cancer cell genomes to elucidate how changes from the normal host genome drive cancer development and how these changes can be targeted for better prevention, diagnosis, and treatment of cancer. In this course, students learn about the multi-step process of tumorigenesis and the confounding development of passenger mutations that challenge the use of genomics to inform therapies. Students will use bioinformatics tools to analyze human cancer genomic data sets to understand the genetic basis of cancer and how to identify genetic signatures in tumors to guide treatment. Topics also include the development of drug resistance, biological sample acquisition, the technologies used to identify and distinguish pathogenic alleles, and how data is stored, referenced, and shared. Discussions about clinical trials and standards of care based on cancer genomics, and about the ethical challenges raised by the use of genomic information to make personal care decisions, are included in the course. Prerequisites: 410.601 Biochemistry or equivalent; 410.602 Molecular Biology; 410.603 Advanced Cell Biology; 410.638 Cancer Biology; 410.633 Introduction to Bioinformatics is recommended.

The emerging field of metagenomics allows for the study of entire communities of microorganisms at once, with far-reaching applications in a wide array of fields, such as medicine, agriculture, and bioremediation. Students will learn the principles of metagenomics through the exploration of published project data and guided readings of recent literature. Using data from the Human Microbiome Project, students will explore practical analysis tasks, including sequence assembly, gene prediction and annotation, metabolic reconstruction, taxonomic community profiling, and more. Prerequisites: 410.601 Biochemistry, 410.602 Molecular Biology, 410.633 Introduction to Bioinformatics, 410.634 Practical Computer Concepts for Bioinformatics. SCI