Registration for the Fall 2022 semester is open. Please see the Academic Calendar for the full schedule.
This Area of Concentration relies on the same 4 Required Core Courses as the standard MS in Biotechnology degree. Additionally, you will complete 4 Area of Concentration Courses and 2 Elective Courses.
Area of Concentration Courses
Choose 4 courses from the list below to qualify for this Area of Concentration:
This course explores the theory and practice of biological database searching and analysis. In particular, students are introduced to integrated systems where a variety of data sources are connected through internet access. Information retrieval and interpretation are discussed, and many practical examples in a computer laboratory setting enable students to improve their data mining skills. Methods included in the course are searching the biomedical literature, sequence homology searching and multiple alignment, phylogeny, gene prediction, protein sequence motif analysis and secondary structure prediction, and several genome browsing methods. Introductory analysis using the R programming language is introduced. Computer access is required. Prerequisites: 410.601 Biochemistry. Corequisite: 410.602 Molecular Biology. SCI
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
Because the gap between the number of protein sequences and the number of protein crystal structures continues to expand, protein structural predictions are increasingly important. This course provides a working knowledge of various computer-based tools available for predicting the structure and function of proteins. Topics include protein database searching, protein physicochemical properties, secondary structure prediction, and statistical verification. Also covered are graphic visualization of the different types of three-dimensional folds and predicting 3-D structures by homology. Computer laboratories complement material presented in lectures. Prerequisites: 410.601 Biochemistry, 410.602 Molecular Biology, 410.633 Introduction to Bioinformatics. SCI
This course will provide a practical, hands-on introduction to the study of phylogenetics and comparative genomics. Theoretical background on molecular evolution will be provided only as needed to inform the comparative analysis of genomic data. The emphasis of the course will be placed squarely on the understanding and use of a variety of computational tools designed to extract meaningful biological information from molecular sequences. Lectures will provide information on the conceptual essence of the algorithms that underlie various sequence analysis tools and the rationale behind their use. Only programs that are freely available as either downloadable executables or as Web servers will be used in this course. Students will be encouraged to use the programs and approaches introduced in the course to address questions relevant to their own work. Prerequisites: 410.601 Biochemistry, 410.602 Molecular Biology, 410.633 Introduction to Bioinformatics. SCI
This course introduces statistical concepts and analytical methods as applied to data encountered in biotechnology and biomedical sciences. It emphasizes the basic concepts of experimental design, quantitative analysis of data, and statistical inferences. Topics include probability theory and distributions; population parameters and their sample estimates; descriptive statistics for central tendency and dispersion; hypothesis testing and confidence intervals for means, variances, and proportions; categorical data analysis; linear correlation and regression model; logistic regression; analysis of variance; and nonparametric methods. The course provides students a foundation with which to evaluate information critically to support research objectives and product claims and a better understanding of statistical design of experimental trials for biological products/devices. Prerequisites: Basic mathematics (algebra). 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
This course builds on the Perl concepts taught in 410.634 Practical Computer Concepts for Bioinformatics. Perl has emerged as the language of choice for the manipulation of bioinformatics data. Bioperl, a set of object-oriented modules that implements common bioinformatics tasks, has been developed to aid biologists in sequence analysis. The course will include an overview of the principal features of Bioperl and give students extensive opportunity to use Perl and the tools of Bioperl to solve problems in molecular biology sequence analysis. Prerequisites: 410.601 Biochemistry, 410.60 Molecular Biology, 410.634 Practical Computer Concepts for Bioinformatics. SCI
The next generation of array and sequencing technologies provides the ability to investigate large quantities of genomics information with higher sensitivity, greater throughput, and lower costs. This also introduces new challenges in data management, novel algorithmic approaches, and general interpretation. This course builds on the topics in 410.671 Gene Expression Data Analysis and Visualization to address analysis of both genetic variation and genomics content, including splice variants, single nucleotide polymorphisms (SNPs) with family-based and case/control genome-wide association, copy number variation, somatic and germline single nucleotide variants, tumor clonality and ploidy estimates, and transcription factor binding sites. Data types will include array, RNA sequencing, and DNA sequencing (targeted and whole exome) with sequence assembly methods presented, such as de novo and reference-based. Prerequisites: 410.602 Molecular Biology, 410.633 Introduction to Bioinformatics, 410.671 Gene Expression Data Analysis and Visualization. SCI
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
With the advent of rapid, low-cost whole-genome sequencing, the field of personalized medicine is growing from a niche field to becoming the new standard of practice in medicine. Already, oncology makes use of genomic sequencing to inform treatment decisions based on tumor types, and patients are seeking knowledge about their genetic and environmental risk factors to make informed health decisions. This class explores the evolving field of personalized medicine, examining genomics as well as proteomics, metabolomics, epigenetics, and the microbiome. Students will read and discuss new developments in pharmacogenomics, rare and complex diseases, genomics for the healthy person, and the ethical, economic, and social implications of these new technologies. These topics will be approached with a view toward application in clinical practice. Prerequisites: 410.602 Molecular Biology; 410.633 Introduction to Bioinformatics. SCI