The bioinformatics analysis minor immerses students in the core challenges and strengths of the field of bioinformatics, as well as the ethical issues involved. Students gain hands-on experience implementing some of the core algorithms utilized by professionals in the field.
Notes about this minor:
This minor is closed to students majoring in bioinformatics and computational biology.
Posting of the minor on the student's academic transcript requires a minimum GPA of 2.0 in the minor.
The plan code for Bioinformatics Analysis Minor is BIOANA-MN.
Students must complete the following courses or their equivalent
General Biology I
This course serves as an introduction to cellular, molecular, and evolutionary biology. Topics will include: a study of the basic principles of modern cellular biology, including cell structure and function; the chemical basis and functions of life, including enzyme systems and gene expression; and the origin of life and evolutionary patterns of organism development on Earth. Lecture 3 (Fall, Summer).
General Biology II
This course serves as an introduction to animal and plant anatomy and physiology, in addition to the fundamentals of ecology. Topics will include: animal development; animal body systems; plant development; unique plant systems; Earth's terrestrial and aquatic environments; population and community ecology; animal behavior; and conservation biology. Lecture 3 (Spring, Summer).
Introductory Biology II
This course serves as an introduction to the diversification of life, plant anatomy and physiology, animal anatomy and physiology, and ecology. Topics include a survey of the taxonomic diversity of the major groups of living organisms, the anatomical and physiological adaptations of both plants and animals, and the principles of the ecological relationships among organisms and environments. Laboratory exercises are designed to illustrate concepts of taxonomy, anatomical & physiological adaptation, and ecological relationships. Labs are also designed to help the development of laboratory skills and techniques for experiments with live organisms, and improve the ability to make, record and interpret observations. Lab 3, Lecture 3 (Fall, Spring).
This course will address the fundamental concepts of molecular biology.
Class discussions, assignments, and laboratory projects will explore the structure and function of molecules and macromolecules, and processes important to storage and maintenance of genetic information and genetic information flow. Students in this course will explore molecular interactions that drive biological processes related to genetic information flow. Students in this course will gain an understanding of various molecular mechanisms, structure/function relationships, and processes as they relate to molecular biology. Students in this course will practice and carry out common laboratory techniques used by Molecular Biologists including, recombinant DNA technology and the detection and tracking of important macromolecules such as DNA, RNA and proteins. (Prerequisites: C- or better in (BIOL-101/102 and BIOL-103/104) or (BIOL-121/122) or equivalent. Students who have taken BIOL-201 cannot receive credit for BIOL-202.
Co-requisites: (CHMG-141/145) or (CHEM-151/155) or CHMG-131 or equivalent course.) Lab 3, Lecture 3 (Fall, Spring).
Introduction to Bioinformatics
This course will explore topics in the field of bioinformatics including tools and resources used by the discipline, including direct experience with the common user environment. Lecture 3 (Fall).
Introduction to Bioinformatics Programming
Computer programming in the life sciences is used for modeling and data analysis across all fields. In this course, students will learn the fundamentals of computer programming and apply it to solve real problems in the life sciences. Breaking down problems, common syntax, and thoughtful decisions on proper use of data structures will be emphasized. (UGRD-COS) Lab 2, Lecture 2 (Fall).
Fundamental Bioinformatics Analysis
This course addresses the fundamental concepts of bioinformatics, focusing on computational analysis of nucleic acids and proteins. Utilization of computational programs for analysis of individual and multiple sequences for functional and evolutionary information will be discussed. The computational laboratory will highlight the applications available for analysis of molecular sequences. (Prerequisites: BIOL-201 or BIOL-202 or equivalent courses.) Lecture 2, Studio 2 (Fall).
Choose two of the following
This is an introductory course in scripting languages focusing on the Perl programming language, the R statistical analysis program, and their application to biological data. We will investigate the use of Perl and R for processing sequence and "-omics" data, managing a variety of biological data types, and providing effective Web and graphical interfaces to existing tools for analysis of these data. (Prerequisites: BIOL-135 or equivalent course.) Lecture 2, Studio 3 (Spring).
Fundamentals of Bioinformatics Programming
Computer programming in the life sciences is used for modeling and data analysis across all fields. In this course, students will learn more advanced techniques to solve life sciences modeling problems efficiently using parallelization and distributed computing. Common methods and thoughtful decisions on proper use of tools will be emphasized. (Prerequisites: BIOL-230 or equivalent course and students in COS Majors.) Lab 2, Lecture 2 (Fall).
Ethical Issues in Biology and Medicine
This course explores major ethical issues in medicine and biology via lecture, readings, films, and presentation and discussion of cases. Students report on current events in ethics as researched on the internet or other news media. The first portion of the course is in a lecture format. Students learn about various theories of ethical analysis that are in current use. Subsequent classes are devoted to particular ethical areas. Relevant cases are given to the students for presentation in both written and oral formats. Any additional background material that may be required to discuss the cases is presented by the instructor and the remainder of the period is discussion based on the philosophical foundation provided at the beginning of the course. (Prerequisites: BIOL-102 or BIOL-122 or (1001-201, 1001-202 and 1001-203) or (1001-251, 1001-252 and 1001-253) or equivalent course.) Lecture 3 (Spring).
Statistical Analysis for Bioinformatics
This course is an introduction to the probabilistic models and statistical techniques used in computational molecular biology. Examples include Markov models, such as the Jukes-Cantor and Kimura evolutionary models and hidden Markov models, and multivariate models use for discrimination and classification. (Prerequisites: (MATH-161 or MATH-173 or MATH-182) and (STAT-145 or MATH-251) or equivalent courses.) Lecture 3 (Spring).
Bioinformatics Algorithms will focus on the types of analyses, tools, and databases that are available and commonly used in Bioinformatics. The labs will apply the lecture material in the analysis of real data through computer programming. (Prerequisites: BIOL-330 and CSCI-243 or equivalent course.) Lab 2, Lecture 2 (Fall).
High Throughput Sequencing Analysis
Students will utilize commonly used bioinformatics tools to analyze a real High Throughput Sequencing data set starting with raw data, proceeding with quality control, either aligning to a reference genome or performing de novo assembly, assessing differential gene expression determination, and finally annotating their results. Weekly lab reports will be required, and a group manuscript is expected at the end of the semester. (Prerequisites: BIOL-201 or BIOL-202 or equivalent courses.) Lab 2, Lecture 2 (Spring).
Molecular Modeling and Proteomics
This course will explore two facets of protein molecules: separation and structure. The separation component will address common protein separation techniques such as 2D gel electrophoresis and chromatography. The structure component will follow the levels of protein structures, focusing on both experimental and computational methods to determine protein structures. Methods for determining primary structures such as Edman degradation method, Sanger method and mass spectrometry will be taught in lectures. Algorithms of predicting secondary structures will be introduced and implemented. Tertiary structure determination techniques such as NMR will be covered, with an emphasis on proton NMR, 13C NMR and multi-dimensional NMR. Homology modeling will be used to predict protein tertiary structures. (Prerequisites: BIOL-330 or equivalent course.) Lab 2, Lecture 2 (Spring).
The course provides opportunities for students and faculty to develop and share professional interests while discussing current trends and developments in bioinformatics. Material for this course will be drawn from the current scientific literature. (This course is restricted to students in the BIOINFO-MS, BIOINFO-BS/MS program.) Lecture 3 (Fall).