Bioinformatics Analysis Minor

2a93eec3-83f3-4d90-ab54-8b317dcdd2b9 | 129839

Overview

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.

Curriculum

Course
Prerequisites
Students must complete the following courses or their equivalent
BIOL-102
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.
or
 
BIOL-122
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.
and 
 
BIOL-201
Cellular and Molecular Biology
This course will address the fundamental concepts of Cellular and Molecular Biology. Lectures, assignments, and laboratory projects will explore the structure and function of molecules, organelles, and cells and the biological processes they are involved in. Students in this course will gain an understanding of various molecular mechanisms, structure/function relationships, and cellular processes as they relate to cellular and molecular biology. Students in this course will practice and carry out common laboratory techniques used by Cellular and Molecular Biologists including, recombinant DNA technology, cell trafficking, and cloning techniques.
and
 
CSCI-141
Computer Science I
This course serves as an introduction to computational thinking using a problem-centered approach. Specific topics covered include: expression of algorithms in pseudo code and a programming language; functional and imperative programming techniques; control structures; problem solving using recursion; basic searching and sorting; elementary data structures such as lists, trees, and graphs; and correctness, testing and debugging. Assignments (both in class and for homework) requiring a pseudo code solution and an implementation are an integral part of the course. An end-of-term project is also required.
and
 
CSCI-142
Computer Science II
This course delves further into problem solving by continuing the discussion of data structure use and design, but now from an object-oriented perspective. Key topics include more information on tree and graph structures, nested data structures, objects, classes, inheritance, interfaces, object-oriented collection class libraries for abstract data types (e.g. stacks, queues, maps, and trees), and static vs. dynamic data types. Concepts of object-oriented design are a large part of the course. Software qualities related to object orientation, namely cohesion, minimal coupling, modifiability, and extensibility, are all introduced in this course, as well as a few elementary object-oriented design patterns. Input and output streams, graphical user interfaces, and exception handling are covered. Students will also be introduced to a modern integrated software development environment (IDE). Programming projects will be required.
Required Courses
BIOL-130
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.
BIOL-296
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.
BIOL-330
Bioinformatics
Bioinformatics introduces students to the analysis of biological sequences: DNA, mRNA, and protein. Emphasis is placed on classical bioinformatics analyses such as gene prediction, sequence alignment, and phylogenetics. The methods are applicable to both human and model organism studies in medical, biotechnological, and classical biology research.
Electives
Choose two of the following
  BIOL-230
   Bioinformatics Languages
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.
  BIOL-470
   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.
  BIOL-530
   Bioinformatics Algorithms
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.
  BIOL-550
   High Throuput 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.
  BIOL-594
   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.
  BIOL-635
   Bioinformatics Seminar
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.