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Bioinformatics Courses at UT

Overview

There are a wide variety of bioinformatics-related courses at the University of Tennessee (UT), ranging from lecture-based overviews of fundamental concepts to programming to applications of relevant mathematical and statistical approaches.  The graduate level courses taught at the University of Tennessee, Knoxville (UTK) and the University of Tennessee Institute of Agriculture (UTIA) most relevant to the field of bioinformatics are summarized and briefly described here.

Organization

Organization

Efforts are currently underway to organize bioinformatics-themed courses across the UTK and UTIA campuses.  Based on a series of meetings and discussions among faculty with interest in bioinformatics in Spring of 2018, bioinformatics-themed courses have been sorted into three broad categories:

  • Foundational Courses
  • Special Topics Courses
  • Programming and Statistics Courses

Descriptions of specific courses are provided below. For current course information and to find instructor contact information, please consult the UT Course Timetable (link provided beneath each course name).


Foundational Courses for Graduate Studies in Bioinformatics

Bioinformatics practice requires familiarity with:

  1. Unix-based operating systems
  2. Programming / scripting languages commonly used in bioinformatics (e.g., Python, R)
  3. Common methods for analysis of sequence-based data sets (assembly, annotation, multivariate statistics, etc.)

Four courses at UT offer introductory level coverage of these principal bioinformatics skills.  Each of these courses incorporates moderate to heavy “project-based” learning.  While there is some overlap in content, these four courses complement one another in terms of skill-sets and techniques.  Appropriate audience includes biologists looking to get started with bioinformatics theory and practice.

Summary of core bioinformatics skills covered by introductory courses. EPP = Entomology and Plant Pathology; LSFC = Life Science – Genome Science and Technology; GEOL = Geology; MICR = Microbiology

 

EPP 531 / CRN 52303

Special Topics: Statistical Genetics and Genomics

Instructor: Bode Olukolu (bolukolu@utk.edu)

Fall

course flyer

An intensive introduction to modern analytical methods and tools for genetic and -omics data. Class activities will include lectures, review of literature, and computational laboratory sessions.Hands-on experience during laboratory sessions will provide a step-by-step guide through analytical pipelines.

 

EPP 622 / LFSC 696

Bioinformatics Applications

Instructor: Meg Staton

Fall (most recent details)

course webpage: github.com/mestato/epp622/wiki

Fundamental bioinformatics concepts, principles and techniques with a focus on the application of bioinformatics to problems in agriculture. Laboratory practical will be taught within a LINUX computational environment where students will gain basic skills in bash and python scripting and construct open-source software workflows to analyze genomic data.

 

LSFC 507

Programming for Statistical and Graphical Analysis of Biological Data

Fall (most recent details)

Syllabus

Instructor: Tian Hong

Topics to be covered include the application of computing, modeling, data analysis, and information technology to fundamental problems in the life sciences.

 

GEOL 590

Introduction to Reproducible Data Analysis

Instructor: Andrew Steen

Course webpage: https://github.com/adsteen/GEOL590_2017

This course aims to teach introductory principals of reproducible data analysis using the R statistical platform.  No prerequisites are required, however students should be engaged in active research in order to benefit maximally from the class.

Students will learn to more effectively and reproducibly analyze their own data, leading to faster analysis times, deeper analyses, and fewer mistakes.

Class involves lectures and a heavy component of peer education.  Students work in teams of 3-4 throughout the semester to teach one another.

 

MICR 540 / LFSC517

Genomics and Bioinformatics

Fall (most recent details)

Instructor: Karen Lloyd

Course is currently in preparation

Course Objectives:

  • Understand the uses of ‘omics tools in microbial ecology, including the pros and cons of the different techniques.
  • Be capable of analyzing a metagenomic, genomic, single cell genomic, or metatranscriptomic dataset.
  • Be able to design and test hypotheses within those datasets that address microbiological questions.

 


Specialized Bioinformatics Topics

These courses offer lecture and laboratory practice in bioinformatics.  

 

BCMB 422

Computational Biology and Bioinformatics

Spring (most recent details)

This course will introduce students to statistical methods and computational tools for visualizing and analyzing large scale ‘omic data. Students will gain hands-on experience using computational tools to investigate real biomedical research questions that will prepare them for future biological research, study in professional programs, or graduate schools.

 

BCMB 510

Computational Structural Biochemistry

Fall (most recent details)

bioinf-flyer

Introduction to computational tools, internet resources and databases for biological research to analyze and model protein structures and to study protein-ligand interactions.

 

COSC 594 sec 4

Special Topics: Bioinformatics Computing

Spring (Instructor for this section- Scott Joseph Emrich, semrich@utk.edu)

course flyer

Broad overview of bioinformatics with substantial active learning components (in class and using real data for a group project).

Sample topics include: generative models for sequences, pairwise sequence alignment, basic methods in molecular phylogeny and evolution, ab initio gene prediction, whole genome comparisons, genome assembly and analysis.

 

MICR 650

Introduction to high throughput amplicon library preparation and analysis for microbial communities

Fall (most recent details)

Students will use bacterial amplicon high throughput sequencing to analyze the microbiota of a sample of their own choosing. Students will extract DNA from the sample, amplify 16S rRNA regions using several primer sets, and prepare libraries for sequencing on the Illumina MiSeq platform. Students will learn how to analyze and interpret the results. This is a new course with a temporary number, not currently in the course catalog

 


Programming and Statistics Courses

Only a core of courses most relevant to bioinformatics practice are listed here.  Check especially the courses offered by the Computer Science and Statistics departments for more options.

 

COSC 505

Introduction to Programming for Scientists and Engineers

Spring (most recent details)

Introduce programming and computational science and engineering to graduate students in the sciences and engineering. Problem solving and algorithm development. Might use various programming languages such as C++, Python or others as needed.

 

COSC 526

Introduction to Data Mining

Fall (most recent details)

A comprehensive introduction to the field of data mining. Topics covered include data preprocessing, predictive modeling (e.g., decision trees, SVM, Bayes, K-nearest neighbors, bagging, boosting), model evaluation techniques, clustering (hierarchical, partitional, density-based), classification, association analysis, and anomaly detection. Case studies from text mining, electronic commerce, social science, and bioinformatics are covered. All programming projects are student-designed (no standard packages permitted).

 

COSC 565

Databases and Scripting Languages

Spring (most recent details)

Introduction to database theory, models, and query formation. Survey of scripting languages, their uses, and their interconnectivity with databases.

 

 

STAT 576

Multivariate and Data Mining Techniques

Spring (most recent details)

Multivariate normal distribution, data visualization, handling missing data, dimension reduction techniques, supervised learning, clustering, outlier detection, including a team-based project and common data mining software.

 

STAT 577

Data Mining Methods and Applications

Fall (most recent details)

Understanding and application of data mining methods. Data preparation; exploratory data analysis and data visualization; predictive modeling using generalized linear models, decision trees, neural networks; model assessment; cluster analysis; association analysis; and other topics. Use of standard computer packages.

 


Special Topics Courses

Many departments offer additional special topics courses with multiple sections and varying content, often including bioinformatics.  Check with the section topics each semester via the Course Timetable or via departments directly.

 

BCMB 520 Special Topics

GEOL 590/1 Special Problems in Geology

LFSC 595/6 Special Topics in Genome Science and Technology

LFSC 695/6 Advanced Topics in Genome Science and Technology