500

A foundation course in cell biology and genome science for the non-life scientist or for life scientists who need a refresher prior to studying bioinformatics. Explore the dynamics of gene expression at the level of DNA, RNA and protein. Develop ability to link techniques in molecular biology with appropriate applications in explaining the scientific approach to gene analysis.

This course provides students with an introduction to programming concepts and techniques used in problem solving. Students will study general programming concepts for the purpose of
data analysis. These concepts are demonstrated through the use of a modern programming language. Students will design, implement and test programs to solve analytical problems.
Students will develop the ability to logically plan and develop programs, and learn to write, test, and debug programs. Topics include file I/O, expressions, types, variables, branching, loops,
data access, data profiling, and data manipulation. Students will apply their knowledge through hands-on programming projects.

After a brief introduction to working in R, this course will focus on the statistical concepts that are used in biology and medicine to analyze and validate data. Topics will include probability,
hypothesis testing, tests for variables (e.g. chi-square, Fisher’s test), t-test, linear and multivariate regression, covariance and Bayesian statistic.

Prerequisite: BIFX 501 or waiver of BIFX 501 or permission of instructor

The accelerated use of next generation sequencing means that the analysis of sequencing data is one of primary job duties of most bioinformaticians. The DNA/RNA sequencing boom is now being followed by a renewed focus on metagenomics as well as high-throughput protein sequencing as analytical techniques. In this course, students will gain detailed knowledge of the biology that underlies these and other techniques. By understanding the full range of transcripts made by cells, the mechanisms that regulate transcription, and the details of RNA transcript processing and translation, students will learn how the underlying biology affects the sensitivity and correct interpretation of key types of bioinformatics assays, including microarrays, genome-wide association studies, and sequencing of DNA, RNA, proteins, and the microbiome. The fundamentals of good experimental design will be emphasized throughout the course.

Prerequisite: A minimum grade of "B-" in BIFX 502 or CSIT 512 and BIFX 551 or permission of the instructor. Not open to students who have completed CS 530. 

A study of the design and implementation of databases from a real-world applications point of view. The course will explore the enterprise perspective of managing data needs of an organization. Topics include data integrity, database models, logical database design, the integration of databases, security,and database administration issues. The course will also address topics such as assessing enduser needs, developing specifications, and designing functionally equivalent solutions. The student will be introduced to query processing within a database environment.

Based on the tenants of the "reproducible research" movement, this course teaches the fundamentals of experimental design, research ethics and communication to foster the ability to coordinate multifaceted research collaborations between scientists with backgrounds in biology, computer science and biostatistics.  Using examples from classical literature and the concepts extolled by philosophers from around the world, we will explore alternative modes of leadership based on effective communication between individuals from different backgrounds.  While this course is essential for those in the field of bioinformatics, anyone who is interested leading high-quality research will benefit from this course.

Prerequisites: BIFX 501 or BIFX 502 or CSIT 512 and BIFX 503

This course on machine learning will provide students with more advanced methods to analyze data using both R and Python, allowing them to computationally represent biological data that can then be used to solve complex problems. Topics that will be covered include regression, classification (nearestneighbor methods, decision-tree based methods) and clustering. A large section of the class will be devoted to modern approaches of neural networks and deep learning including convolutional neural networks and reinforcement learning. Approaches for developing expert
systems will also be covered based on formal logic, ontologies and technologies for the semantic web.

Prerequisites: BIFX 501 or BIFX 502. 

The goal of this course is to provide the students with a more in-depth knowledge of web-based bioinformatics tools and other freely available tools. The course will emphasize a hands-on approach using available web-based tools and public domain data. Secondly the class will cover the foundation for developing novel web-resources using HTML, CSS, Javascript and web frameworks such as Shiny and Django.

Prerequisite: BIFX 551 or permission of the instructor. 

Basic knowledge of programming in R is required. Data visualization is a sub-area of Human-Computer Interaction (HCI). Students will learn the theories and tools of data visualization. This course covers the basic theories of data visualization, such as data types, chart types, visual variables, visualization techniques, structure of data visualization, navigation in data visualization, color theory, cognitive theory, and visualization evaluation. Various frameworks for data visualization will be used such as ggplot in R and Tableau.

Prerequisite: Completion of BIFX 504 or instructor permission. 

This course will use sequence and structural information to solve biological disease-centered problems. BIFX550 will provide the knowledge to study both individual and collections of genes/proteins (NGS data) with the goal of modeling the functional impact of genomic variants. Functional genomics has applications in several areas of health sciences including drug-discovery. This course serves as an intermediate level class for graduate students who plan to work in the areas of computational biology or bioinformatics using available applications and/or interested in
developing custom R or Linux-based pipeline(s)/script(s). In BIFX550, students will work on a
semester-long functional genomics (find-a-gene) project where students will apply the knowledge gained in the classes to identify a novel gene. While completing the project, students will gain a theoretical understanding of common bioinformatics tasks and learn to effectively apply the relevant software applications (ex. NCBI's sequence comparison software, BLAST) to solve problems. BIFX550, will introduce the essential mathematics and statistics before introducing each application. Protein function depends on its 3D structure/folding. BIFX550 will help students make the connection from 1D DNA/RNA sequence to their corresponding 3D protein(s). To address the shifting trends in computational genomics research from the High-Performance-Computing/CPU-dependent platforms to Tensor-Processing-Unit/cloud-based solutions, this course will teach how to install/use important software, develop SHELL scripts in Linux-based environments and learn web-based (Galaxy; NCBI) resources.

Prerequisites: A minimum grade of "B-" in BIFX 502 or CSIT 512 or MGMT 566 or IT 518; or permission of the instructor.

This class introduces the R programming language and advanced concepts and techniques to discover patterns in data. Students will explore datasets by identifying variables with the most predictive power, and developing and assessing predictive models in R. Discussion topics include exploratory data analysis, visualization, and data transformation. Students will implement the following data mining techniques: regression, neural networks, classification, clustering, principal component analysis, and survival analysis. Advanced techniques such as bagging, boosting, and random forests will also be explored. Significant time will be spent learning to program within an integrated development environment and implementing version tracking.

Prerequisites: A minimum grade of "B-" in BIFX 551 or permission of the instructor. 

This class introduces applied data science skills needed by bioinformatics professionals. A focus will be placed on reproducible bioinformatics research and will include the following topics and tools:beginning to intermediate use of the Unix command line, working with remote computing resources, version tracking, R and Bioconductor, tools for manipulating sequence data, and
creation of pipelines.

Prerequisites: A minimum grade of "B-" in BIFX 552 or permission of the instructor.

Students will continue to develop the data science skills learned in BIFX 552 while gaining a deeper understanding of statistics and machine learning by performing real-world analyses of biological
data. A deeper understanding will be gained of what can go wrong in data analyses, and principles of reproducible research will be emphasized. Students will use the highperformance-
computing cluster using R as well as important application programs for processing sequencing data. A large portion of the class will be devoted to processing high-throughput sequencing data (RNA-Seq, whole genome sequencing, whole exome sequencing, variant analysis, metagenomics). Additional possible topics are genomewide association studies (GWAS), phylogenetics, and the analysis of protein motifs and protein domains.

Prerequisites: A minimum grade of "B-" in BIFX 504 and BIFX 552 or permission of the instructor. 

This course provides a practical, hands-on experiential learning opportunity for Bioinformatics students and emphasizes experimental design and biological interpretation of results. Students will apply a wide variety of concepts and skills that they have learned throughout previous courses to perform a full start-to-finish analysis and interpretation of reallife RNA sequencing data. And will compare the methods used to for RNA sequencing analysis to those used for microarray and metagenomic (microbiome) studies. Over the course of the semester, students will utilize best practices for conducting robust, reproducible research and will generate a series of workflow scripts that can be adapted and reused to perform future work. This course serves as the capstone for the Bioinformatics Certificate and is a recommended practicum for Bioinformatics Masters’ students.

Prerequisites: Completion of 18 BIFX credits including a minimum grade of “B-“ in BIFX 504 and in BIFX 552, or permission of the instructor. 

This course provides a practical, hands-on experiential learning opportunity for Bioinformatics MS students and emphasizes experimental design and biological interpretation of results. Students will utilize a wide variety of concepts and skills that they have learned throughout previous courses to complete an original research project from start to finish. Over the course of the semester, students will share their questions and insights during frequent “lab meeting” style presentations, and will work both independently and collaboratively to complete their projects. They will present their work in the form of a well annotated script that contains all of the code necessary to complete the project, a brief paper describing the project in paragraph form, and a final poster presentation of their results. Students are encouraged to collaborate with their employer or an outside laboratory to propose a project to the course instructor during the first week of class. Alternatively, project ideas and data will be provided by the instructor. Computational resources will be provided via the Bioinformatics program’s High-Performance Computing Cluster.

Prerequisites: Completion of 18 BIFX credits including BIFX 503, BIFX 550, and BIFX 551

Part I of the preparation of the master's thesis includes development of the research proposal, design of the study, data acquisition, and data processing.

Prerequisites: Completion of 18 BIFX credits including BIFX 503, BIFX 550, BIFX 551, and completion or concurrent enrollment in BIFX 580A. 

Part II of the preparation of the master's thesis includes data analysis and interpretation and writing and defense of the thesis.