INFO 101: Intro Bioinformatics Sem
CRN: 42206
Min CR: 1
Schedule Type: Lecture/Lab
Instructor: Elliot J Lefkowitz

Description

Faculty-led seminar course that exposes students to cutting edge research topics and career opportunities in the field of bioinformatics. Students will read assigned articles and be prepared for discussion. Subject matter varies by term and students will take this course during multiple semesters for a maximum of two credits.

INFO 403: Bioinformatics-II
CRN: 45983
Min CR: 3
Schedule Type: Lecture 
Instructor: Ryan L Melvin (P)

Prerequisites

Undergraduate level INFO 302 Minimum Grade of C and Undergraduate level CS 303 Minimum Grade of C

Description

Development of computational algorithms to solve biological questions with a significant problem-solving component. This includes computational techniques such as dynamic programming, optimization, hidden Markov models, graph algorithms, and other mathematical and statistical approaches. In addition, data mining and machine learning methods in computational biology will be covered.

INFO 497: Research in Bioinformatics 
CRN: 43717
Min CR: 0
Max CR: 4
Schedule Type: Undergraduate Research 
Instructor: Elliot J Lefkowitz (P)

Prerequisites

Undergraduate level PSDO 200 Minimum Grade of C and Undergraduate level CS 103 Minimum Grade of C

Description

Research in Bioinformatics for non-honors students under the supervision of a faculty sponsor.

INFO 498: Honors Bioinformatics Research
CRN: 43718
Min CR: 0
Max CR: 4
Schedule Type: Capstone Course Undergraduate Research
Instructor: Elliot J Lefkowitz (P)

Prerequisites

Undergraduate level PSDO 200 Minimum Grade of C and Undergraduate level CS 103 Minimum Grade of C

Description

Honors Research is an innovative course that will provide undergraduate students with an opportunity to engage in rigorous scholarly practice of the core bioinformatics skills necessary for performing independent research. Program faculty will closely work with students to identify a project that explores an area of interest for the student based on the integration of prior learning. Students will be performing bioinformatics analyses on laboratory data or publicly available large-scale data, incorporate quality control and develop software pipelines.

INFO 499: Bioinformatics Capstone
CRN: 44674
Min CR: 3
Schedule Type: Lab Capstone Course
Instructor: Elliot J Lefkowitz (P)

Prerequisites

Undergraduate level INFO 403 Minimum Grade of C and Undergraduate level INFO 404 Minimum Grade of C and Undergraduate level PSDO 200 Minimum Grade of C

Description

Students will be allowed to rotate to different program faculty or continue with their mentor from BY/CS 498 Bioinformatics Capstone Research I. With close mentoring and guidance from program faculty, the student will identify a capstone project or continue their existing bioinformatics project. The capstone project is expected to culminate in a formal scholarly work reflecting integration of the scientific knowledge gained through the project. The scholarly work may take the form or a written manuscript or semester report.

INFO 602: Algorithms in Bioinformatics 
CRN: 42384
Min CR: 3
Schedule Type: Lecture
Instructor: Ryan L Melvin (P)

Prerequisites

Graduate level INFO 601 Minimum Grade of C 

Description

This course introduces various fundamental algorithms and computational concepts for solving questions in bioinformatics and functional genomics. These include graph algorithms, dynamic programming, combinatorial algorithms, randomized algorithms, pattern matching, classification and clustering algorithms, hidden Markov models and more. Each concept will be introduced in the context of a concrete biological or genomic application. A broad range of topics will be covered, ranging from genome annotation, genome reconstruction, microarray data analysis, phylogeny reconstruction, sequence alignments, to variant detection.

INFO 603: Biological Data Management
CRN: 46894
Min CR: 3
Schedule Type: Lecture Online
Instructor: Jake Y Chen (P)

Prerequisites

Graduate level INFO 601 Minimum Grade of C 

Description

The introduction of biological data management concepts, theories, and applications. Basic concepts such as relational data representation, relational database modeling, and relational database queries will be introduced in the context of SQL and relational algebra. Advanced concepts including ontology representation and database development workflow will be introduced. Emerging big data concepts and tools, including Hadoop and NoSQL, will be introduced in the context of managing semi-structured and unstructured data. Application of biological data management in biology will be covered using case studies of high-impact widely used biological databases. A class project will be required of all participants.

INFO 501: Biomedical Informatics Research
3 hours

This course provides an overview of the field of biomedical informatics, including subfields ranging from bioinformatics to public health informatics, from the perspective of research accomplishments and challenges. Each topic will be taken from a historical perspective – where are we now and how did we get here – and then explore the current research directions. There will be an emphasis on underlying concepts, theories, and methods. It is intended for students who are studying applied areas of informatics (including Health Informatics and Nursing Informatics) as well as students who would like to explore the possibility of an informatics research career. Although this course can serve as a survey of the field, it is also intended for students who will pursue research in some area of biomedical informatics. 

INFO 510: Bioinformatics Application Skills
2 hours

This course provides students necessary bioinformatics programming and data skills using Linux, MySQL and R. Linux commands and use of scripting languages will be taught in the context of bioinformatics data processing. Basic and practical database skills will be covered. Basic statistics using R to conduct reproducible research will be taught. Students will learn homology search using BLAST, understand basic next-generation sequencing data processing and analysis pipeline development. The focus will be on practical bioinformatics concepts using scripting/programming applied to data analysis problems

INFO 601/701: Introduction to Bioinformatics
3 hours

Introduction to bioinformatics and computational biology, with emphasis on concepts and application of informatics tools to molecular biology. It covers biological sequence analysis, gene prediction, genome annotation, gene expression analysis, protein structure prediction, evolutionary biology and comparative genomics, bioinformatics databases, cloud computing, basic R-based data analysis, simple programming skills using Perl, Linux/Unix environment and command lines, visual analytics, and social/legal aspects of open science. It will have a class research project component.

INFO 602/702: Agorithms in Bioinformatics
3 hours

This course introduces various fundamental algorithms and computational concepts for solving questions in bioinformatics and functional genomics. These include graph algorithms, dynamic programming, combinatorial algorithms, randomized algorithms, pattern matching, classification and clustering algorithms, hidden Markov models and more. Each concept will be introduced in the context of a concrete biological or genomic application. A broad range of topics will be covered, ranging from gene identification, genome reconstruction, microarray data analysis, phylogeny reconstruction, sequence alignments, to variant detection. Pre-requisite is INFO 701 or with instructor permission.

Prerequisites: INFO 601/701 [Min Grade: C]

INFO 603/703: Biological Data Management
3 hours

The introduction of biological data management concepts, theories, and applications. Basic concepts such as relational data representation, relational database modeling, and relational database queries will be introduced in the context of SQL and relational algebra. Advanced concepts including ontology representation and database development workflow will be introduced. Emerging big data concepts and tools, including Hadoop and NoSQL, will be introduced in the context of managing semi-structured and unstructured data. Application of biological data management in biology will be covered using case studies of high-impact widely used biological databases. A class project will be required of all participants. 

Prerequisites: INFO 601/701 [Min Grade: C]

INFO 604/704: Next-generation Sequencing Data Analysis
3 hours

The introduction of next-generation sequencing (NGS) technologies and the various new genomics applications. Basic analysis begins with NGS data representations using FASTQ, BAM, and VCF files. Major NGS applications in the characterization of DNA, RNA, methylation, ChIP, and chromatin structure analysis will be described. Topics will cover alignment, whole genome de novo assembly, variant detection, third generation sequencing technologies, functional genomics, metagenomics, single cell genomics, genetic diseases and cancer genomics. NGS workflows and translational applications in disease biology and genome medicine will also be emphasized. 

Prerequisites: INFO 601/701 [Min Grade: C]

INFO 611/711: Intermediate Statistical Analysis I
3 hours
Cross listed to BST 621

Students will gain a thorough understanding of basic analysis methods, elementary concepts, statistical models and applications of probability, commonly used sampling distributions, parametric and non-parametric one and two sample tests, confidence intervals, applications of analysis of two-way contingency table data, simple linear regression, and simple analysis of variance. Students are taught to conduct the relevant analysis using current software such as the Statistical Analysis System (SAS).

INFO 690/790: Data Mining and Statistical Learning
3 hours
Cross listed to NUR 790

Students will learn to discover and implement meaningful insights and knowledge from data. This course covers major concepts and algorithms of data mining. The course will be taught using the SAS Enterprise Miner program. The final project will demonstrate all the data mining techniques covered in the course and furthermore expose students working with real data. At the end of the course students will be proficient in utilizing data mining techniques to exploit data patterns and behavior, gain insider understanding of the data, and produce new knowledge that healthcare decision-makers can act upon. Furthermore, SAS Certified Predictive Modeler certification exam will be offered at the end of the course. Instructor permission is required.

INFO 691/791: Bioinformatics Seminar I       
1 hour

For master’s student only. Students will learn how to prepare, present, and critique research presentations in bioinformatics by attending seminar presentations made by presenters. Seminars are presented by graduate students, faculty, visitors, or online speakers. Students must show evidence of prior preparation, active participation, and documented comprehension of the topics. 

Prerequisites: INFO 601/701 [Min Grade: C]

INFO 692/792: Bioinformatics Seminar II 
1 hour

For master’s student only. Students will learn how to prepare, present, and critique research presentations in bioinformatics by attending seminar presentations made by presenters. Seminars are presented by graduate students, faculty, visitors, or online speakers. Students must show evidence of prior preparation, active participation, and documented comprehension of the topics. 

Prerequisites: INFO 691/791 [Min Grade: C]

INFO 693/793: Bioinformatics Journal Club   
2 hours

Students will learn how to read, present, and critique primary research publications in bioinformatics. Journal club participants will present high-impact recent journal publications selected by course instructors and learn how to read the paper, write critiques, and organize analysis insights into review papers. Students must show evidence of prior preparation prior to journal clubs and write critiques to show comprehension of the topics throughout the semester. 

Prerequisites: INFO 691/791 [Min Grade: C]

INFO 698: Bioinformatics Master’s Projects
1-6 hours

Admission to bioinformatics master’s program (Plan B: “Project Option”) is required. Independent study to conduct bioinformatics research projects, guided by the instructor as the mentor. Permission of instructor and graduate program director is required.

INFO 699: Bioinformatics Master’s Thesis Research
1-6 hours

Admission to bioinformatics master’s program (Plan A: “Thesis Option”) is required.

INFO 794: Advanced Bioinformatics Journal Club
2 hours

Students will learn how to read, present, and critique primary research publications in bioinformatics. Journal club participants will present high-impact recent journal publications selected by course instructors and learn how to read the paper, write critiques, and organize analysis insights into review papers. Students must show evidence of prior preparation prior to journal clubs and write critiques to show comprehension of the topics throughout the semester. 

Prerequisites: INFO 793 [Min Grade: P]

INFO 799: Bioinformatics Research for Dissertation
1-12 hours

Admission to candidacy is required

INFO 671: Clinical Informatics Seminar I 
1 hour

For master’s student only. Students will learn how to prepare, present, and critique research presentations in clinical informatics by attending seminar presentations made by presenters. Seminars are presented by graduate students, faculty, visitors, or online speakers. Students must show evidence of prior preparation, active participation, and documented comprehension of the topics.

Prerequisites: INFO 501 [Min Grade: C]

INFO 672: Clinical Informatics Seminar II  
1 hour

For master’s student only. Students will learn how to prepare, present, and critique research presentations in clinical informatics by attending seminar presentations made by presenters. Seminars are presented by graduate students, faculty, visitors, or online speakers. Students must show evidence of prior preparation, active participation, and documented comprehension of the topics. 

Prerequisites: INFO 671 [Min Grade: C]

INFO 673: Clinical Informatics Journal Club
1 hour
Cross listed to GBSC 700-VTQ

This course exposes students to cutting edge research in the field of clinical informatics, including (but not limited to) clinical information systems, electronic health records, decision support systems, and medical expert systems.  The objective is for the student to understand that informatics is not just a field of solutions to be applied to biomedical problems but that it is a hypothesis-driven endeavor in its own right. Presenters will be expected to discuss papers approved by the instructor, with discussion of historical context of the work, comparison with similar papers, and critique of the science presented.

Prerequisites: INFO 671 [Min Grade: C]

INFO 612/712: Visual Analytics for Biomedical Research
3 hours

In this course, we will explore the use of visualization techniques as a concise and effective way to help understand, interpret and communicate complex biological data. Principles of design, visual rhetoric/communication, and appropriate usage will be introduced. We will cover representation of different data types, concentrating on those generated by data-rich platforms such as next-generation sequencing applications, cytometry, and proteomics, and will discuss the use of visualization techniques applied to assessing data quality and troubleshooting. Various topics including dimension reduction, hierarchical visualizations, unsupervised learning, graph theory, networks/layouts and interactivity will be covered. We will review the algorithmic underpinnings of various methods that lead to their appropriate and effective use. Finally, we will review a variety of genomics/bioinformatics-related visualization tools that are available online, and will explore the use of lower-level approaches (like Matlab or R) to create beautiful and effective visualizations.

Prerequisites: INFO 603/703 [Min Grade: C] or permission of the instructor.

INFO 651/751: Systems Biomedicine of Human Microbiota
3 hours

The human microbiota is the collection of microorganisms (bacteria, archaea, fungi and viruses) that reside within human tissues and biofluids. Such resident microorganisms compose the majority of cells in human bodies and are key contributors to human development, health, and disease. However, most studies focus on genomics and microbiome statistical representations alone, while spatial-temporal analysis, multi-source data integration and modeling are necessary to predict and understand interactions between microorganisms, human hosts, and the environment. This course will highlight state-of-the-art microbiome/microbiota research and provide essential training in mathematical, computational and systems biology to derive integrative and predictive models of microbiota-host interactions in the context of human health and disease.

Prerequisites: INFO 601/701 [Min Grade: C] and MA 560 or BME670 [Min Grade: C] or permission of the instructor.

INFO 662/762: Biomedical Applications of Natural Language Processing
3 hours
Cross listed to CS 662

Students will be introduced to Natural Language Processing (NLP) including core linguistic tasks such as tokenization, lemmatization/stemming, POS tagging, parsing and chunking. Applications covered include Named Entity Recognition, semantic role labeling, word sense disambiguation, normalization, information retrieval, question answering and text classification. Applications and data will have a biomedical focus, but no biology or medical background is required.

Prerequisites: INFO 601/701 [Min Grade: C] or permission of the instructor and programming experience equivalent to CS 303/350/355.

INFO 695/795: Special Topics in Bioinformatics
3 hours

Topics of current research interest, such as metagenomics, microbiome, computational medicine, complex systems, deep learning in biology, artificial intelligence in biomedical, and translational bioinformatics applications. May be repeated as different sections taught by different instructors for credit. Permission of instructor is required.

INFO 798: Bioinformatics Independent Study
1-6 hours

Independent study to conduct bioinformatics research projects, guided by the instructor as the mentor. Permission of instructor and graduate program director is required.

INFO 602: Algorithms in Bioinformatics 
CRN: 42384
Min CR: 3
Schedule Type: Lecture
Instructor: Ryan L Melvin (P)

Prerequisites

Graduate level INFO 601 Minimum Grade of C 

Description

This course introduces various fundamental algorithms and computational concepts for solving questions in bioinformatics and functional genomics. These include graph algorithms, dynamic programming, combinatorial algorithms, randomized algorithms, pattern matching, classification and clustering algorithms, hidden Markov models and more. Each concept will be introduced in the context of a concrete biological or genomic application. A broad range of topics will be covered, ranging from genome annotation, genome reconstruction, microarray data analysis, phylogeny reconstruction, sequence alignments, to variant detection.

INFO 603: Biological Data Management
CRN: 46894
Min CR: 3
Schedule Type: Lecture Online
Instructor: Jake Y Chen (P)

Prerequisites

Graduate level INFO 601 Minimum Grade of C 

Description

The introduction of biological data management concepts, theories, and applications. Basic concepts such as relational data representation, relational database modeling, and relational database queries will be introduced in the context of SQL and relational algebra. Advanced concepts including ontology representation and database development workflow will be introduced. Emerging big data concepts and tools, including Hadoop and NoSQL, will be introduced in the context of managing semi-structured and unstructured data. Application of biological data management in biology will be covered using case studies of high-impact widely used biological databases. A class project will be required of all participants.

INFO 604: Next-gen Sequ Data Analysis
CRN: 43768
Min CR: 3
Schedule Type: Lecture Online
Instructor: Jin Chen (P)

Prerequisites

Graduate level INFO 601 Minimum Grade of C 

Description

This course is aimed to equip participants with the essential knowledge and skills required to begin analyzing next-generation sequencing data and carry out some of the most common types of analysis. The topics covered in-depth during this course are the analysis of RNA-Seq, ChIP-Seq data, ATACseq data, and Single-cell data, with an optional Variant Calling session. The sessions will also include Introduction to next-generation sequencing (NGS) technologies, common NGS data analysis issues, applications of sequencing technologies, introduction to bioinformatics file formats (e.g. FASTQ, bam, bed) and bioinformatics toolkits. At the end of this course, participants will have the expertise to perform these data analysis independently.

INFO 691: Bioinformatics Seminar
CRN: 45776
Min CR: 1
Schedule Type: Lecture Seminar
Instructor: Amy Wang (P)

Prerequisites

Graduate level INFO 601 Minimum Grade of C 

Description

For master’s student only. Students will learn how to prepare, present, and critique research presentations in bioinformatics by attending seminar presentations made by presenters. Seminars are presented by graduate students, faculty, visitors, or online speakers. Students must show evidence of prior preparation, active participation, and documented comprehension of the topics.

INFO 697: Biomed Informatics Methods
CRN: 45778
Min CR: 3
Schedule Type: Lecture Online
Instructor: Tiago K Colicchio (P)

Prerequisites

Undergraduate level INFO 696 Minimum Grade of C 

Description

Biomedical informatics is the art and science of collecting, representing and analyzing patient and biomedical information and translating insights from the information into better health and new medical discoveries. The spectrum of informatics applications ranges from molecules (bioinformatics) to individuals and populations (clinical and public health informatics). We will examine the scientific field and research methods that form the foundation for biomedical informatics research. The course will include didactics, readings, and applications in applying research methods, culminating in a research plan in grant proposal format and review by a mock panel. This foundational course is intended for informatics majors and students in allied fields (e.g., health, biological, or computer sciences) who are interested in exploring the field of informatics. It is primarily intended for students who will pursue research careers in biomedical informatics and is the second course in a two-part series.

INFO 703: Biological Data Management
CRN: 46895
Min CR: 3
Schedule Type: Lecture 
Instructor: Jake Y Chen (P)

Prerequisites

Graduate level INFO 701 Minimum Grade of C 

Description

The introduction of biological data management concepts, theories, and applications. Basic concepts such as relational data representation, relational database modeling, and relational database queries will be introduced in the context of SQL and relational algebra. Advanced concepts including ontology representation and database development workflow will be introduced. Emerging big data concepts and tools, including Hadoop and NoSQL, will be introduced in the context of managing semi-structured and unstructured data. Application of biological data management in biology will be covered using case studies of high-impact widely used biological databases. A class project will be required of all participants.

INFO 704: Next-gen Sequ Data Analysis
CRN: 43851
Min CR: 3
Schedule Type: Lecture 
Instructor: Jin Chen (P)

Prerequisites

Graduate level INFO 701 Minimum Grade of C 

Description

This course is aimed to equip participants with the essential knowledge and skills required to begin analyzing next-generation sequencing data and carry out some of the most common types of analysis. The topics covered in-depth during this course are the analysis of RNA-Seq, ChIP-Seq data, ATACseq data, and Single-cell data, with an optional Variant Calling session. The sessions will also include Introduction to next-generation sequencing (NGS) technologies, common NGS data analysis issues, applications of sequencing technologies, introduction to bioinformatics file formats (e.g. FASTQ, bam, bed) and bioinformatics toolkits. At the end of this course, participants will have the expertise to perform these data analysis independently.

INFO 791: Bioinformatics Seminar I
CRN: 45775
Min CR: 1
Schedule Type: Lecture 
Instructor: Amy Wang (P)

Prerequisites

Graduate level INFO 701 Minimum Grade of C 

Description

For doctoral student only. Students will learn how to prepare, present, and critique research presentations in bioinformatics by attending seminar presentations made by presenters. Seminars are presented by graduate students, faculty, visitors, or online speakers. Students must show evidence of prior preparation, active participation, and documented comprehension of the topics.

INFO 797: Biomed Informatics Methods
CRN: 45777
Min CR: 3
Schedule Type: Lecture Online 
Instructor: Tiago K Colicchio (P)

Prerequisites

Graduate level INFO 701 Minimum Grade of C 

Description

Biomedical informatics is the art and science of collecting, representing and analyzing patient and biomedical information and translating insights from the information into better health and new medical discoveries. The spectrum of informatics applications ranges from molecules (bioinformatics) to individuals and populations (clinical and public health informatics). We will examine the scientific field and research methods that form the foundation for biomedical informatics research. The course will include didactics, readings, and applications in applying research methods, culminating in a research plan in grant proposal format and review by a mock panel. This foundational course is intended for informatics majors and students in allied fields (e.g., health, biological, or computer sciences) who are interested in exploring the field of informatics. It is primarily intended for students who will pursue research careers in biomedical informatics and is the second course in a two-part series.