Bioinformatics, Genomics, and Computational Biology Courses

graduate courses

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MS & Cert PhD Course Description
BINF 5171 - Business of Biotechnology
BINF 5191 - Biotechnology and the Law
BINF 6100 BINF 8100 Biological Basis of Bioinformatics
BINF 6101 BINF 8101 Energy and Interaction in Biological Modeling
BINF 6111 BINF 8111 Bioinformatics Programming I
BINF 6112 BINF 8112 Bioinformatics Programming II
BINF 6200 BINF 8200 Statistics for Bioinformatics
BINF 6201 BINF 8201 Molecular Sequence Analysis
BINF 6202 BINF 8202 Computational Structural Biology
BINF 6203 BINF 8203 Genomics
BINF 6204 BINF 8204 Mathematical Systems Biology
BINF 6205 BINF 8205 Computational Molecular Evolution
BINF 6210 BINF 8210 Numerical Methods and Machine Learning for Bioinformatics
BINF 6211 BINF 8211 Design and Implementation of Bioinformatics Databases
BINF 6310 BINF 8310 Advanced Statistics for Genomics
BINF 6311 BINF 8311 Biophysical Modeling
BINF 6312 BINF 8312 Computational Comparative Genomics
BINF 6313 BINF 8313 Structure, Function, and Modeling of Nucleic Acids
BINF 6318 BINF 8318 Computational Proteomics and Metabolomics
BINF 6350 BINF 8350 Biotechnology and Genomics Laboratory
BINF 6380 BINF 8380 Advanced Bioinformatics Programming
BINF 6382 BINF 8382 Accelerated Bioinformatics Programming
BINF 6400 - Internship Project
BINF 6600 BINF 8600 Seminar
BINF 6880 - Independent Study
- BINF 8911 Research Rotation I
- BINF 8912 Research Rotation II
- BINF 8991 Doctoral Dissertation Research

BINF 5171 Business of Biotechnology
Introduces students to the field of biotechnology and how biotech businesses are created and managed. Students should be able to define biotechnology and understand the difference between a biotech company and a pharmaceutical company. Additional concepts covered will include platform technology, biotechnology's history, biotechnology products and development processes, current technologies used by biotech companies today, biotechnology business fundamentals, research and development within biotech companies, exit strategies, and careers in the biotech field. (Fall)
Credits: 3 Category: Professional Preparation Course Syllabus

BINF 5191 Life Sciences and the Law
Law and regulations permeate our daily lives, and nowhere is this truer than in areas of life sciences.  This course explores what the law is, how our current laws developed, and factors currently affecting the evolution of the law.  It provides a general overview of U.S. law, including constitutional law, criminal law, contract law, tort law, property law (especially intellectual property law), business law (especially legal aspects of forming a new company), and administrative law.  It then focuses on specific aspects of the law affecting the life sciences, such as ownership of tissues and organisms, regulation of drugs and medical devices, regulation of research in the life sciences, the history and regulation of medicine, the economics and various types of health care delivery, and food production. (Spring)
Credits: 3 Category: Professional Preparation Course Syllabus

BINF 6100 | BINF 8100 Biological Basis of Bioinformatics
Prerequisites: Admission to graduate standing in Bioinformatics and undergraduate training in Computer Science or other non-biological discipline. Provides a foundation in molecular genetics and cell biology focusing on foundation topics for graduate training in bioinformatics and genomics. (Fall)
Credits: 3 Category: Fundamentals Syllabus

BINF 6101 | BINF 8101 Energy and Interaction in Biological Modeling
Prerequisites: Admission to graduate standing in Bioinformatics. This course covers: (a) the major organic and inorganic chemical features of biological macromolecules; (b) the physical forces that shape biological molecules, assemblies and cells; (c) the chemical driving forces that govern living systems; (d) the molecular roles of biological macromolecules and common metabolites; (e) and the pathways of energy generation and storage. Each section of the course builds upon the relevant principles in biology and chemistry to explain the most common mathematical and physical abstractions used in modeling in the relevant context. (Spring)
Credits: 3 Category: Fundamentals Syllabus 

BINF 6111 | BINF 8111 Bioinformatics Programming I
Prerequisites: Admission to graduate standing in Bioinformatics or permission of instructor. The course grade includes the student's performance in BINF 6111L, which is a required co-requisite. This course introduces fundamentals of programming for bioinformatics using a high-level object-oriented language such as Python. The first weeks cover core data types, syntax, and functional programming, focusing on construction of programs from small, testable parts. Students will learn productive use of the Unix environment, focusing on Unix utilities that are particularly useful in bioinformatics. The course will cover object-oriented programming, introduce analysis of algorithms and sequencing alignment methods, and introduce tools that are particularly useful in bioinformatics analysis such as R, BioPython, and Web services in bioinformatics. By the end of the class, students will have gained the ability to analyze data within the python interpreter and write well-documented, well-organized programs. (Fall)
Credits: 3 Category: Fundamentals Syllabus

BINF 6111L | BINF 8111L Bioinformatics Programming I Laboratory
Corequisite: BINF 6111. Students will gain hands-on experience in programming to solve bioinformatics problems. (Fall)
Credits: 0 Category: Fundamentals 

BINF 6112 | BINF 8112 Bioinformatics Programming II
Prerequisite: BINF 6111 or permission of instructor. This is a continuation of Bioinformatics Programming I (BINF 6111). The course grade includes the student's performance in BINF 6112L, which is a co-requisite. In this second semester, students will practice and refine skills learned in the first semester. New topics introduced will include: programming as part of a team, using sequence analysis algorithms in realistic settings; writing maintainable and re-usable code; Web programming; and graphical user interface development. At the end of the semester, students will be able to evaluate and deploy computer languages, tools, and software engineering techniques in bioinformatics research. (Spring)
Credits: 3 Category: Fundamentals Syllabus

BINF 6112L | BINF 8112L Bioinformatics Programming II Laboratory
Corequisite: BINF 6112 or permission of instructor. Students will gain hands-on experience in programming to solve bioinformatics problems. (Spring)
Credits: 0 Category: Fundamentals 

BINF 6200 | BINF 8200 Statistics for Bioinformatics
Corequisite: BINF 6200L. Prerequisite: Permission of the department. The course grade includes the student's performance in BINF 6200L, which is a required co-requisite. Introduces students to statistical methods commonly used in bioinformatics. Basic relevant concepts from probability, stochastic processes, information theory, and other statistical methods will be introduced and illustrated by examples from molecular biology, genomics and population genetics with an outline of algorithms and software. R is introduced as the programming language for homework. (Fall)
Credits: 3 Category: Core Syllabus

BINF 6200L | BINF 8200L Statistics for Bioinformatics Laboratory
Corequisite: BINF 6200. The aim of this lab course is to introduce R and its application in solving common statistical problems in bioinformatics. Basic relevant concepts from probability, probability distributions, and statistical inference will be introduced and illustrated by examples from bioinformatics applications using R. (Fall)
Credits: 0 Category: Core

BINF 6201 | BINF 8201 Molecular Sequence Analysis
Prerequisite: BINF 6100 and 6200 or equivalent. The course grade includes the student's performance in BINF 6201L, which is a required co-requisite. This course introduces the basic computational methods and open sources software commonly used in molecular sequence analysis. The course covers biological sequence data formats and major public databases, concepts of computer algorithms and complexity, introductions to principle components analysis and data clustering methods, dynamics of genes in populations, evolutionary models of DNA and protein sequences, derivation of amino acid substitution matrices, algorithms for pairwise sequence alignments and multiple sequence alignments, algorithms for fast sequence database search, methods for molecular phylogenetic analysis, and hidden Markov models and neural networks for sequence pattern and family recognition, and introductions to genome evolution and omics data analysis. (Spring)
Credits: 3 Category: Core Syllabus

BINF 6201L | BINF 8201L Molecular Sequence Analysis Laboratory
Corequisite: BINF 6201. Prerequisite: BINF 6100 and 6200 or equivalent. This course provides hands-on experience with common software methods for biological sequence data analysis. Topics include: basic UNIX utilities, principle component analysis, clustering analysis, global and local pair-wise sequence alignments, multiple sequence alignments, sequence database search methods, phylogenetic tree constructions, hidden Markov models and neural networks. (Spring)
Credits: 0 Category: Core Syllabus

BINF 6202 | BINF 8202 Computational Structural Biology
Corequisite: BINF 6202L. Prerequisite: BINF 6101, 6201 or equivalents. The course grade includes the student's performance in BINF 6202L, which is a required co-requisite. This course will cover: (a) the fundamental concepts of structural biology (chemical building blocks, structure, superstructure, folding, etc.); (b) structural databases and software for structure visualization; (c) Structure determination and quality assessment; (d) protein structure comparison and the hierarchical nature of biomacromolecular structure classification; (e) protein structure prediction and assessment; and (f) sequence- and structure-based functional site prediction. (Fall)
Credits: 3 Category: Core Syllabus

BINF 6202L | BINF 8202L Computational Structural Biology Laboratory
Corequisite: BINF 6202. This course covers being able to correctly use and apply (a) structural classification databases; (b) software for visualization of biological structures; (c) computational methods to evaluate and compare biological structures; (d) computational methods to align biological structures; and (f) computational methods to predict biological structures from sequence. (Fall)
Credits: 0 Category: Core

BINF 6203 | BINF 8203 Genomics
Prerequisite: BINF 6100 or equivalent. Surveys the application of high-throughput molecular biology and analytical biochemistry methods and data interpretation for those kinds of high volume biological data most commonly encountered by bioinformaticians. The relationship between significant biological questions, modern genomics technology methods, and the bioinformatics solutions that enable interpretation of complex data is emphasized. Topics include: genome sequencing and assembly, annotation, and comparison; genome evolution and individual variation; function prediction; gene ontologies; transcription assay design, data acquisition, and data analysis; and metabolic pathways and databases and their role in genome analysis. Course grade includes performance in BINF 6203L. (Spring)
Credits: 3 Category: Core Syllabus

BINF 6203L | BINF 8203L Genomics Laboratory
Corequisite: BINF 6203. Prerequisite: BINF 6100 or equivalent. This course provides hands-on experience with software methods for genome-scale data analysis. Topics include: genome sequencing and assembly, genome annotation, genome comparison, functional classification and gene ontologies, genome evolution and individual variation, transcriptomic and epigenetic assay design, data acquisition, and data analysis. (Spring)
Credits: 0 Category: Core

BINF 6204 | BINF 8204 Mathematical Systems Biology
Prerequisites: BINF 6200 and 6210 or equivalents. The course introduces basic concepts, principles and common methods in systems biology. The class emphasizes on molecular networks, models and applications, and covers the following topics: the structure of molecular networks; network motifs and their systems properties and roles they play in biological processes; complexity and robustness of molecular networks; hierarchy and modularity of molecular interaction networks; kinetic proofreading; optimal gene circuit design; rules for gene regulation. (Spring)
Credits: 3 Category: Core Syllabus

BINF 6205 | BINF 8205 Computational Molecular Evolution
Prerequisites: BINF 6201 (Molecular Sequence Analysis) and BINF 6200 Statistics for Bioinformatics (or permission of the instructor). This course covers major aspects of molecular evolution and phylogenetics with an emphasis on the modeling and computational aspects of the fields. Topics will include: models of nucleotide substitution, models of amino acid and codon substitution, phylogenetic reconstruction, maximum likelihood methods, Bayesian methods, comparison of phylogenetic methods and tests on trees, neutral and adaptive evolution and simulating molecular evolution. Students will obtain an in-depth knowledge of the various models of evolutionary processes, a conceptual understanding of the methods associated with phylogenetic reconstruction and testing of those methods and develop an ability to take a data-set and address fundamental questions with respect to genome evolution. (On demand)
Credits: 3 Category: Core Genomics Syllabus

BINF 6210 | BINF 8210 Machine Learning for Bioinformatics
Prerequisites: Calculus and BINF 6200/6200L. The aim of this course is to introduce commonly used machine learning methods in the field of bioinformatics. Topics include dimension reduction using principal component analysis, singular value decomposition, linear discriminant analysis, clustering using k-means, hierarchical, expectation maximization approaches, classification using k-nearest neighbor and support vector machines. To help understand these methods, basic concepts from linear algebra, optimization, and information theory will be explained. Application of these machine learning methods to solving bioinformatics problems will be illustrated using examples from the literature. (Fall)
Credits: 3 Category: Core Syllabus

BINF 6211 | BINF 8211 Design and Implementation of Bioinformatics Databases
Corequisite: BINF 6211L. Prerequisite: permission of instructor. The course grade includes the student's performance in BINF 6211L, which is a required co-requisite. This course introduces the fundamentals of database modeling as used in bioinformatics. By the end of the course the student should be able to: understand different types of data models, know how hierarchical and relational models work and give examples that are widely used for biological databases, understand the capabilities of a standard, open source RDBMS, understand the tasks required for data integration and how to use SQL as a research tool. Students will be introduced to XML and XML Schema, and BioOntologies, as widely used data exchange and organization tools in bioinformatics databases. (Spring)
Credits: 3 Category: Core Syllabus

BINF 6211L | BINF 8211L Design and Implementation of Bioinformatics Databases Laboratory
Corequisite: BINF 6211. Prerequisite: permission of instructor. Students will practice skills described in the lecture, particularly design principles for the relational model and using SQL. Students will complete projects in which they design, implement, protoype and use a research biological database. Students will be able to obtain correctly formatted data from public repositories and will know how to use XML, XML Schema and BioOntologies as tools in the data integration process. Students will learn to use SQL to create, populate and perform complex queries on genomics databases. (Spring)
Credits: 0 Category: Core

BINF 6215  Bioinformatics Pipeline Programming
Prerequisite: BINF 6203. This course covers the concept of pipelines - assemblies of basic bioinformatics tools and data sources to solve complex data processing problems. The pipeline concept will be introduced with simple UNIX command line methods and then extended to the use of preconfigured commercial and extensible open-source workflow management systems. Reproducibility of analysis, collection of analytic provenance information, and database integration will also be covered. (On demand)
Credits: 3 Category: Elective Syllabus

BINF 6310 | BINF 8310 Advanced Statistics for Genomics
Corequisite: BINF 6310L. Prerequisite: BINF 6200 or equivalent. The course grade includes the student's performance in BINF 6310L, which is a required co-requisite. The class covers canonical linear statistics (t-test, ANOVA, PCA) and their non-parametric equivalents. In addition, we will examine the application of Bayesian statistics, Hidden Markov Models and machine learning algorithms to problems in bioinformatics. Students should have fluency in a high-level programming language (PERL, Java, C#, Python or equivalent) and will be expected, in assignments, to manipulate and analyze large public data sets. The course will utilize the R statistical package with the bioconductor extension. (On demand)
Credits: 3 Category: Core Computational Syllabus

BINF 6310L | BINF 8310L Advanced Statistics for Genomics Laboratory
Corequisite: BINF 6310. Prerequisite: BINF 6200 or equivalent. This is the laboratory class to accompany BINF 6310. This class allows the students to gain hands-on experience with using the R programming language. (On demand)
Credits: 0 Category: Core Computational

BINF 6311 | BINF 8311 Biophysical Modeling
This course will cover: (a) overview of mechanical force fields; (b) energy minimization; (c) dynamics simulations (molecular and coarse-grained); (d) Monte-Carlo methods; (e) systematic conformational analysis (grid searches); (f) classical representations of electrostatics (Poisson-Boltzmann, Generalized Born and Colombic); (g) free energy decomposition schemes; and (h) hybrid quantum/classical (QM/MM) methods. (On demand)
Credits: 3 Category: Elective

BINF 6312 | BINF 8312 Computational Comparative Genomics
Prerequisite: BINF 6201 or equivalent. This course introduces computational methods for comparative genomics analyses. The course covers the following topics: the architecture of prokaryotic and eukaryotic genomes; the evolutionary concept in genomics; databases and resources for comparative genomics; principles and methods for sequence analysis; evolution of genomes; comparative gene function annotation; evolution of the central metabolic pathways and regulatory networks; genomes and the protein universe; cis-regulatory binding site prediction; operon and regulon predictions in prokaryotes; regulatory network mapping and prediction. (On demand)
Credits: 3 Category: Elective Syllabus

BINF 6313 | BINF 8313 Structure, Function, and Modeling of Nucleic Acids
Prerequisite: BINF 6100-6101 or equivalent. The course covers the following topics: atomic structure, macromolecular structure-forming tendencies and dynamics of nucleic acids; identification of genes which code for functional nucleic acid molecules, cellular roles and metabolism of nucleic acids; 2D and 3D abstractions of nucleic acid macromolecules and methods for structural modeling and prediction; modeling of hybridization kinetics and equilibria; hybridization-based molecular biology protocols, detection methods and molecular genetic methods, and the role of modeling in designing these experiments and predicting their outcome. (On demand)
Credits: 3 Category: Electives 

BINF 6318 | BINF 8318 Computational Proteomics and Metabolomics
Prerequisite: BINF 6200 or equivalent.  This 3-credit hour course introduces commonly used computational algorithms, software tools, and databases for analyzing mass spectrometry-based proteomics and metabolomics data. Students will learn: 1) how to implement algorithms for processing raw mass spectrometry data and extracting qualitative and quantitative information about proteins and metabolites, 2) how to align multiple datasets, 3) how to perform differential analysis of proteomics and metabolomics datasets, and 4) how to use commonly used protein and metabolite databases. The course also introduces chromatography, mass spectrometry , and isotopic patterns of proteins and metabolites to provide background information for students to understand the nature of mass spectrometry data. (On demand)
Credits: 3 Category: Electives 

BINF 6350 | BINF 8350 Biotechnology and Genomics Laboratory
Co-requisite: BINF 6350L. Prerequisite: A background in molecular biology and biochemistry or the permission of the instructor. The course grade includes the student's performance in BINF6350L, which is a required co-requisite. This course introduces students to the molecular biological methods by which samples are converted to a state from which sequence information can be produced. When sequence data is produced in a highly parallel fashion across a large fraction of a genome it is the basis of genomics. For historical reasons the sample put on a sequencer is called a library, and the art of genomics lies in library construction. The experimental design and the technical details of library construction will significantly affect the analyses that are appropriate and the conclusions that can be made. Lectures cover the design of experiments, how to critically read the literature to select an appropriate protocol for a variety of experimental purposes, and follow it to transform a sample into high quality sequence data. Quality control and library validation methods will be explained. Topics will include selecting applications tuned to the experiment design to ensure proper data analysis and interpretation. (Fall)
Credits: 3 Category: Elective Syllabus

BINF 6350L | BINF 8350 Biotechnology and Genomics Hands on Laboratory
Co-requisite: BINF 6350. Students will gain hands-on experience producing sequencing templates and libraries, discussed in the lecture. The lab introduces students to the practical skills needed to carry out a series of experiments that result in sequence data. The unifying concept will be to characterize allelic variants of selected genes from related organisms. Students will purify nucleic acid and then produce a selected subset of each genome using PCR. Quality control via spectroscopy, gel electrophoresis and quantitative PCR will be performed. Sequencing libraries will be produced and run on the Ion Torrent PGM and the ABI 3130 Genetic Analyzer. The CLCbio Genomics Workbench software for assessing data quality and identifying polymorphisms will be utilized. Students are expected to keep laboratory notebooks that allow all aspects of experiments to be reconstructed. (Fall)
Credits: 0 Category: Elective

BINF 6380 | BINF 8380 Advanced Bioinformatics Programming
Co-requisite: BINF 6380L. Pre-requisites: BINF 6112 or equivalent or permission of instructor. The course grade includes the student's performance in BINF 6380L, which is a required co-requisite. Advanced algorithms in bioinformatics with an emphasis placed on the implementation of bioinformatics algorithms in the context of parallel processing.  Topics covered depend on instructor expertise and student interest, but may include assembly of short read fragments from next-generation sequencing platforms, clustering algorithms, machine learning, development of multi-threaded applications, developing for multi-core processors and utilization of large clusters and “cloud” supercomputers.  Students are expected to complete a significant independent project. (On demand)
Credits: 3 Category: Elective Syllabus

BINF 6380L | BINF 8380L Advanced Bioinformatics Programming
Co-requisite: BINF 6380. Pre-requisites: BINF 6112 or equivalent. This is the lab component of BINF 6380. The goal of this class is to obtain hands-on experience with multi-threaded programming. (On demand)
Credits: 0 Category: Elective

BINF 6382 | BINF 8382 Accelerated Bioinformatics Programming
Co-requisite: BINF 6382L. Pre-requisites: BINF 6112 or equivalent or permission of instructor.  The course grade includes the student's performance in BINF 6382L, which is a required co-requisite. Computationally intensive algorithms in bioinformatics with an emphasis placed on the implementation of bioinformatics algorithms in the context of parallel processing using modern hardware processor accelerators such as GPUs and FPGAs.  Topics covered depend on instructor expertise and student interest but may include multi-threaded applications and developing for multi-core processors and for large clusters and other “cloud” computers.  Students will be expected to complete a significant independent project. (On demand)
Credits: 3 Category: Elective

BINF 6382L | BINF 8382L Accelerated Bioinformatics Programming
Co-requisite: BINF 6382L. Pre-requisites: BINF 6112 or equivalent or permission of instructor.  This is the lab component of BINF 6382. The goal of the class is to obtain hands-on experience with accelerated programming in bioinformatics. (On demand)
Credits: 0 Category: Elective

BINF 6399 | Principles of Team Science
Prerequisite: Department approval. This course will teach students appropriate project design, implementation and management skills needed to function as a small team solving typical problems in Bioinformatics. The students will be given a realistic problem and be required to develop specifications, deliverables, timelines, and costs. Under faculty supervision, the group will assign roles, responsibilities, and deadlines in order to complete the project and then execute the project. At the end of the course, the group will produce a written document with deliverables, and make a formal presentation of the project. (On demand)
Credits: 3 Category: Elective

BINF 6400 Internship Project
Project chosen and completed under the guidance of an industry partner, which results in an acceptable technical report. (Fall, Spring)
Credits: 3 Category: Research

BINF 6600 | BINF 8600 Seminar
Prerequisite: Admission to graduate standing in Bioinformatics. Departmental seminar. Weekly seminars will be given by bioinformatics researchers from within UNC Charlotte and across the world. (Fall, Spring)
Credits: 1 Category: Seminar

BINF 6601 Journal Club
Prerequisites: Admission to graduate standing in Bioinformatics. Each week, a student in the class is assigned to choose and present a paper from the primary bioinformatics literature. (Fall, Spring)
Credits: 1 Category: Seminar

BINF 6880 Independent Study
Faculty supervised research experience to supplement regular course offerings. (On demand)
Credits: 1-3 Category: Elective

BINF 6900 Master's Thesis
Prerequisites: 12 graduate credits and permission of instructor. Project chosen and completed under the guidance of a graduate faculty member, which results in an acceptable master's thesis and oral defense. (On demand)
Credits: 3 Category: Research

BINF 8911 Research Rotation I
Faculty supervised research experience in bioinformatics to supplement regular course offerings. The purpose of this course is to broaden students exposure to state-of-the-art technologies currently being utilized within the field of bioinformatics, and to guide them towards recognizing important, outstanding questions in specific scientific domains, and to give them hands-on training in conducting experiments within those domains. (Fall, Spring)
Credits: 2 Category: Required

BINF 8912 Research Rotation II
Faculty supervised research experience in bioinformatics to supplement regular course offerings. The purpose of this course is to broaden students exposure to state-of-the-art technologies currently being utilized within the field of bioinformatics, and to guide them towards recognizing important, outstanding questions in specific scientific domains, and to give them hands-on training in conducting experiments within those domains. (Fall, Spring)
Credits: 2 Category: Required

BINF 8991 Doctoral Dissertation Research
Individual investigation culminating in the preparation and presentation of a doctoral dissertation. (Fall, Spring, Summer)
Credits: 1-9 Category: Required

 

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