University of North Carolina at Charlotte · Courses
BINF
61 courses with the subject BINF, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
BINF 1101Introduction to Bioinformatics and Genomics4
Introduction to the genomics perspective in the life sciences, combining a general introduction to genomic technologies and the bioinformatics methods used to analyze genome-scale data with a presentation of real world scientific problems where these technologies are having an impact. The lab component provides hands-on experience with biological sequence and structure databases, using small-scale projects to introduce students to the world of bioinformatics research. Fulfills the General Education Natural Science and Lab requirement.
Unrestricted Elective Courses, Double Major and/or Minor to Complete Requirements for Graduation Students must earn a minimum of 120 credit hours to graduate. This requirement can be fulfilled through a combination of elective courses, minor, or a double major. The 120-credit minimum must be satisfied regardless of the specific academic path chosen (e.g., a minor combined with electives). Degree Total = 120 Credit Hours
BINF 3900Undergraduate Research. (1 to 3) Prerequisite(s): BINF
1101 and permission of instructor. Enables students in the Bioinformatics and Genomics program to initiate research projects in their respective fields of interest and to interact with faculty in pursuing research experience. May be repeated for credit.
BINF 4010Topics in Bioinformatics and Genomics. (1 to 3)
Prerequisite(s): Permission of department. Exploration of specific topics from the areas of bioinformatics and genomics. May be repeated for credit with permission of department; students may register for multiple sections of the course with different topics in the same or different semesters.
Subject
BINF
Type
course
Repeatable
May be repeated for credit with permission of department; students may register for multiple sections of the course with different topics in the same or different semesters.
or BINF 4191 - Life Sciences and the Law (3) Unrestricted Elective Courses, Double Major and/or Minor to Complete Requirements for Graduation Students must earn a minimum of 120 credit hours to graduate. This requirement can be fulfilled through a combination of elective courses, a minor, or a double major. The 120-credit minimum must be satisfied regardless of the specific academic path chosen (e.g., a minor combined with electives).
BINF 4650Senior Project. (1 to 3) Prerequisite(s): Senior standing and
permission of department. An individual or group project in the teaching, theory, or application of bioinformatics, genomics, or computational biology under the direction of a faculty member. Projects must be approved by the department before they can be initiated. May be repeated one time.
Elective Courses (9 credit hours) The remaining 9 credit hours of required coursework can be satisfied by elective courses. The M.S. Graduate Coordinator, in conjunction with the Graduate Program Director, reviews the student’s plan of study each semester. Bioinformatics Elective Courses Any BINF graduate courses are open to M.S. students seeking to complete their coursework requirements. Recommended Elective Courses Offered By Other
BINF 6010Topics in Bioinformatics. (3) Prerequisite(s)
permission of the department. Topics in bioinformatics and genomics selected to supplement the regular course offerings. A student may register for multiple sections of the course with different topics in the same semester or in different semesters.
Prerequisite(s): Admission to graduate standing in Bioinformatics and undergraduate training in Computer Science or other non-biological discipline. Cross-listed Course(s): BINF 8100. Provides a foundation in the fundamental processes involved in molecular biology including transmission genetics, molecular genetics and cell biology focusing on topics essential for understanding the biological aspects of genomics and computational biology. Students will learn genetic mapping, inheritance patterns, DNA structure and packaging, mechanisms of replication and repair, the central dogma of molecular genetics, regulation of gene expression and basic cellular biology. This course is designed for students without a biological background.
BINF 6101Energy and Interaction in Biological Modeling3
Prerequisite(s): 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.
Prerequisite(s): BINF 6201 and BINF 6200 or permission of the instructor. Cross-listed Course(s): BINF 8205. Covers both the theoretical and practical aspects of performing phylogenetic analyses, with particular emphasis on understanding the differences and applications of the most commonly used methods. Topics to be covered will include: finding the appropriate data and getting it into the right format; using distance-based and probability-based methods to construct phylogenetic trees; understanding and calibrating the molecular clock to calculate divergence times; incorporating “big data” into phylogenetics, and estimating population parameters such as population size, growth, and migration rates.
Prerequisite(s): BINF 6200 and Calculus. Introduction of commonly used machine learning methods in the field of bioinformatics. Topics include: dimension reduction using principal component analysis, singular value decomposition, and 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 are explained. Application of these machine learning methods to solving bioinformatics problems are illustrated using examples from the literature.
BINF 6211Design and Implementation of Bioinformatics
Databases (3) Students must choose one additional BINF elective at the 8200 level. Gateway Courses (3 credit hours) The department offers two intensive graduate-level courses designed to provide accelerated training in a second discipline that complements the student's undergraduate training. Students entering the program without preparatory coursework in computing or biology may need to take the Gateway course that is appropriate for their background. These courses are not required core courses for all students, but are designed to satisfy core
Prerequisite(s): BINF 6111, BINF 6200, BINF 6201. Cross-listed Course(s): BINF 8305. Genome sequence variation in nature is shaped by evolutionary forces of mutation, migration, drift, selection, and mating structure. Computational Population Genetics covers evolutionary theory and models that can explain sequence diversity in nature, as well as computational techniques for genomic analysis. Students will gain hands-on experience with techniques and algorithms for analysis of variation within populations. We will discuss mathematical models and use for emerging bioinformatic tools for evolutionary inference. Course material is designed to provide students with analytical tools and theoretical understanding for careers in computational evolutionary genomics including modern human evolutionary genetics.
Prerequisite(s): BINF 6200 or equivalent. Cross-listed Course(s): BINF 8310. Students will gain expertise in statistical methods commonly used in bioinformatics and genomics analysis. This course builds upon the prerequisite by establishing students’ comprehensive understanding of statistical methods, including underlying theory, assumptions, limitations, and applications across bioinformatics. Students will gain the expertise to choose, implement, analyze, visualize, and report on statistical methods used in academic and professional bioinformatics analysis. Beyond statistical methods, the course builds core skills in ethical statistical practices, professional statistical writing, and critical statistics evaluation. The course includes lectures and coding assignments utilizing real- world data in the R programming language.
Prerequisite(s): A background in molecular biology and biochemistry or permission of the instructor. Introduction of 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
Prerequisite(s): BINF 6112 or equivalent or permission of instructor. 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. Course includes hands-on experience with multi-threaded programming.
BINF 6400Internship Project. (1 to 3) Prerequisite(s)
Admission to graduate standing in Bioinformatics. Project is chosen and completed under the guidance of an industry partner, and results in an acceptable technical report.
BINF 6900Master’s Thesis. (1 to 3) Prerequisite(s)12
graduate credit hours and permission of instructor. Project is chosen and completed under the guidance of a graduate faculty member, and will result in an acceptable master's thesis and oral defense. Graded on a Satisfactory Progress/Unsatisfactory Progress or Standard Letter Grade basis each term.
BINF 8010Topics in Bioinformatics. (3) Prerequisite(s)
Permission of department. Topics in bioinformatics and genomics selected to supplement the regular course offerings. A student may register for multiple sections of the course with different topics in the same semester or in different semesters.
BINF 8101Energy and Interaction in Biological Modeling3
Prerequisite(s): Admission to graduate standing in Bioinformatics. Topics include: the major organic and inorganic chemical features of biological macromolecules; the physical forces that shape biological molecules, assemblies and cells; the chemical driving forces that govern living systems; the molecular roles of biological macromolecules and common metabolites; 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.
Prerequisite(s): BINF 8101 and BINF 8201, or their equivalents. Topics include: (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. BINF 8203. Genomics. (3) Prerequisite(s): BINF 8201 and BINF 6115, or completion of an undergraduate degree, minor or concentration in Bioinformatics. Cross-listed Course(s): BINF 6203. Builds on the methods learned in Molecular Sequence Analysis to show how they are applied to genome-scale problems. Working with datasets from microbial and viral systems, students will learn how sequencing is used to generate reference genome sequences, collect genomic surveillance data, identify and classify genetic variants, explore gene expression and regulation, and discover what makes up microbial communities and how they interact. Emphasis on method benchmarking, real world best practices, and reproducibility of analyses.
Prerequisite(s): BINF 8200 and BINF 8201, or permission of the instructor. Cross-listed Course(s): BINF 6205. Theoretical and practical aspects of performing phylogenetic analyses, with particular emphasis on understanding the differences and applications of the most commonly used methods. Topics will include: finding the appropriate data and getting it into the right format; using distance-based and probability-based methods to construct phylogenetic trees; understanding and calibrating the molecular clock to calculate divergence times; ate Catalog 2026-2027
Prerequisite(s): BINF 8200 and Calculus. Introduces commonly used machine learning methods in the field of bioinformatics. Topics include: dimension reduction using principal component analysis, singular value decomposition, and 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 are explained. Application of these machine learning methods to solving bioinformatics problems are illustrated using examples from the literature.
BINF 8211Design and Implementation of Bioinformatics
Databases. (3) Prerequisite(s): Permission of instructor. Introduces the fundamentals of database modeling as used in bioinformatics. By the end of the course, students are 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. Introduction of XML, XML Schema, and BioOntologies as widely used data exchange and organization tools in bioinformatics databases.
Prerequisite(s): BINF 8200, BINF 8201, BINF 8111 or equivalent. Cross-listed Course(s): BINF 6305. Genome sequence variation in nature is shaped by evolutionary forces of mutation, migration, drift, selection, and mating structure. Computational Population Genetics covers evolutionary theory and models that can explain sequence diversity in nature, as well as computational techniques for genomic analysis. Students will gain hands-on experience with techniques and algorithms for analysis of variation within populations. We will discuss mathematical models and use for emerging bioinformatic tools for evolutionary inference. Course material is designed to provide students with analytical tools and theoretical understanding for careers in computational evolutionary genomics including modern human evolutionary genetics.
Prerequisite(s): BINF 8200 or equivalent. Cross-listed Course(s): BINF 6310. Students will gain expertise in statistical methods commonly used in bioinformatics and genomics analysis. This course builds upon the prerequisite by establishing students’ comprehensive understanding of statistical methods, including underlying theory, assumptions, limitations, and applications across
Prerequisite(s): A background in molecular biology and biochemistry or permission of instructor. Introduction 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 significantly affects 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 are explained. Topics include: selecting applications tuned to the experiment design to ensure proper data analysis and interpretation.
Prerequisite(s): BINF 8112 or equivalent, or permission of instructor. 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 duate Catalog 2026-2027
Course(s): BINF 6600. Prerequisite(s): Admission to graduate standing in Bioinformatics. Departmental seminar. Weekly seminars will be given by bioinformatics researchers from within the University and across the world. May be repeated for credit.
Prerequisite(s): Permission of department. Students conduct research in bioinformatics under the direction of one or more Bioinformatics faculty. A major goal of this course is to prepare the student for the Qualifying Examination. May be repeated for credit.
