Revise the Curricula for All Graduate Bioinformatics Programs and BINF courses

Date: May 25, 2012
To: College of Computing & Informatics
From: Office of Academic Affairs
Approved On: May 2, 2012
Implementation Date: 2012

Note: Deletions are strikethroughs. Insertions are underlined.

Catalog Copy

Professional Science Master’s in Bioinformatics

Additional Admission Requirements

In addition to the general requirements for admission to the Graduate School, the following are required for study toward the Professional Science Masters (PSM) in Bioinformatics:

Under most circumstances, students admitted to the program will have:

  1. A baccalaureate degree from an accredited college or university in Biology, Biochemistry, Chemistry, Physics, Mathematics, Statistics, Computer Science, or another related field that provides a sound background in life sciences, computing, or both.
  2. A minimum undergraduate GPA of 3.0 (4.0 scale) and 3.0 in the major.
  3. A minimum combined score of 1000 on the verbal and quantitative portions of the GRE, and acceptable scores on the analytical and discipline-specific sections of the GRE.
  4. A combined TOEFL score of 220 (computer-based), 557 (paper-based), or 83 (Internet-based) is required if the previous degree was from a country where English is not the common language.
  5. Positive letters of recommendation.

Degree Requirements

The Professional Science Masters (PSM) in Bioinformatics degree requires a minimum of 3740 graduate credit hours, and a minimum of 3336 credit hours of formal coursework. A minimum of 24 credit hours presented toward a PSM in Bioinformatics must be from courses numbered 6000 or higher. A maximum of 6 hours of graduate credit may be transferred from other institutions.

Total Hours Required

The program requires 3740 post-baccalaureate credit hours. Because of the interdisciplinary nature of this program, which is designed to provide students with a common graduate experience during their professional preparation for the PSM in Bioinformatics degree, all students will be required to take a general curriculum that includes a two-year sequence of courses as described below:

Core Requirements

  1. Fundamentals CoursesGateway Course

The Fundamentals course sequencesGateway courses are intensive graduate-level courses designed to provide accelerated training in a second discipline that complements the student’s undergraduate training. Students entering the program are expected to have achieved proficiency in either Biological Sciences or Computing, and to require at most two of the Fundamentals courses. take the Gateway course that is appropriate for their background. For students entering from computing backgrounds, BINF 6100 (Biological Basis of Bioinformatics), should be chosen, while students entering from biological science backgrounds should choose BINF 6111 (Bioinformatics Programming I).

Fundamental Biology track: This course sequence is designed for students entering with a degree in Computer Science or another quantitative science discipline. The Fundamental Biology course sequence provides accelerated training in Genetics, Cell and Molecular Biology, and Biochemistry for students entering Bioinformatics from computer science or a quantitative science. BINF 6100, 6101.

Fundamental Computing track: The Fundamental Computing track is designed for students entering with a degree in a life science discipline. The Fundamental Computing course sequence provides accelerated training in programming and data structures for students entering Bioinformatics from life sciences. BINF 6111, 6112.

  1. Core Bioinformatics Courses

FundamentalsGateway courses prepare students for the required Core courses. All students must take BINF 6101 (Energy and Interaction in Biological Modeling, BINF 6112 (Bioinformatics Programming II), BINF 6200 (Statistics for Bioinformatics). In addition, students must take 6 additional credit hours of Core Genomics courses from among), BINF 6201 (Molecular Sequence Analysis), BINF 6203 (Genomics), BINF 6205 (Computational Molecular Evolution)and BINF 6211 (Design and Implementation of Bioinformatics Databases). A student who has previously taken a course with a syllabus that closely follows one of the course courses may test out of the core requirement by passing a written exam, and BINF 6350 (Biotechnology and Genomics Laboratory) and 6 credit hours from the Core Computational courses from among BINF 6202 (Computational Structural Biology), BINF 6204 (Mathematical Systems Biology), BINF 6210 (Numerical Methods and Machine Learning for Bioinformatics), and BINF 6310 (Advanced Statistics for Genomics).may then substitute an advanced elective for the required core course.

  1. Professional Preparation Requirement

Students are required to take at least 36 credit hours of electives designed to prepare them to function effectively and ethically in a professional environment. Some All Bioinformatics PSM students are required to enroll in BINF 6152 (Program and Professional Orientation) (1cr), BINF 6151 (Professional Communications) (1cr) and BINF 6153 (Career Development) (1cr). The remaining PLUS credits may be chosen from a list of recommended electives in this category, which include BINF 5171 (Business of Biotechnology), BINF 5191 (Biotechnology and the Law), BINF 6151 (Professional Communications), PHIL 6050 (Research Ethics), and ITIS 6362 (Information Technology Ethics, Policy, and Security). Additional elective choices that may fulfill this requirement can be identified by the student and the student’sPSM Advisory Committee.

  1. The remaining credit hours of formal coursework can be completed in additional Core Bioinformatics courses and/or other recommended program electives. elective coursework. The student’sPSM Advisory Committee will review the student’s plan of study each semester.

Bioinformatics Electives

Any courses with BINF numbers, with the exception of Fundamentals courses, which require approval, are open to PSM students seeking to complete their coursework requirements.

Recommended Electives Offered By Other Departments

A wide range of courses in Biology, Chemistry, Computer Science, Software and Information Systems, and other departments may be appropriate electives for PSM in Bioinformatics students. As course offerings change frequently, the Bioinformatics Program maintains a list of current recommended electives, which can be found online at bioinformatics.uncc.edu.

Elective Clusters

Students are encouraged to choose their electives with a topical focus that reflects their scientific and career interests. Courses from one of the following recommended clusters of advanced electives can be selected, or the student can design his or her own elective focus with the approval of the PSM Advisory Committee.

Genomic Biology Cluster

BINF 6205 Computational Molecular Evolution

BINF 6305 Biotechnology and Genomics Laboratory

BINF 6310 Advanced Statistics for Genomics

BINF 6318 Computational Proteomics and Metabolomics

Modeling and Simulation Cluster

BINF 6202 Computational Structural Biology

BINF 6204 Mathematical Systems Biology

BINF 6210 Numerical Methods and Machine Learning in Bioinformatics

BINF 6311 Biophysical Modeling

Computing and Technology Cluster

BINF 6210 Numerical Methods and Machine Learning in Bioinformatics

BINF 6310 Advanced Statistics for Genomics

BINF 6380 Advanced Bioinformatics Programming

BINF 6382 Accelerated Bioinformatics Programming

  1. Other Requirements

Bioinformatics Seminar

In addition to 33 hours formal coursework, students are required to enroll in the Bioinformatics Program seminar (BINF 6600) for at least one semester (1 credit hour) and to enroll in either an approved internal or external internship (BINF 6400) or a faculty-supervised original research project leading to a thesis (BINF 6900).

Grade Requirements

An accumulation of three C grades will result in suspension of the student’s enrollment in the graduate program. If a student makes a grade of U in any course, enrollment in the program will be suspended.

Amount of Transfer Credit Accepted

A maximum of 6 credit hours of coursework from other institutions will count toward the PSM in Bioinformatics degree requirements. Only courses with grades of A or B from accredited institutions are eligible for transfer credit.

Graduate Certificate in Bioinformatics Applications

The purpose of the Graduate Certificate in Bioinformatics Applications is to train individuals in the application of established bioinformatics methods for analysis of biological sequence, structure, and genomic data. The certificate requires twelve (12) credit hours of coursework. The certificate may be pursued concurrently with a related graduate degree program at UNC Charlotte or as a standalone program.

Admission Requirements

For admission into the certificate program, applicants must meet the following requirements:

  1. A bachelor’s degree in a life science discipline, that includes advanced coursework in molecular biology and genetics.
  2. Practical experience and confidence with computers, for instance use of common web browsers, word processing, plotting, and spreadsheet applications.

Program Requirements

Students will take four courses that introduce core methods for analysis of molecular biological data:

BINF – 6200 Statistics for Bioinformatics (3)

And three courses chosen from the following list of electives:

BINF 6201 Molecular Sequence Analysis (3)

BINF 6202 Computational Structural Biology (3)

BINF 6203 Genomics (3)

And one of the following:

BINF 6211 Design and Implementation of Bioinformatics Databases (3)

BINF 6350 Genomic Biotechnology (3)and Genomics Laboratory (3)

If a student wishes to enter the program having completed coursework that is equivalent to one or more of the core requirements, the requirements may be waived at the discretion of the certificate coordinator. In this case, the required 12 credit hours may be selected from other advanced graduate courses offered by the Department of Bioinformatics and Genomics.

Transfer credit may not be applied toward this certificate.

It is suggested that students in the Graduate Certificate Program arrange formal co-mentorship by a Department of Bioinformatics and Genomics faculty member, if the student is concurrently enrolled in another thesis-based degree program on campus and intends to extend or enable their thesis research through the application of bioinformatic methods.

It is suggested that students in the Graduate Certificate Program arrange formal co-mentorship by a Department of Bioinformatics and Genomics faculty member, if the student is concurrently enrolled in another thesis-based degree program on campus and intends to extend or enable their thesis research through the application of bioinformatic methods.

Graduate Certificate in Bioinformatics Technology

The purpose of the Graduate Certificate in Bioinformatics Technology is to train individuals in method development for analysis of large-scale biological data and modeling of complex biological systems, with a focus on acquiring complementary skill sets in life sciences and in programming, statistical analysis, and database development. The certificate requires fifteen (15) credit hours of coursework. The certificate may be pursued concurrently with a related graduate degree program at UNC Charlotte.

Admission Requirements

For admission into the certificate program, applicants must meet the following requirements:

  1. A bachelor’s degree in related field, including, but not limited to, a life science, physical science, mathematics, or computing discipline.
  2. Practical experience and confidence with computers, for instance use of common web browsers, word processing, plotting, and spreadsheet applications.

Program Requirements

Students will follow one of two pathways through the program, depending on their bachelor’s degree field and previous experience. The following courses make up the required core:

If the bachelor’s degree is in life sciences:

BINF 6200 Statistics for Bioinformatics (3)

BINF 6110 Bioinformatics Programming I (3)

BINF 6111 Bioinformatics Programming II (3)

BINF 6203 Genomics (3)

If the bachelor’s degree is in computing or mathematics:

BINF 6200 Statistics for Bioinformatics (3)

BINF 6100 Biological Basis of Bioinformatics (3)

BINF 6101 Energy and Information in Biological Modeling6111 Bioinformatics Programming II (3)

One advanced bioinformatics technology course from BINF 6203 Genomics (3)

And one of the following list of electives is requiredcourses:

BINF 6201 Molecular Sequence Analysis (3)

BINF 6211 Design and Implementation of Bioinformatics Databases (3)

BINF 6310 Advanced Statistics for Bioinformatics (3)

BINF 6380 Bioinformatics Programming III (3)

One bioinformatics applications course from the following list of electives is required:

BINF 6201 Molecular Sequence Analysis (3)

BINF 6202 Computational Structural Biology (3)

BINF 6203 Genomics (3)

If a student wishes to enter the program having completed coursework that is equivalent to the core course requirements, the core requirements may be waived at the discretion of the certificate coordinator. In this case, the required 15 coursework hours may be selected from the electives listed above, or from other advanced graduate courses offered by the Department of Bioinformatics and Genomics.

Transfer credit may not be applied toward this certificate.

It is suggested that students in the Graduate Certificate Program arrange formal co-mentorship by a Department of Bioinformatics and Genomics faculty member, if the student is concurrently enrolled in another thesis-based degree program on campus and intends to extend or enable their thesis research through the application of bioinformatic methods.

It is suggested that students in the Graduate Certificate Program arrange formal co-mentorship by a Department of Bioinformatics and Genomics faculty member, if the student is concurrently enrolled in another thesis-based degree program on campus and intends to extend or enable their thesis research through the application of bioinformatic methods.

Courses in Bioinformatics (BINF)

BINF 5171. Business of Biotechnology. (3) Prerequisite: Admission to a graduate program. 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. (On demand)

BINF 5191. Biotechnology and the Law. (3) Prerequisite: Admission to a graduate program. At the intersection of biotechnology and the law, an intricate body of law is forming based on constitutional, case, regulatory and administrative law. This body of legal knowledge is interwoven with ethics, policy and public opinion. Because biotechnology impacts everything in our lives, the course will provide an overview of salient legal biotechnology topics, including but not limited to: intellectual property, innovation and approvals in agriculture, drug and diagnostic discovery, the use of human and animal subjects, criminal law and the courtroom, agriculture (from farm to fork), patient care, bioethics, and privacy. The body of law is quite complex and it is inundated with a deluge of acronyms. The course will provide a foundation to law and a resource to help students decipher laws and regulation when they are brought up in the workplace. (On demand)

BINF 6010. Topics in Bioinformatics. (3) Prerequisite: 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. (On demand)

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

BINF 6101. Energy and Interaction in Biological Modeling. (3) Prerequisite: 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)

BINF 6111. Bioinformatics Programming I. (3) Prerequisite: Admission to graduate standing in Bioinformatics. or permission of instructor. Introduces fundamentals of programming for bioinformatics using a high-level object-oriented language such as python.Java or Python. The first weeks cover core data types, syntax, and functionalcourse introduces object-oriented programming, focusing on constructionanalysis of programs from small, testable parts. algorithms and fundamental sequence alignment methods. Students will learn productive use of the Unix environment, focusing on Unix utilities that are particularly useful in bioinformatics. The course covers object-oriented programming, introduce analysis of algorithms and sequence alignment methods, and introduce computational environments that are particularly useful in bioinformatics analyses such as R, BioPython, and Web services in bioinformatics. By the end of the semester, students will have gained the ability to analyze data within the python interpreter (for example) and write well-documented, well-organized programs. (Fall)

BINF 6112. Bioinformatics Programming II. (3) Prerequisite: BINF 6111/ITSC 6111. or permission of instructor. Continuation of BINF 6111. In this second semester course, students practice and refine skills learned in the first semester. New topics include: (a) programming as part of a team, using sequence analysis algorithms in realistic settings; (b) writing maintainable and re-usable code; (c) Web programming; and (dc) 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)

BINF 6151. Professional CommunicationsCommunication. (1) Cross-listed as GRAD 6151. Principles and useful techniques for effective oral presentations, poster presentations, scientific writing, use of references, and avoiding plagiarism. Students in the course critique and help revise each other’s presentations and learn how to avoid common pitfalls. In addition, students learn how to properly organize and run a meeting. Students prepare a CV, job application letter, and job talk. (Fall)

BINF 6171. Business of Biotechnology. (3) 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 include platform technology, biotechnology’s history, biotechnology products and development processes, current technologies used by biotech companies, biotechnology business fundamentals, research and development within biotech companies, exit strategies, and careers in the biotech field. (Summer)

BINF 6200.

BINF 6152. Program and Professional Orientation. (1) Students in the course learn to identify key Bioinformatics skill sets and where they are applied in research and industry settings, join appropriate professional networks, use the major professional and research journals in the field, identify key organizations and companies driving intellectual and technology development in Bioinformatics, and achieve beginner-level proficiency with key molecular data repositories. (Fall)

BINF 6153. Career Development in Bioinformatics. (1) Students in the course will prepare intensively for the job search, from developing a resume, to identifying appropriate opportunities, to preparing for the interview. Students are expected to complete a final interview practicum with faculty and members of the PSM Advisory Board. (Fall)

BINF 6200. Statistics for Bioinformatics. (3) Introduces students to statistical methods commonly used in bioinformatics. Basic 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)

BINF 6201. Molecular Sequence Analysis. (3) Prerequisite: BINF 6100 or equivalent. Introduction to bioinformatics methods that apply to molecular sequence and to biological databases online. Sequence databases, molecular sequence data formats, sequence data preparation and database submission. Local and global sequence alignment, multiple alignment, alignment scoring and alignment algorithms for protein and nucleic acids, genefinding and feature finding in sequence, models of molecular evolution, phylogenetic analysis, comparative modeling. (Fall)

BINF 6202. Computational Structural Biology. (3) Prerequisites: BINF 6101 and BINF 6201 or their equivalents. This course covers: (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)

BINF 6203. Genomics. (3) 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: (a) genome sequencing and assembly, annotation, and comparison; (b) genome evolution and individual variation; (c) function prediction; (d) gene ontologies; (e) transcription assay design, data acquisition, and data analysis; (f) proteomics methods; (g) methods for identification of molecular interactions; and (h) metabolic databases, pathways and models. databases and their role in genome analysis. (Spring)

BINF 6204. Mathematical Systems Biology. (3) Prerequisites: BINF 6200 and BINF 6210 or equivalents. Introduces basic concepts, principles and common methods used in systems biology. Emphasizes molecular networks, models and applications, and covers the following topics: (a) the structure of molecular networks; (b) network motifs, their system properties and the roles they play in biological processes; complexity and robustness of molecular networks; (c) hierarchy and modularity of molecular interaction networks; kinetic proofreading; (d) optimal gene circuit design; and (e) the rules for gene regulation. (Spring)

BINF 6205. Computational Molecular Evolution. (3) Prerequisites: BINF 6201 and BINF 6200 or permission of the instructor. 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)

BINF 6210. Numerical Methods and Machine Learning in Bioinformatics. (3) Prerequisites: Ability to program in a high-level language (Perl, Java, C#, Python, Ruby, C/C++) and Calculus. Focuses on commonly used numerical methods and machine learning techniques. Topics will include: solutions to linear systems, curve fitting, numerical differentiation and integration, PCA, SVD, ICA, SVM, PLS. Time permitting, Hidden Markov Chains and Monte Carlo simulations will be covered as well. Students learn both the underlying theory and how to apply the theory to solve problems. (Fall)

BINF 6211. Design and Implementation of Bioinformatics Databases. (3) Students learn the necessary skills to access and utilize public biomedical data repositories, and are expected to design, instantiate, populate, query and maintain a personal database to support research in an assigned domain of bioinformatics. Topics include common data models and representation styles, use of open-source relational DBMS, and basic and advanced SQL. Focuses on how data integration is achieved, including the use of standardized schemas, exchange formats and ontologies. Examines large public biomedical data repositories such as GenBank and PDB, learn how to locate and assess the quality of data in Web-accessible databases, and look at representation, standards, and access methods for such databases. (Spring)

BINF 6310. Advanced Statistics for Genomics. (3) Prerequisite: BINF 6200 or equivalent. The first half of this course emphasizes canonical linear statistics (t-test, ANOVA, PCA) and their non-parametric equivalents. The second half of the course emphasizes Bayesian statistics and the application of Hidden Markov Models to problems in bioinformatics. Students should have fluency in a high-level programming language (PERL, Java, C# 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)

BINF 6311. Biophysical Modeling. (3) This course covers: (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)

BINF 6312. Computational Comparative Genomics. (3) Prerequisite: BINF 6201 or equivalent. Introduces computational methods for comparative genomics analysis. Covers the following topics: (a) the architecture of prokaryotic and eukaryotic genomes; (b) the evolutionary concept in genomics; (c) databases and resources for comparative genomics; (d) principles and methods for sequence analysis; evolution of genomes; (e) comparative gene function annotation; (f) evolution of the central metabolic pathways and regulatory networks; (g) genomes and the protein universe; (h) cis-regulatory binding site prediction; (i) operon and regulon predictions in prokaryotes; and (j) regulatory network mapping and prediction. (On demand)

BINF 6313. Structure, Function, and Modeling of Nucleic Acids. (3) Prerequisites: BINF 6100 and BINF 6101 or their equivalents. Covers the following topics: (a) atomic structure, macromolecular structure-forming tendencies and dynamics of nucleic acids; (b) identification of genes which code for functional nucleic acid molecules, cellular roles and metabolism of nucleic acids; (c) 2D and 3D abstractions of nucleic acid macromolecules and methods for structural modeling and prediction; (d) modeling of hybridization kinetics and equilibria; and (e) 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)

BINF 6318. Computational Proteomics and Metabolomics. (3) Prerequisites: BINF 6200 or equivalent. The aim of this 3-credit course is to introduce commonly used computational algorithms and software tools for analyzing mass spectrometry-based proteomics and metabolomics data. Chromatography and mass spectrometry will be covered at the beginning of the course to provide background information for the students to understand the nature of mass spectrometry data. (On demand)

BINF 6350. Biotechnology and Genomics Laboratory. (3) Teaches basic wet-lab techniques commonly used in biotechnology to generate genomics data. Lectures cover methods for sample isolation, cell disruption, nucleic acid and protein purification, nucleic acid amplification, protein isolation and characterization, molecular labeling methods and commonly used platforms for characterizing genome-wide molecular profiles. In particular, students discuss and learn to perform: tissue culture and LCM isolation of cells, DNA sequencing methods, DNA fingerprinting methods, RT-qPCR and microarrays of cDNA, 1D and 2D gels for protein separation, protein activity assays, and proteomics platforms. Lectures describe emerging methodologies and platforms, and discuss the ways in which the wet-lab techniques inform the design and use of bioinformatics tools, and how the tools carry out the processing and filtering that leads to reliable data. This course also discusses the commercial products beginning to emerge from genomics platforms. (Spring)

BINF 6380. Advanced Bioinformatics Programming III. (3) Prerequisite: BINF 6112 or equivalent. Emphasizes 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. (Fall (On demand)

BINF 6382. Accelerated Bioinformatics Programming. (3) Prerequisite: BINF 6112 or equivalent. Prerequisite: BINF 6112 or equivalent or permission of instructor. 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)

BINF 6400. Internship Project. (1-3) Prerequisite: 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. (Fall, Spring)

BINF 6600. Seminar. (1) Prerequisite: Admission to graduate standing in Bioinformatics. Weekly seminars are given by bioinformatics researchers from within the University and across the world. (Fall, Spring)

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

BINF 6880. Independent Study. (1-3) Faculty supervised research experience to supplement regular course offerings.

BINF 6900. Master’s Thesis. (1-3) Prerequisites: 12 graduate credits 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. (On demand)

BINF 7999. Master’s Degree Graduate Residency Credit. (1) (Fall, Spring, Summer)

BINF 8010. Topics in Bioinformatics. (3) Prerequisite: 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. (On demand)

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

BINF 8101. Energy and Interaction in Biological Modeling. (3) Cross-listed as ITSC 8101. (3) Prerequisites: Admission to graduate standing in Bioinformatics. Covering: (a)This course covers: (i.) the major organic and inorganic chemical features of biological macromolecules; (b)ii.) the physical forces that shape biological molecules, assemblies and cells; (c)iii.) the chemical driving forces that govern living systems; (d)iv.) the molecular roles of biological macromolecules and common metabolites; (e)v.) 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)

BINF 8111. Bioinformatics Programming I. (3) Prerequisite: Admission to graduate standing in Bioinformatics. Introduction of or permission of instructor. Introduces fundamentals of programming for bioinformatics using a high-level object-oriented language such as pythonJava or Python. The first weeks cover core data types, syntax, and functionalcourse introduces object-oriented programming, focusing on constructionanalysis of programs from small, testable parts. algorithms and fundamental sequence alignment methods. Students will learn productive use of the Unix environment, focusing on Unix utilities that are particularly useful in bioinformatics. Object-oriented programming, analysis of algorithms and sequence alignment methods, and computational environments that are particularly useful in bioinformatics analyses such as R, BioPython, and Web services in bioinformatics. By the end of the course, students will have gained the ability to analyze data within the python interpreter (for example) and write well-documented, well-organized programs. (Fall)

BINF 8112. Bioinformatics Programming II. (3) Prerequisite: BINF 8111. or permission of instructor. Continuation/ of BINF 6111. In this second semester of BINF 8111. Students will , students practice and refine skills learned in the first semester. New topics introduced will include: (a) programming as part of a team, using sequence analysis algorithms in realistic settings; (b) writing maintainable and re-usable code; Web programming; and (c) 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)

BINF 8151. Professional Communications. (1) Cross-listed as GRAD 8151. This course covers: Principles and useful techniques for effective oral presentations, poster presentations, scientific writing, use of references and avoiding plagiarism. Students in the class will critique and help revise each other’s presentations and learn how to avoid common pitfalls. In addition, students will learn how to properly organize and run a meeting. Students will prepare a CV, job application letter, and job talk. (Fall)).

BINF 8171. Business of Biotechnology. (3) 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 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. (Summer)

BINF 8200. Statistics for Bioinformatics. (3) This course aims to introduce statistical methods commonly used in bioinformatics. Basic 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)

BINF 8201. Molecular Sequence Analysis. (3) Prerequisite: BINF 8100 or equivalent. BINF 8100 or equivalent. Introduction to: (a) bioinformatics methods that apply to molecular sequence; (b). Intro to biological databases online; (c) sequence. Sequence databases, molecular sequence data formats, sequence data preparation and database submission; and (d) local. Local and global sequence alignment, multiple alignment, alignment scoring and alignment algorithms for protein and nucleic acids, genefinding and feature finding in sequence;, models of molecular evolution, phylogenetic analysis, and comparative modeling. (Fall)

BINF 8202. Computational Structural Biology. (3) Prerequisites:Prerequisite: BINF 8101 and BINF, 8201 or equivalents. This course covers: (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)

BINF 8203. Genomics. (3) Prerequisite: BINF 8100 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: (a) genome sequencing and assembly, annotation, and comparison; (b) genome evolution and individual variation; (c) function prediction; (d) gene ontologies; (e) transcription assay design, data acquisition, and data analysis; (f) proteomics methods; (g) methods for identification of molecular interactions; and (h) metabolic databases, pathways and models.databases and their role in genome analysis. (Spring)

BINF 8204. Mathematical Systems Biology. (3) Prerequisites: BINF 8200 and BINF 8210 or equivalents. IntroducesThis course introduces basic concepts, principles and common methods used in systems biology. Emphasizes The class emphasizes on molecular networks, models and applications, and covers the following topics: (a) the structure of molecular networks; network motifs, their system properties and the roles they play in biological processes; (b) complexity and robustness of molecular networks; (c) hierarchy and modularity of molecular interaction networks; kinetic proofreading; (d) optimal gene circuit design; and (e) the rules for gene regulation. (Spring)

BINF 8205. Computational Molecular Evolution. (3) Cross-listed as ITSC 8205. Prerequisites: BINF 8200 Pre-requisites: BINF 8201 (Molecular Sequence Analysis) and BINF 8201, 8200 Statistics for Bioinformatics (or permission of the instructor. Major). This course will cover 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)

BINF 8210. Numerical Methods and Machine Learning in Bioinformatics. (3) PrerequisitePrerequisites: Ability to program in a high-level language (Perl, Java, C#, Python, Ruby, C/C++), Calculus. FocusesThis course focuses on commonly used numerical methods and machine learning techniques. Topics will include: solutions to linear systems, curve fitting, numerical differentiation and integration, PCA, SVD, ICA, SVM, PLS. Time permitting, hidden markov chains and Monte Carlo simulations will be covered as well. Students will learn both the underlying theory and how to apply the theory to solve problems. (Fall)

BINF 8211. Design and Implementation of Bioinformatics Databases. (3) StudentsIn this course students will acquire skills needed to access and utilize public biomedical data repositories, and arewill be expected to design, instantiate, populate, query and maintain a personal database to support research in an assigned domain of bioinformatics. The course content includes common data models and representation styles, use of open-source relational DBMS, and basic and advanced SQL. FocusesThe course focuses on how data integration is achieved, including the use of standardized schemas, exchange formats and ontologies. Examination of We will examine large public biomedical data repositories such as GenBank and PDB, learn how to locate and assess the quality of data in Web-accessible databases, and look at representation, standards and access methods for such databases. (Spring)

BINF 8310. Advanced Statistics for Genomics. (3) Prerequisite: BINF 8200 or equivalent. The first half of this course emphasizes canonical linear statistics (t-test, ANOVA, PCA) and their non-parametric equivalents. The second half of the course emphasized Bayesian statistics and the application of Hidden Markov Models to problems in bioinformatics. Students should have fluency in a high-level programming language (PERL, Java, C# or equivalent) and will be expected in assignments to manipulate and analyze large public data sets. Utilization ofThe course will utilize the R statistical package with the bioconductor extension. (Spring)

BINF 8311. Biophysical Modeling. (3) This course covers: (a) an 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 Coulombic); (g) free energy decomposition schemes; and (h) hybrid quantum/classical (QM/MM) methods. (On demand)

BINF 8312. Computational Comparative Genomics. (3) Prerequisite: BINF 8201 or ITSC 8201 or equivalent. IntroducesThis course introduces computational methods for comparative genomics analyses. The course covers the following topics: (a) the architecture of prokaryotic and eukaryotic genomes; (b) the evolutionary concept in genomics; (c) databases and resources for comparative genomics; (d) principles and methods for sequence analysis; evolution of genomes; (e) comparative gene function annotation; (f) evolution of the central metabolic pathways and regulatory networks; genomes and the protein universe; (g) cis-regulatory binding site prediction; (h) operon and regulon predictions in prokaryotes; and (i) regulatory network mapping and prediction. (On demand)

BINF 8313. Structure, Function, and Modeling of Nucleic Acids. (3) Prerequisite: BINF 8100-8101 or equivalent. The course covers the following topics: (a) atomic structure, macromolecular structure-forming tendencies and dynamics of nucleic acids; (b) 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; (c) modeling of hybridization kinetics and equilibria; and (d) 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)

BINF 8318. Computational Proteomics and Metabolomics. (3) Prerequisites: BINF 8200 or equivalent. The aim of this 3-credit course is to introduce commonly used computational algorithms and software tools for analyzing mass spectrometry-based proteomics and metabolomics data. Chromatography and mass spectrometry will be covered at the beginning of the course to provide background information for the students to understand the nature of mass spectrometry data. (On demand)

BINF 8350. Biotechnology and Genomics Laboratory. (3) Prerequisite: none. This course teaches basic wet-lab techniques commonly used in biotechnology to generate genomics data. Lectures will cover methods for sample isolation, cell disruption, nucleic acid and protein purification, nucleic acid amplification, protein isolation and characterization, molecular labeling methods and commonly used platforms for characterizing genome-wide molecular profiles. In particular, students we will discuss and learn to perform: tissue culture and LCM isolation of cells, DNA sequencing methods, DNA fingerprinting methods, RT-qPCR and microarrays of cDNA, 1D and 2D gels for protein separation, protein activity assays, and proteomics platforms. Lectures will describe emerging methodologies and platforms, and will discuss the ways in which the wet-lab techniques inform the design and use of bioinformatics tools, and how the tools carry out the processing and filtering that leads to reliable data. The course will also discussesdiscuss the commercial products beginning to emerge from genomics platforms. (Spring)

BINF 8380. Advanced Bioinformatics Programming III. (3) Prerequisite: BINF 8112 or equivalent. Emphasizes 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. (Fall (On demand)

BINF 8382. Accelerated Bioinformatics Programming. (3) Prerequisite: BINF 8112 or equivalent or permission of instructor. 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)

BINF 8600. Seminar. (1) Prerequisite: Prerequisites: Admission to graduate standing in Bioinformatics. Departmental seminar. Cross-listed as BINF 6600. Weekly seminars will be given by bioinformatics researchers from within the university and across the world. This course may be repeated for credit. (Fall, Spring)

BINF 8601. Journal Club. (1) Prerequisite:Prerequisites: Admission to graduate standing in Bioinformatics. Each week, a student in the courseclass is assigned to choose and present a paper from the primary bioinformatics literature. (Fall, Spring)

BINF 8911. Research Rotation I. (2) ), BINF 8912 Research Rotation II (2). Faculty supervised research experience in bioinformatics to supplement regular course offerings. (Fall, Spring)

BINF 8991. Doctoral Dissertation Research. (1-9) Individual investigation culminating in the preparation and presentation of a doctoral dissertation. A student may register for multiple sections of this course in the same semester or different semesters. (Fall, Spring, Summer)

BINF 9999. Doctoral Degree Graduate Residency Credit. (1) (Fall, Spring, Summer)