CSAT5024. RNA Biology and Genomics II. 1 Credit Hour.
The challenges of controlling RNA viruses, the promise of RNA vaccines and the recent findings on the roles of ncRNAs and RNA binding proteins in human disease highlight the importance of studying RNA biology. This course, coupled with MMED6001, covers all aspects of RNA expression and metabolism, such as RNA processing, decay, transport, alternative splicing and translation and, the function of RNA binding proteins and non-coding RNAs. We will also discuss recent discoveries, such as RNA vaccines, RNA granules, RNA modification, the impact of RNA mediated processes in metabolic syndrome, neurodegenerative diseases and cancer and, RNA therapeutics. Another important goal of these courses is to teach students to employ omics methods such as RNA-seq, RIP-Seq, BRIC, CLIP, Ribo-seq, and CRISPR to study these processes and their regulators. This includes hands-on training on biological databases and classes covering examples of the use of genomics. We expect students to acquire skills that will help them visualize how RNA genomics can be used in their own research projects. Open for Cross Enrollment on Space Available Basis.
CSAT6005. Rigor & Reproducibility. 1 Credit Hour.
This course will focus on two of the cornerstones of science advancement, which are rigor in designing and performing scientific research and the ability to reproduce biomedical research findings. The course will also emphasize the application of rigor that ensures robust and unbiased experimental design, methodology, analysis, interpretation, and reporting of results. The notion that when a result can be reproduced by multiple scientists, it validates the original results and readiness to progress to the next phase of research will be covered in this course. This is especially important for preclinical studies that provide the basis for rigorous clinical trials in humans. In recent years, there has been a growing awareness of the need for rigorously designed published preclinical studies, to ensure that such studies can be reproduced. The aim of this course is to help attendees acquire the skills necessary to meet the need to enhance rigor and reproducibility in preclinical scientific research. Successful completion of CSAT5095, or an equivalent approved by the Rigor & Reproducibility course director, is a prerequisite for this course.
CSAT6095. Analysis and Visualization of Genomic Data. 2 Credit Hours.
This course covers the basics of genomic data analysis and visualization. The focus is on general computational methods, their basis in biomedicine, and how to evaluate and visualize analysis results. Students are expected to be able to qualitatively describe the algorithms presented.Prerequisites: CSAT5095 or Equivalent.
INTD6062. Next-Generation Sequencing Data Analysis. 2 Credit Hours.
Next-generation sequencing (NGS) is becoming increasingly commonplace in biomedical research. For many labs, the main bottleneck to implementing NGS applications is data analysis. This course is designed to introduce students to bioinformatics analysis of NGS data. The course consists of two modules: the first module covers working in the Unix/Linux environment, mapping NGS data to a genome of interest, and performing downstream analysis of RNA-seq, ChIP-seq, and ATAC-seq data. The second module will be an introduction to the programming language Perl, which will enable students to perform custom bioinformatics analysis. This course will be taught in the form of interactive hands-on computer classes. No prior knowledge of programming or coding is required.
TSCI5070. Responsible Conduct of Research. 2 Credit Hours.
This foundational course introduces students to core ethical content necessary for responsible research conduct. Through interactive seminars, students will learn about (1) scientists as responsible members of society (contemporary ethical issues in biomedical research and environmental/social impacts of research), (2) policies for research with human subjects and vertebrate animals, (3) collaborative research, (4) conflicts of interest (personal, professional, financial), (5) data acquisition and laboratory tools (management, sharing, ownership), (6) responsible authorship and publication, (7) mentor/trainee responsibilities and relationships, (8) peer review (9) research misconduct (forms of misconduct and management policies) (10) informed consent, privacy regulations, good clinical practice, and special populations in clinical investigations.
TSCI5073. Integrated Molecular Biology With Patient-Oriented Clinical Research. 1 Credit Hour.
This interdisciplinary course is designed to train participants on integrating molecular biology methods into patient-oriented clinical research. Students will have the opportunity to learn to: (1) appropriately use molecular terms in clinical investigation; (2) describe the events involved in protein synthesis; (3) describe the principles involved in molecular techniques (e.g., polymerase chain reactions, southern blots); (4) identify the appropriate specimens, collection, and handling requirements for each molecular technique; (5) identify and correct common sources of error in performing molecular techniques; (6) cite examples of clinical applications of molecular techniques in clinical medicine; and (7) apply molecular techniques in the laboratory to specific clinical problems.
TSCI5074. Data Management, Quality Control And Regulatory Issues. 2 Credit Hours.
This interdisciplinary course is designed to train participants in the necessary data management and quality control procedures required for the conduct of patient-oriented clinical research. It consists of three segments: (1.) introduction to data management principles and standard practices; (2) development of the student's own mentored research; and (3) introduction to bioinformatics.
TSCI5075. Scientific Communication. 2 Credit Hours.
This interdisciplinary course is designed to train participants to write effectively in all aspects of conducting patient-oriented clinical research. Students will have the opportunity to learn to and, by the end of the course, be required to: (1) recognize and avoid errors in grammar, punctuation, and usage that are common in scientific writing; (2) construct units of writing whose structure, style, and logical continuity allows instant and clear comprehension; (3) construct concise, informative titles; (4) develop clear, comprehensive, abstracts for papers and grant proposals; (5) construct complete, well-rationalized sets of specific aims for grant proposals; and (6) effectively apply the 4-Point Rule (What is the question? How did we approach it? What happened? What does it mean?) to all forms of scientific writing.
TSCI5201. Statistical Principles of Machine Learning for Biomedical Data. 3 Credit Hours.
This class offers a hands-on approach to machine learning and data science. The class discusses the application of supervised and unsupervised techniques for machine learning including random forests, support vector machines, boosting, deep learning, K-means clustering and mixture models. The course focuses on real data application with open source implementations in Python and R. Prerequisites: Introductory-level course in probability and statistics; comfort with a programming language (R and/or Python) will be essential for completing the homework assignments; basic linear algebra and calculus are plus.
TSCI5230. Analytical Programming for Biomedical Data Science. 3 Credit Hours.
This class offers a hands-on approach to data science programming for biomedical research. We will introduce R, Python, SQL, and the software tools that interoperate with them. We will also cover cross-cutting best practices for organizing one's work to facilitate collaboration, reproducibility, and portability. Students who already have data they want to analyze are encouraged to use it in their assignments.
TSCI6060. Patient-Oriented Clinical Research Methods-2. 2 Credit Hours.
This interdisciplinary course is the second in a two-semester sequence designed to train participants in the conduct of patient-oriented clinical research. Students will have the opportunity to learn to and, by the end of the course be required to: (1) define criteria for inferring causation from observational studies; (2) design strategies for subject retention in a prospective study; (3) design strategies for monitoring progress in a randomized control trial; (4) delineate strategies for minimizing bias in cohort studies and randomized control trials; (5) compare and contrast the uses, strengths, and weaknesses of different clinical trial designs; (6) read and interpret research reports of cohort studies and randomized control trials; and (7) describe the steps in conducting a meta-analysis.Prerequisites: TSCI5071.
TSCI6061. Patient-Oriented Clinical Research Biostatistics-2. 2 Credit Hours.
This interdisciplinary course is the second in a two-semester sequence designed to train participants in the biostatistical analysis and patient-oriented clinical research. Students will have the opportunity to learn to and, by the end of the course, be required to: (1) perform a two-way analysis of variance and explain the results; (2) perform survival analysis; (3) compare and contrast the purpose and characteristics of different forms of interventional trials; and (4) plan the sample size, analysis, and stopping rules of a randomized clinical trial.Prerequisites: TSCI5072.
TSCI6100. Practicum In IACUC Procedures. 1 Credit Hour.
This elective course presents an in-depth introduction to the institutional program that provides oversight and regular review of projects that involve the care and use of animals. This includes consideration of the operational procedures of the Institutional Animal Care and Use Committee (IACUC) of the UT Health Science Center at San Antonio. Course objectives are achieved through a combination of readings, monthly attendance at selected IACUC meetings, and discussions with faculty.
TSCI6102. Practicum In IRB Procedures. 1 Credit Hour.
This elective course presents an in-depth introduction to the institutional program that provides oversight and regular review of research projects that involve human subjects. This includes consideration of the operational procedures of the multiple Institution Review Boards (IRB) of the UT Health Science Center at San Antonio. Course objectives are achieved through a combination of readings, monthly attendance at selected IRB meetings, and discussions with faculty.
TSCI6201. Data Science Leadership in Healthcare. 1 Credit Hour.
This class offers a hands-on approach to data science operations in biomedical science. The class discusses the management of data science teams, collaboration within healthcare organizations, and the social and ethical responsibility of data scientists. The course focuses on real world applications.
TSCI6202. Data Visualization and Building Applications. 2 Credit Hours.
This class offers a hands-on approach to data visualization for biomedical data science. The class uses R, Python and Javascript and the software tools that interoperate with them. Some cross-cutting best practices. The course focuses on real world applications. Prerequisites: Introductory-level courses in probability and statistics; comfort with a programming language will be helpful to completing the homework assignments.
TSCI6203. Practicum in Biomedical Data Science. 1 Credit Hour.
This elective course provides an opportunity for participation in unique biomedical data science and translational research activities that are highly individualized for each student on the basis of prior experience and research interests.
