- BIOS 610 Biostatistics for Laboratory Scientists. This course introduces the basic concepts and methods of statistics, focusing on applications in the experimental biological sciences. Highly recommended for second year graduate students in the biomedical scientists who do not have a strong statistical background.
Instructor: Eric Bair | Taught Fall, TTh, 10 weeks
BCB 720 Introduction to Statistical Modeling. This module introduces foundational statistical concepts and models that motivate a wide range of analytic methods in bioinformatics, statistical genetics, statistical genomics, and related fields. Students are expected to know single-variable calculus, be familiar with matrix algebra, and have some programming experience.
Instructors: Will Valdar and Ethan Lange | Taught: Fall, TTh, Full Semester | Course Website
BCB 725 Introduction to Statistical Genetics. This is an introductory course for graduate students in Computational Biology, Bioinformatics, Biostatistics, Genetics, Statistics, Epidemiology, and other related quantitative disciplines. The course will cover statistical methods for the analysis of family and population based genetic data. Topics covered will include classical linkage analysis, population-based and family‐based association analysis, haplotype analysis, genome‐wide association studies, basic principles in population genetics, imputation-based analysis, pathway‐based analysis, admixture mapping, analysis of copy number variations, and analysis of massively parallel sequencing data. Students will be exposed to the latest statistical methodology and computational tools on gene mapping for complex human diseases. We will also have guest lecturers covering the fundamentals of mice genetics; and other special topics.
Instructors: Yun Li and Ethan Lange | Taught: Every other spring (odd years), WF, Full Semester | Syllabus
BIOC/PHCO 741 Contemporary Topics in Cell Signaling: The class will also consist of workshops that involve mastering the graphics program PyMOL in order to visualize, analyze, and present protein structures related to class topics. The associated primary literature and questions will guide each workshop. The class will be divided into two groups to cover the topic areas. At the start of each workshop, each group will discuss their results, generate consensus, and elect one group member to present their findings to the class at large.
Instructors: J. Sondek, A. Cox | Taught: Spring, TTh, 10 wks | Syllabus
PHCO746 Computer Vision in Modern Microscopy: This graduate-level course will introduce the basic principles of computer vision as a high-level image analysis tool, with an emphasis on its practical application to live-cell fluorescence microscopy. Important topics and available techniques will be discussed and demonstrated using examples from current biomedical research. The course is designed as an introduction to computer vision applications for modern microscopy and will not be mathematically or computationally intensive. Students will obtain the very basic knowledge of how to use MATLAB for image analysis, which will provide them with the opportunity to practice with the introduced techniques and expend their toolkit beyond the capabilities of standard software packages as ImageJ. There are no formal prerequisites required for this course and no prior experience with MATLAB is expected.
Instructors: D. Tsygankov | Taught: Spring, 8wks | Syllabus
Quantitative Genetics and Sequence Analysis
GNET 744 Sequence, Protein Structure, and Genome-Wide Data Analysis: This module provides an introduction to basic protein structure/function analyses combining sequence informatics and macromolecular structure. In the second half the focus will switch to analysis of genome-wide data sets and methods used for the analysis of such “big data”.
Instructors: Brenda Temple and Joel Parker | Taught: Spring, MTW, 5wks | Syllabus
BCB 716 Sequence Analysis and Comparison. The comparison of DNA and protein sequences is a classic playground for computational and statistical methods in biology, there continue to be interesting and challenging problems in this domain, and sequence comparison algorithms such as BLAST are among the most widely-used bioinformatic tools. This module covers the fundamental concepts and methods in sequence comparison using computational techniques such as dynamic programming, hidden Markov models and Markov Chain Monte Carlo.
Instructors: Todd Vision and Zefeng Wang | Taught: Fall, MF, 4wks | Syllabus
BCB 712 Information Science for Bioinformatics. This module introduces the basic information-science methods for storage and retrieval of biological information. Instructors review standard database types and their applicability to bioinformatics data generated in research laboratories. Students learn the role of metadata and ontologies as standardization mechanisms for providing interoperability between different information resource types such as genetic sequences, microarray maps, and journal articles.
Instructor: Brad Hemminger | Taught: Spring, TTH, 11:00-12:15pm | Course Website
Bioinformatics and Databases
- BCB 712 Information Science for Bioinformatics. This module introduces the basic information-science methods for storage and retrieval of biological information. Instructors review standard database types and their applicability to bioinformatics data generated in research laboratories. Students learn the role of metadata and ontologies as standardization mechanisms for providing interoperability between different information resource types such as genetic sequences, microarray maps, and journal articles.
Instructor: Brad Hemminger. Taught: Spring, TTH, 4 wks | Course Website
BCB 715 Modeling signaling and regulatory networks. The course will provide an introduction to the basic mathematical techniques used to develop and analyze models of signaling pathways and regulatory networks. Both deterministic and stochastic models will be discussed. The numerical techniques covered in the class will include methods for solving ordinary differential equations and Monte Carlo methods. If time permits, the diffusion equation also will be considered. Homework assignments will be completed using MATLAB. No experience using MATLAB is assumed. Particular emphasis will be placed on feedback and feed-forward control mechanism used to regulate biochemical pathways. The course will be self-contained, with all the necessary biology and mathematics covered in class. Upon completion of the course, students will have a working knowledge of MATLAB and be able to construct and simulate mathematical models of signaling pathways and regulatory networks.
Course Director: Timothy Elston | Taught: SPRING, TTh, 4wks | Syllabus
Computer Programming for Biomedical Students
GNET 742 UNIX and Perl for Biomedical Researchers: This module will introduce UNIX and Perl programming. It is mainly targeted towards biomedical scientists who would be able to use Perl to analyze, transform and manage large datasets.
Instructor: Hemant Kelkar | Taught: Spring, 4wks | Syllabus
GNET 743 Introductory Statistics in R for the Biomedical Scientist: This module will introduce the data analysis environment "R" and use it to illustrate basic concepts in data manipulation, plotting of complex data, and basic statistical modeling. Class examples will be general, and will aim to build familiarity and confidence with R and data analysis.
Instructors: Will Valdar and Ethan Lange | Taught: Spring, MTW, 4wks | Syllabus
WORKSHOPS THROUGH HEALTH SCIENCES LIBRARY
HSL offers a suite of individual sessions on topics related to bioinformatics and computational biology. A sampling of the workshops offered are listed below. For course availability, schedule, and further details, please click here.
- Introduction to UNIX for Biologists
- Introduction to Sequencing Technology
- Introduction to Analysis of Next Generation Sequencing Data
- Introduction to Protein Data Bank and Pymol
- Browsing Genes and Genomes with Ensembl and Ensembl Genomes (Full day workshop)
|Probability and Statistics|