Capture, analyze, manage, and utilize all types of genomics data.
The Human Genome Project kicked off a process of accrual of massive amounts of genomics data. The genomics data on diverse organisms that is now routinely generated and it is being leveraged in multiple industries with an impact on broad sectors of our economy, including the delivery of medicine, the design of pharmacological agents and even the manufacturing of industrial products such as materials and foodstuff.
Because genomic data is now found in so many different industries and scientific endeavors, a basic understanding on how to exploit it has become indispensable for scientists as well as other technical professionals. A familiarity with genomic data how to gather, analyze, and interpret it, is needed to succeed in most sectors of the life sciences and biomedical industry. Such understanding is at the core of bioinformatics.
The specialized certificate in Applied Bioinformatics is aimed at scientists in the life sciences, engineers, technical professionals working in industrial biotechnology and ancillary industries who need to utilize tools developed for bioinformatics. You will learn how to effectively utilize the data generated by the genomics revolution by giving you a solid understanding of the tools and methods of bioinformatics, including:
There will be a $60 fee upon acceptance into the program
Genetics is a fascinating topic that is frequently in the news. “Learn valuable health and ancestry information” and “Discover your genetic risk for disease” are just a few of the advertisements for direct-to-consumer genetic test kits. This course demystifies genetics and is open to anyone who wishes to learn more about inherited traits, their variation, and how they are transmitted between generations. It will provide an introduction to the principles of genetics with an emphasis on human disease. Topics include: fundamental concepts of Mendelian inheritance; basic principles of molecular genetics; inheritance patterns of genetic diseases; the human genome project; and the potential of personalized medicine.
Goals and Objectives:
Informatics is the study of structure, algorithms, behavior, and interactions of information systems. Its applications are powerful and broad, and include such fields as life sciences, data mining, business analytics, and social computing.
This hands-on course introduces the Python programming language, and is targeted toward students without prior programming experience who are interested in how informatics can be employed to provide solutions to complex, data intensive problems in a variety of scientific and business domains. After learning the core syntax and elements of the Python language, students will gain experience in the fundamentals of network programming, web services, databases and Structured Query Language (SQL), and data visualization.
Software: Students will use Python 2.7 and 3.X in this course. There is no additional cost to access this software.
Course typically offered: Online in Winter and Summer; In-class in Fall and Spring
Next Steps: Upon completion of this course, consider taking other courses in data science to continue learning.
More Information: For more information about this course, please contact firstname.lastname@example.org.
All three (3) courses required.
Bioinformatics is the glue that allows us to manipulate, utilize and learn from vast amounts of biological data. Bioinformatics tools and databases enable a range of scientific, technological, and biomedical applications that would otherwise be impossible to achieve. Familiarity with bioinformatics concepts and widely used tools allows one to effectively leverage biological data into useful biological information. The course will cover the use of resources like NCBI's Entrez and EBI, and encourages students to explore various web tools for sequence search, alignment, PCR design, protein structure, etc. An introduction to database design and the principles of programming languages will be provided as well as an overview of how bioinformatics is applied in the industry.
Review current genomic sequencing technologies while exploring the scientific and medical applications that these technologies are enabling. Instruction centers on traditional and next generation sequencing including: genetic test design strategies, bioinformatics workflows at genomic scale, population genetics and medicine. For each topic, we start with theoretical considerations and explore current literature examples. Coverage of current and developing next generation sequencing technologies is the primary goal. Bioinformatics aspects are explored from both theoretical and practical perspectives. Discussion of genetic applications enabled by emerging technologies is touched upon.
Explore bioinformatics approaches to processing genomic scale datasets and distilling consequential biomedical information. Genomic sequencing technologies deliver such huge volumes of data that specific, dedicated handling methods are critical for analysis. This course focuses on methodologies appropriate for analysis tasks commonly employed with various sequencing experiments. Instruction covers general considerations ranging from experiment configuration, data QC, and software systems, to tuning of algorithms and visualization of results. In addition, assigned work with public datasets will provide students hands on experience with several widely used methodologies, including variant discovery (e.g. cancer treatment), metagenomics (e.g. gut flora), and “Seq” technologies (e.g. RNA-Seq). Class sessions consist of slides, assigned reading and a quiz. Mandatory data processing homework is a significant part of this class. Slides summarizing performed analyses (e.g. homework) are required for completion.
Complete three (3) units.
This course will cover the fundamentals of relational database design as applied to biological information, including gene and protein sequences, protein structures, and laboratory information management systems. The theory of relational databases will be covered, including keys, normalization, and the effect of NULL data. The process of database design will be introduced and applied to specific problems that occur in many biological database design projects.
Statistics allows us to collect, analyze, and interpret data. The R programming language is one of the most widely-used tools for data analysis and statistical programming. Its easy to learn syntax, built-in statistical functions, and powerful graphing capabilities make it an ideal tool to learn and apply statistical concepts.
In this class, you will master the most widely used statistical methods, while also learning to design efficient and informative studies, to perform statistical analyses using R, and to critique the statistical methods used in published studies. No prior knowledge of statistics or R is required and emphasis is on concepts and applications, with many opportunities for hands-on work.
Software: R, a free software environment for statistical computing and graphics, is used for this course.
Course typically offered: Online in Fall and Spring
Prerequisites: Knowledge of basic programming or Introduction to Programming is recommended.
The interconnections of biological components of cells are taking center stage in biology. It is our ability to generate detailed lists of biological components, determine their interactions and to generate genome-wide data sets that has lead to the emergence of systems biology. Detailed biological 'parts catalogs' of cells are emerging, and thanks to advances in computer technologies, the interactions of these parts are being documented. This course is focused on helping current university faculty, life-science researchers, and others gain an advanced understanding of the cutting-edge field of systems biology.
Proteins play a fundamental role in biology and their structure and dynamics provide a key perspective on their function. This course aims to provide an introduction to those with an interest in developing an understanding on how the available proteins structures can be used to gain a better understanding of their function and how it could be applied to drug discovery and optimization. After an introduction to amino acids as the building blocks of proteins, we discuss the different levels at which protein structure can be analyzed, and the experimental and modeling techniques that can be used to obtain tem. We show the importance of computational molecular modeling methods for extracting information from the protein structure. We carry out a survey of different protein classification schemes and carry out a survey of the structure of major classes of therapeutic proteins and drug targets. Finally we discuss the elements that govern protein interactions with other proteins and small molecules. We summarily describe the main techniques to analyze the motion of proteins.
Goals and Objectives:
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Moores Cancer Center, UC San Diego
J. Craig Venter Institute
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