ON Demand Courses
A high-performance computing cluster empowers users to harness computational power far beyond that of a single machine. This online course module teaches users how to run and manage computational analyses on the HARDAC cluster, Duke's high-performance computing resource tailor-designed for computational genomics. The course module consists of a set of interactive activities that take users from connecting to HARDAC all the way to running powerful array jobs. To follow the activities included in this course, users must be granted access to HARDAC, which is available to everyone on Duke's campus through the Computational Solutions service center. To inquire about access, please contact the Computational Solutions team.
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Custom software is a common component of almost any computational workflow. Using and installing these on a shared high-performance computing environment such as HARDAC presents challenges due to frequently conflicting recursive dependency chains and versions. A number of mechanisms have been developed to isolate software environments, their versions, and their dependencies from each other, and understanding these is key in recruiting already installed software packages, and installing one's own. This course teaches the use of Environment Modules as provided on HARDAC. It is the first in a planned series of three; the other two will teach the use of conda environments and Singularity containers, respectively. To follow the material in this class effectively, you should have access to HARDAC, and you should have taken (or have full command over the material presented in) the "High Performace Computing (HPC) / SLURM Best Practices for HARDAC" online course.
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GCB Academy is a series of stand-alone workshops in genome topics offered to Duke faculty, postdocs, graduate students and staff at little to no fee. Sabbatical scholars and other collaborating visitors may request registration and will be accommodated on a space-available basis. The workshops, taught by faculty and staff in GCB, range from 101-style introductions in genomic technologies, computational approaches and mass spectrometer analyses to more focused topics of molecular analysis. They are intended to introduce Duke community members to the field and build capacity in areas to further their own research.
There is no enrollment cap for online courses. Enrollment for in-person courses is capped at 20 students, and registration closes 7 days before each class. You will receive an enrollment confirmation 3 days before each class. For all in-person courses, a $100 no-show fee will be assessed if you fail to notify us 24 hours before the class begins. In the event you are unable to attend your registered course(s), please contact Julia Walker. Many in-person courses have a waitlist and we can offer your spot to another person.
Additional fall courses may be added at a later date. Please check back for updates.
Dates: October 16, 23, 30; 2 - 3:30 p.m.
Registration Deadline: October 13
Instructor: Sunil Suchindran
Location: Zoom (link will be emailed to registrants)
This workshop has two objectives. First, it seeks to develop an understanding of risk prediction and classification in the Omics setting. Second, for researchers who plan to develop risk models, this workshop seeks to provide concrete steps for study design, analysis, and interpretation. To accomplish these goals, we will discuss how different aspects of a statistical model can provide measures of association or measures of predictive accuracy. This distinction is important in understanding how developing a model for association/etiology/causal inference is conceptually different from using the model to predict. We will then discuss risk models in the conventional setting: larger sample sizes with a smaller number of predictors. We will cover study design, statistical models, and performance metrics. The workshop seeks to develop an appreciation of challenging considerations in the field, but also seeks to provide clear steps on how to proceed. Finally, we will review areas of active research and in what direction the field is moving. After establishing foundations, we will move into the Omics realm, which is characterized by smaller samples sizes and thousands of predictors. We will review current best practices with an emphasis on estimating performance. This course will not include any hands-on coding because of time limitations, but this will be the topic of a future course. The workshop will also be useful to those interested in general risk models not specific to Omics.