- RNA-Seq Analysis **WAITLIST ONLY**
- Introduction to DNA Sequencing Technologies
- An Overview of Risk Prediction & Classification in 'Omics settings
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.
Enrollment for each course is capped at 20 students, and registration closes 10 days before each class. You will receive an enrollment confirmation 10 days before each class. For all 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 firstname.lastname@example.org. Many courses have a waitlist and we can offer your spot to another person.
RNA-Seq Analysis **WAITLIST ONLY**
Date: March 29, 2018; 9 am – 1:00 pm (Limited to 12 participants)
Registration Deadline: March 19, 2018
Instructor: David Corcoran
Location: Old Chem 101
Cost: $50 for faculty, postdocs and staff; Free for grad students
This 4-hour tutorial will provide you with a better understanding of the data processing and analysis methods that are used in RNA-seq analysis. We will cover topics such as data quality control, normalization, and calling differentially expressed genes. We will provide hands-on experience that will allow you to go back to your lab and work with your own data.
Pre-requisites: "Introduction to Unix" and "Introduction to Scientific Computing for Genomics" (or equivalent experience).
During the past decade, a new generation of high-throughput DNA sequencers has transformed biomedical and biotechnology research. These new technologies have fostered the development of a wide range of applications to basic and clinical research, including SNP discovery, transcriptome profiling, genome sequencing, and epigenetics. The goal of this introductory course is to teach the basic principles of next generation sequencing technology (NGS) and to present an overview of various library preparations and their applications. Advantages and limitations of various methods will be discussed and compared across technologies/platforms (Illumina, PacBio, Oxford Nanopore, Ion Torrent). This course will also provide an introduction to primary data analysis and data quality assessment steps. Attendees will become familiar with NGS technology terms and fundamentals, NGS data format and quality, and will acquire a better understanding of how to choose a suitable NGS sequencing method or instrument for their study.
Pre-requisites: Basic understanding of molecular biology.
This course 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 course 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 course 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. Prediction models in Omics often use machine-learning techniques, so we will cover some common machine-learning techniques and what makes them different from more conventional models. We will review current best practices with an emphasis on estimating performance.
We will conclude with a mix of topics including: the time-to-event setting, the competing -risk setting, the challenges of replication in biomarker research, and a summary of steps to develop a risk-prediction model.