- Introduction to DNA Sequencing Technologies
- An Overview of Risk Prediction and Classification in Omics Settings
- Introduction to Scientific Computing for Genomics
- Fundamentals of Mass Spectrometry from Proteomic & Metabolomic Analysis
- RNA-Seq Analysis (Waitlist only)
- Experimental Design: Get the Most Out of Your Proteome
- Proteomic Data Analysis: Strategies and Software Solutions
- Strategies for Metabolomic Data Collection, Analysis & Interpretation
- Single-cell RNA-Seq
- Introduction to Biobanking
- Refresher on Medical Genetics and Genome Medicine
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 Julia Walker. Many courses have a waitlist and we can offer your spot to another person.
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.
An Overview of Risk Prediction and Classification in Omics Settings
Date: Oct. 26, 2018; 9:00 A.M. - 12:00 P.M.
Registration Deadline: Oct. 8, 2018
Instructor: Sunil Suchindran
Location: 2240 CIEMAS
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. This course will not include any hands-on coding because of time limitations, but this will be the topic of a future course. The course focuses on understanding the most important aspects of risk prediction and classification.
Introduction to Scientific Computing for Genomics
Date: Oct. 29, 30, 31, and Nov. 2, 2018; 8 A.M. - 12 P.M. each day
Registration Deadline: Oct. 19, 2018
Instructor: Hilmar Lapp
Location: 2240 CIEMAS
Cost: $200 for faculty, postdocs and staff; Free for grad students
Computing has become an integral and indispensable part of genomic biology. This course teaches basic skills in scientific computing, with a focus on applications for genomic science, aimed at making you more productive, your computational work more reliable, and your research easier to reproduce and extend, including by your future self. The course includes introductions to (1) using Unix shell commands to efficiently find, organize, and stage data for analysis; (2) basic data types, control flows, functions, and 3rd party packages for the Python programming language commonly encountered in scientific computing; (3) using version control to manage with confidence the numerous directions research code takes from inception to publication; and (4) techniques for optimizing how your computational analyses run on a high-performance computing cluster. The format of the course is inspired by the acclaimed Software Carpentry-style bootcamps. Hence, this is a fully hands-on workshop, and students are expected to bring a laptop.
Pre-requisites: "Introduction to Unix" (or equivalent experience).
Fundamentals of Mass Spectrometry from Proteomic & Metabolomic Analysis
Date: Oct. 30, 2018; 1:00 - 5:00 P.M.
Registration Deadline: Oct. 22, 2018
Instructor: Arthur Moseley
Location: 2240 CIEMAS
Liquid chromatography coupled with tandem mass spectrometry (LC/MS/MS) continues to be the key technology for the qualitative and quantitative analysis of peptides, proteins and metabolites for both basic and clinical research projects. This GCB Academy session is designed as an introduction for researchers needing to expand their knowledge of the use of LC/MS/MS-based methods for proteomics and metabolomics, and thus help researchers better understand how these technologies can help inform their research goals. Background material in basic protein/metabolite chemistries will be provided, with an emphasis on how to use the physicochemical characteristics of these biomolecules for sample preparation specifically for LC/MS/MS analyses. In addition, the fundamentals of liquid chromatography and mass spectrometry will be discussed to enable students to understand the nuances of the experimental designs required to address their specific project. Real-world examples will be used to illustrate sample preparation and analysis strategies, including basic identification projects, characterization of Post-Translational Modifications and differential expression analyses (including 'omic biomarker discovery and targeted biomarker verification).
RNA-Seq Analysis *Waitlist Only*
Date: Nov. 8, 2018; 9 am – 1:00 pm (Limited to 12 participants)
Registration Deadline: Oct. 29, 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).
This course will provide an in-depth overview of experimental design, focusing on proteomic analysis of protein post-translational modifications (PTMs) and protein expression in (but not limited to) mammalian cells, tissues and biofluids. Topics will be aimed at getting maximum biological information from your samples. We will discuss methods for enriching subproteomes and PTMs; best practices for insuring sample integrity and avoiding common contaminants that will be carried downstream; and how to be aware of additional factors that might influence reproducibility across biological replicates. In addition, we will discuss where discovery-based or targeted proteomic analyses may be most appropriate. Feel free to bring specific questions about your favorite proteins, model systems, or biological matrices. Prerequisite: Fundamentals of Mass Spectrometry for Proteomic and Metabolomic Analyses, encouraged, but not required.
Liquid chromatography coupled with mass spectrometry (LC/MS) is a versatile tool for the qualitative and quantitative characterization of peptides, proteins and metabolites for both basic and clinical research projects. One of the most important considerations in being able to translate LC-MS datasets into meaningful biological observations is to effectively use open source software packages and/or online resources geared toward LC-MS based datasets. This GCB Academy session is designed as a complement to GCB Academy course “Fundamentals of Mass Spectrometry for Proteomic and Metabolomic Analyses” (Nov. 7) and GCB Academy course “Experimental Design: Get the most your of your proteome” (Nov. 8) and is intended for users of the Proteomics and Metabolomics Shared Resource who have or plan on generating LC/MS based Proteomic Datasets with the Shared Resource. This first portion of the course will focus on the effective use of Scaffold to characterize qualitative proteomic datasets. This will include an overview of Scaffold and features such as interpretation of spectral matches at a protein or peptide level, gene ontology classification, homology matching, spectral count data, and data export. The second portion of the course will cover common proteomic data analysis strategies from supplemental data (typically .xlsx file formats from Rosetta Elucidator) provided as part of the Shared Resource’s quantitative proteomic workflows. This will include an overview of the typical features of a quantitative data return document, various data summarization levels, calculating peptide/protein relative fold-changes and p-values, exporting data for motif analysis (PTM specific datasets), and performing Principle Component Analysis (PCA) and 2D Clustering within JMP Pro.
Metabolomics has emerged as a powerful approach for characterization of molecular systems and also development of biomarkers for disease progression or diagnosis. Broadly, metabolomics is the characterization of small molecules by mass spectrometry and can include both "unbiased" or non-targeted techniques, as well as "targeted" methods. A common misconception in the use of metabolomics is that non-targeted metabolomics can capture the majority of metabolites in any sample type; however, unlike proteomics, metabolomic analysis of any sort is inherently biased based simply on the type of solvent extraction used to isolate the metabolites. Given these complexities, the most important aspect for a successful metabolomics study is deciding which technique to use and understanding the data each approach will likely be able to provide. In this introductory course, we will utilize case studies to discuss the critical differences in targeted and non-targeted metabolomics and an investigator might choose one over another. For non-targeted metabolomics, the course will cover current methods for identification and quantification using publicly available tools as well as methods for analysis of high dimensional datasets. For targeted metabolomics, the course will cover methods for accurate quantification, how to enable longitudinal translation of metabolomics assays, and tools focused on targeted pathway mapping.
Date: Nov. 15, 2018; 9:00 A.M. - 1:00 P.M. (Limited to 12 participants)
Registration Deadline: Nov. 5, 2018
Instructor: David Corcoran
Location: 101 Chem
Cost: $50 for faculty, postdocs and staff; Free for grad students
This 4-hour hands-on tutorial will provide you with experience working with data from a single-cell RNA-Seq experiment. We will cover quality control, filtering, normalization, clustering, differential expression and mark identification analysis.
Pre-requisites: Must have previously taken the GCB Academy “RNA-Seq Analysis” course.
This seminar will offer an introductory overview of key considerations and best practices in establishing and maintaining clinical biospecimen collections for genomic and precision medicine research. Topics covered will include: basic concepts in biobank and cohort research; role of standardization, harmonization, and quality control; maintaining unique sample identification and robust chain-of-custody tracking; need for secure information and inventory management systems for samples and data; important considerations in repository design; and an overview of biobanking resources at Duke and beyond.
Course summary: This 90-minute course will provide attendees with an overview of general principles of genetics, genomics and molecular biology, and clinical applications and technologies currently used in clinical practice. In particular, the course will provide an overview of genomics, genome-wide association studies and other large initiatives and a range of testing technologies for diagnosis and treatment. Introduction of new technologies such as liquid biopsies will also be briefly discussed. Continuing education credits will be available.