The Short Course on Methods for Multiomics Data Analysis

The Short Course on Methods for Multiomics Data Analysis will provide practical experience in genomic data science and team training skills using Common Fund data as a vehicle for learning.  The course will transform how scientists do research by introducing new strategies that drive discovery and promote rigorous, reproducible research practices while also creating a collaborative network of researchers and delivering critical skills and practice in team collaboration. A balanced mix of lecture, computational labs, and team building activities will provide participants with experience in state-of-art technical skills as well as social ‘soft skills’ that are often overlooked in scientific training.

The course is designed to create an inclusive and welcoming environment for researchers from a broad range of academic levels from doctoral students to early-stage investigators. Participants will gain skills and confidence required to navigate and use Common Fund data through data exploration, worked examples of data reuse, and networking with scientists responsible for generating the data. Detailed examples of successful data reuse case studies will provide hands on training in accessing, analyzing, and presenting research findings using Common Fund datasets, while also inviting critical examination of the studies. Participants will solidify these technical skills by putting them into practice in an interdisciplinary team setting. With guidance from mentors, students will engage in activities that reinforce teamworking skills that build trust and respect for their colleagues. Mentoring will help to clarify some of the technical and scientific complexity of genomic data science, while also providing implementation strategies for working in biomedical research teams. All of this is built around a cutting-edge topic: multiomics data analysis.

At the end of the course, all participants, remote and in-person, will be able to

• access and describe Common Fund data sets and experimental designs that produced those data; and
• evaluate sound experimental designs that produce robust and unbiased results.

In addition to these outcomes, in-person participants will be able to:

• implement appropriate analysis methods;
• integrate datasets across multiple data types;
• use tools for disseminating reproducible research results; and
• employ strategies to anticipate, prevent, and manage conflict in scientific research teams.

Prerequisites

In-person participants only: This course provides training in data analysis using R. If you have written code in any programming language, you’re ready for this course. You should also have some prior experience with omics data (e.g. genomics, proteomics, transcriptomics, metabolomics).

Remote participants will attend morning lectures only. There are no prerequisites for remote participants since computational labs will not be available for them.

Travel and registration scholarships are available for in-person participants at all career stages. Learn more below.