Reproducible and FAIR Bioinformatics Analysis of Omics Data 2021

Please note that this course will be an online event in 2021.

MDI Biological Laboratory is excited to present an updated and extended introduction to our previous Applied Bioinformatics course. Our new focus on FAIR data – that is data that are Findable, Accessible, Interoperable and Reusable – addresses a key initiative of the NIH and will prepare participants to benefit from the vast amount of publicly available biomedical data. At the same time, we have maintained our emphasis on teaching students how to analyze gene expression data, because the skills required to analyze large transcriptomic data sets are rapidly transferable to proteomics and metabolomics.

The course begins with a complete introduction to the R statistical programming environment, and is designed throughout to be comfortable for participants who are new to R, bioinformatics and biostatistics. At the same time, the course is designed to be rewarding for participants with substantial experience in these areas, because each learning module includes exercises appropriate for beginner, intermediate and advanced students. A substantial amount of the course is dedicated to independent work on assigned problems. We have found that this approach leads to much higher levels of confidence and better retention of key concepts as long as challenges are appropriate to a specific student and students have plenty of access to knowledgeable teaching assistants. This class will have at least one teaching assistant for every six attendees.

The two week format of Reproducible and FAIR Bioinformatics Analysis of Omics Data enables students to build confidence in diverse areas including the following:

• Planning Omics Experiments
• Accessing the UNIX Environment
• Identifying Differentially Expressed Genes
• Pathway Analysis of Gene Expression Data
• Applying Machine-Learning and Data-Driven Approaches to Gene Expression Data
• Taking Advantage of Publicly Available Data
• Ensuring Rigor and Reproducibility
• Creating Publication Quality Visualizations of Complex Data
• Sharing Code and Data
• Analyzing Single-Cell RNA-seq Experiments
• Analyzing Microbiome Data
• Documenting Statistical Approach in a Publication
• Developing a Data Management Plan