MDI Biological Laboratory
Bioinformatics

Reproducible and FAIR Bioinformatics Analysis of Omics Data

A training course for graduate students, post-doctoral trainees, and others who would like to incorporate bioinformatics into their biomedical research

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Application Deadline: 06/15/2022

Overview

This course is an updated and extended introduction to our previous Applied Bioinformatics course. The renewed focus is on FAIR data – that is data that are Findable, Accessible, Interoperable and Reusable. This addresses a key initiative of the NIH and will prepare participants to benefit from the vast amount of publicly available biomedical data. 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

 

Course Directors

Course Faculty

Invited Speakers

Tuition

2022 rates for in person course:

Students/post-docs: $1,750 USD

Faculty/professionals: $2,000 USD

Limited financial aid may be available for students with need. The funding request form is included in the online application.

CEUs

Students currently enrolled in the Molecular and Cellular Biology (MCB) Graduate Program at the Geisel School of Medicine at Dartmouth College may receive 1 full credit for completing this as an elective.

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Funding

This research training opportunity is supported by a research education grant from the National Human Genome Research Institute of the National Institutes of Health under grant number R25 HG011447.

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