Both workshops are now completely full. Thanks for your interest!

Two optional workshops for trainees (students and postdocs), limited to 25 enrollees each, will be offered on Friday morning preceding the meeting, tentatively 9:00 -- 12:00. Lodging at the Kellogg Center on Thursday evening will be provided to attendees. 

How to register. Email meeting co-chair Charles Hoogstraten, hoogstr3@msu.edu, with the following:

Subject: RRM Workshop
Text:
Workshop 1: Career Development Strategies:  Plan Your Career, Work Your Plan
Facilitator: Dr. Julie Rojewski, Director of Ph.D. Career Services and Program Manager for the BEST (Broadening Experiences in Scientific Training) program, The Graduate School, Michigan State University.

When you think about your “dream job,” do you also think about what it takes to get that job? Not sure exactly what you want to do, and wonder what next steps make sense? Wonder no more! In this session, a team of career development experts from the MSU Graduate School will take you through a 3-hour career workshop that will equip you with the expertise and tools for you to take charge of your career now, and into the future.  We will discuss how to explore career options, how to network to find great opportunities, and how to go after the job of your dreams.  This is a great chance for trainees at all levels to take stock of their career ambition and take steps to getting ahead!


Workshop 2: RNA-seq Data Analysis: From Transcript Abundance Estimates to Biological Insight

 

Facilitator: Dr. Stephanie Hickey, Research Technology Support Facility, Michigan State University

In this workshop, participants will use R programming to mine meaningful biological insights from RNA-seq data. Starting with transcript abundance estimates, the workshop will cover data filtering and normalization, differential expression analysis, and functional enrichment analysis, with a heavy emphasis on data visualization. Participants will be introduced to data manipulation with the tidyverse suit of R packages and plotting with ggplot2.