Difference between revisions of "ISMB 2018: BioinfoCoreWorkshop"

From BioWiki
Jump to navigationJump to search
Line 26: Line 26:
  
 
==Part C: When good experiments go bad: Negotiating experiment quality failures==
 
==Part C: When good experiments go bad: Negotiating experiment quality failures==
- detecting failure
+
* detecting failure
- guidelines for terminating bad projects
+
* guidelines for terminating bad projects
  
 
==Part D: Small group discussion==
 
==Part D: Small group discussion==

Revision as of 08:55, 25 April 2018

Organizers:

Madelaine Gogol, Stowers Institute, United States Hemant Kelkar, University of North Carolina, United States Alastair Kerr, University of Edinburgh, United Kingdom Brent Richter, Partners HealthCare of Massachusetts General and Brigham and Women’s Hospitals, United States Alberto Riva, University of Florida, United States

Presentation Overview

The bioinfo-core workshop is scheduled for Saturday, July 7, 2018, from 2:00-4:00 pm.

The workshop will explore three topics relevant to bioinformatics core facilities. Members of core facilities will share their experience and insights in lightning talks followed by topical small group discussions. Introduction

Part A: Strategies for Hiring, Recruiting, and Interviewing new bioinformaticians

  • finding and hiring people
  • interview techniques and questions
  • best practices for recruiting candidates

Part B: Containerization, Clouds, and Workflows

  • cloud infrastructure limitations and recommendations
  • Key datasets in Clouds
  • containerization
  • workflow development and results of a favorite-tool survey

Part C: When good experiments go bad: Negotiating experiment quality failures

  • detecting failure
  • guidelines for terminating bad projects

Part D: Small group discussion

During this session, audience members will divide into groups based on their own interests. Groups will come up with their main take away points and bring them back to the larger group. Topics may include: - interviewing / hiring / etc. - containers, clouds, workflows - experiment failure / quality control - single cell analysis - nanopore