Training, Community and Finding Help

There are a variety of resources for training on various aspects of bioinformatics, analytics and data skills available at the Fred Hutch, in the Seattle area and on the web.

Resources At Fred Hutch

Note: Currently resources at the Hutch are in flux due to staffing changes and evolving data science support mechanisms. Stay tuned for new training opportunties beyond the peer-to-peer resources dsecribed here.

In Person Courses

All in person training has been put on hold.

Community Groups

Currently, peer-to-peer training and support for data intensive work at the Fred Hutch is mainly avilable via the Fred Hutch Bioinformatics and Computational Research (FH-BCR) Slack workspace here. This Slack workspace is open to Fred Hutch Cancer Center staff (with fredhutch or scca emails) as well as collaborators at some local institutions. The workspace hosts a question and answer channel as well as evolving chnanels to meet community needs such as supporting groups organizing to develop and distribute new resources to the community, collaborations with external companies/efforts that aim to develop shared Fred Hutch infrastructure, and topic-oriented groups like these:

• Workflow Manager User Group: Discusses issues related to the use of workflow managers Nextflow and Cromwell at Fred Hutch, including support for shifting from bash or snakemake pipelines, software containerization and configuration for local and shared workflow useage. The group welcomes users of all levels of expertise as well as those just interested in learning about how workflow managers can move their science forward. See the #workflow-managers channel on FH-BCR Slack for more information.

• R User Group: Discusses programming, data analysis, and troubleshooting in R for users of a wide range of expertise and research topic, as well as tools based in R such as Shiny applications. See the #r-user-comm channel on FH-BCR Slack for more information.

• Python User Group: Discusses programming, data analysis, and troubleshooting in Python and related topics in general software development. See the #python-user-comm channel on FH-BCR Slack for more information.

Office Hours

• SciComp Next Gen/HPC Office Hours: Scientific Computing (SciComp) supports researchers interested in emerging technologies like next generation sequencing and cloud computing. Staff are available to answer questions about getting started building analytical pipelines in the cloud, and generally making computation more scalable and reproducible. Email scicomp to find out the current dates, times and (typically remote) meeting location information.

Resources In Seattle

• UW Biostatistics Summer Institutes offer yearly intensive courses over the summers on a wide variety of topics.
• Meetup hosts various coding groups that meet regularly to share skills and provide networking opportunities. RLadies Seattle and Seattle UseR both include leaders from Fred Hutch.

Resources On the Web

Classroom-Style Courses

These resources are organized in a lecture type format as slides, screencasts, and video. Most are work-at-your-own-pace, but some may be linked to a course calander.

• MCB517A: Tools for Computational Biology: A graduate-level course taught for UW by Fred Hutch CompBio faculty. This links to a GitHub repository that includes all lectures and homework.
  • How to install necessary software for this course
  • Ask questions about this course
  • Course materials all available for free
• edX: Offers a collection of courses for Data Analysis and Statistics and Bioinformatics
  • Rafael Irizarry of Dana Farber has online programs available through edX:
    • Introduction to Data Science
    • Data Analysis for Life Sciences
    • Data Anaysis for Genomics
  • Generally speaking, edX courses are all free to audit for a limited period of time. Unlimited access and the ability to earn a course Certificate will require payment
• Coursera: Offers a collection of courses for Data Science and Bioinformatics
  • R Programming: A beginner-level program has five mini-courses. It takes about 4 months to complete.
  • Statistics with R: A beginner-level program with five mini-courses. It takes about 7 months to complete.
  • Genomic Data Science: An intermediate-level program for those who are already aquainted with R. It has eight mini-courses. It takes about 6 months to complete.
  • Python Programming: An intermediate-level program that takes about 4 months to complete.
  • Coursera offers a 7-day free trial, and is a paid subscription service after
• Udacity: Offers a collection of courses for Data Science
  • Udacity is a paid subscription service
  • Currently offering one month free for their Nanodegree programs.
• Udemy: Offers a collection of courses for Data Science
  • Udemy offers courses at various price points.
  • Keep an eye out for sales which happen regularly and can drastically reduce the cost.
• CognitiveClass.ai Offers a collection of courses for data science, AI, and cloud computing.
  • All courses are free
• The Open Source Data Science Masters: An open-source curriculum for learning data science. This is a mixed media course made up of videos, books, and slides.
  • Some content is free, some is paid
• CalTech Learning from Data
  • A free YouTube series

Interactive Coding Platforms

These resources offer classes that are work-at-your-own-pace with a major focus on hands-on problem-sets and projects.

• DataQuest: A subscription service that offers programs and courses focused on data anlysis and engineering in Python and R.
  • Tiered payment system with basic and premium plans
• CodeAcademy: A subscription service that offers coding programs and courses in many different languages.
  • Tiered payment system with limited content available for free

Free ebooks

• Biostar Handbook: A Beginner’s Guide to Bioinformatics

• R Cookbook Second Edition

• Advanced R

• R for Data Science

• O’Reilly books available through Seattle Public Library

Misc

• The Carpentries, with lessons from Data Carpentry and Software Carpentry

• ROSALIND interactive bioinformatics problem set