iPlant Genomics in Education

hilgert@email.arizona.edu

The Advent of Big Data in Biology

The abundance of biological data generated by high-throughput technologies creates challenges, as well as opportunities:

•How do scientists share their data and make it publically available?

•How do scientists extract maximum value from the datasets they generate?

•How can students and educators (who will need to come to grips with data-intensive biology) be brought into the fold?

The Promise ofPublic Databases & Open Source Software

Research Education

For the first time in the history of biologystudents can work with

the same dataat the same time

with the same toolsas research scientists.

Insights from Genomics in Education Washington University, June 16-19, 2009

44 participants from three worlds and three kingdoms

• Problem/Question-based learning.• Students have limited patience for pure computer

work and benefit from wet lab “hook.”• Someone has to care about the data generated by

students.• Projects should potentially lead to publication.• Move from simple workflows to complex tools and

from individual experiments to course-based and distributed research projects.

Training Budding (Data) Scientists

Objectives: • Teach concepts• Convey skills• Guide research

Resources

Objective: Teach (Biological) Concepts

• What are genes?• Do genes have a structure?• What are proteins?• How are genes and proteins connected?• How do proteins “know” where to act?• How does life convey information?• How can we find the information in bio molecules?• What is bioinformatics?• What is “Big Data?”

• GeneBoy (GC-rich; Translate)• Multimedia Primer– Meaning #0, #9, #12– Structure #0, #13, #14, #15, #16, #17, #18, #19, #20– Evidence > Annotation Tool

• DNA Subway (Lay-out: 4 lines, start project, run analyses)– Annotation (Repetitive DNA, Different tools, Visualization)– Mining (sequences occur in families, evolutionary forces)– Relationships (that’s it)– RNA-Seq (Big Data – and then some...)

• iPlant Academy & Textbook

Objective: Teach (Biological) Concepts

Objective: Convey Skills

• Handle data• Identify meaning in sequences• Associate sequence with information• Think computationally• Select the appropriate tools• Critically analyze results

• GeneBoy (GC-rich; Translate)• DNA Subway– Annotation (Viewing results; Apollo; Uploading; Comparing)– Mining (sequences occur in families, evolutionary forces)– Relationships (dealing with sequences; publishing)– RNA-Seq (patience, double-checking)

• Discovery Environment– Thinking computationally

• Atmosphere– Command line, sophistication

Objective: Convey Skills

Objective: Guide Research

• Class research• Independent studies• Distributed research projects

Objective: Guide Research

For the first time in the history of biologystudents can work with

the same dataat the same time

with the same toolsas research scientists.

• GeneBoy (Quick checks)• DNA Subway– Annotation (Starting Project with own data, sharing)– Mining (Identifying gene/transposon families)– Relationships (Publishing barcodes; human origins)– RNA-Seq (heard Roger’s presentation?)

• Discovery Environment (see iPlant Academy)– Setting students free...

• Atmosphere (see iPlant Academy)– ...to soar high and above

Objective: Guide Research

iPlant Genomics in Education

hilgert@email.arizona.edu