Transcript Genomics in Education

iPlant Genomics in Education
[email protected]
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 of
Public Databases & Open Source Software
For the first time in the history of biology
students can work with
the same data
at the same time
with the same tools
as research scientists.
Research
Education
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
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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?”
Objective: Teach (Biological) Concepts
• 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)
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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: Convey Skills
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Handle data
Identify meaning in sequences
Associate sequence with information
Think computationally
Select the appropriate tools
Critically analyze results
Objective: Convey Skills
• GeneBoy (GC-rich; Translate)
• DNA Subway
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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: Guide Research
• Class research
• Independent studies
• Distributed research projects
Objective: Guide Research
For the first time in the history of biology
students can work with
the same data
at the same time
with the same tools
as research scientists.
Objective: Guide Research
• GeneBoy (Quick checks)
• DNA Subway
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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
iPlant Genomics in Education
[email protected]