1. iPlant Genomics in Education hilgert@email.arizona.edu

2. 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?

3. The Promise of Public Databases & Open Source Software ResearchEducation 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.

4. 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.

5. Training Budding (Data) Scientists Objectives: Teach concepts Convey skills Guide research

6. Resources

7. 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?”

8. 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

9. Objective: Convey Skills Handle data Identify meaning in sequences Associate sequence with information Think computationally Select the appropriate tools Critically analyze results

10. 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

11. Objective: Guide Research Class research Independent studies Distributed research projects

12. 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.

13. 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

14. iPlant Genomics in Education hilgert@email.arizona.edu