BIg Data Science in Biology & Medicine: Careers in Bioinformatics & Computational Biology

1. BIg Data Science in Biology & Medicine: Careers in Bioinformatics & Computational Biology Moderated by: Andrea Ilg, Education Manager – ilgan@ohsu.edu Date: Thursday, July 25th 2013

2. Webinar Agenda • Welcome – Andrea Ilg, ilgan@ohsu.edu • Big Data Science– Dr. McWeeney • Program Overview & Careers in Bioinformatics – Dr. McWeeney • Question/Answer Session

3. Getting local…. How to learn more • BCB focused DMICE seminars • BCB Facebook & twitter feeds www.facebook.com/OHSUBioinformatics www.twitter.com/OHSUBCB

4. Housekeeping Items for Webinar • Please post any questions to chat pod • *6 will mute your phone • Please do not place your phone on “hold” • This presentation is being recorded

5. Shannon McWeeney, PhD • Associate Professor, Biostatistics & Bioinformatics • Division Head, Bioinformatics and Computational Biology • PhD Statistical Genetics, MSE Computational Biology • Development and application of statistical and computational methodologies to solve research bottlenecks • Integrated approaches to facilitate identification of drugs for individualized clinical trials (Precision Medicine)

6. A Day in the Life • Our clock starts at Diagnosis • Will the patient respond to Drug X? • For this patient, what treatment plan is best (Individualized Therapeutics)? • If the patient relapses, what is the best choice regarding second line of therapies (for this patient)? Autumn Boynton, age 4

7. The Fourth Paradigm (Jim Gray, Microsoft) • First Paradigm • Descriptive • Second Paradigm • Theoretical • Third Paradigm • Computational • Fourth Paradigm • Exploration

8. “Day of Big Data*” • New technology = More data at finer resolution (spatial/imaging, genomics etc) • Increase in amount and type of data = More advanced algorithms • Sophisticated methods for analysis & integration = more complicated data structures *Nature462, 722-723

9. Reshaping clincial medicine Chin et al. (2011) Nature Medicine

10. “Dammit Jim, I’m a doctor, not a computational biologist!*” • Advent of big data is leaving biologists and clinicians at a loss • Sets up “blind trust” scenarios • Bioinformatics can be seen as rate-limiting step *Modified from Christophe Lambert (http://blog.goldenhelix.com/?p=652)

11. What we need to do What you need to know • Shift from data mining to focused biological questions • Understand assumptions, parameters and limitations of methods • Make data trustworthy (confidence and uncertainty) • Overcome cognitive barriers via visualization and novel ways to make data accessible

12. What is the question? • Critical to define our biological question of interest • Ability to translate that to a hypothesis and then appropriate analysis method • Discovery is wonderful for hypothesis generation. But from this we should be able to focus on key questions and eventually hypothesis testing.

13. Career Advice • Erase mental “barriers” • View your coursework as valuable – skills you can use and master • Don’t be afraid to ask questions • Feed your mind – this is a life long process • Don’t be afraid of change • Seek out research and volunteer opportunities – it will help you clarify what you are drawn to for possible career areas • Best path is one that you are good out, that you are passionate about and that is sustainable

14. Translational Bioinformatics @OHSU What is Translational Bioinformatics? • Translational Bioinformatics is defined as the development of analytic, storage, and interpretive methods to optimize the transformation of the increasingly voluminous genetic, genomic, and biological data into diagnostics and therapeutics for medicine. • Our program was designed with the recognition that a key component of this translation is computational biology, the underlying algorithmic and quantitative framework.

15. Translational Bioinformatics @ OHSU Active Areas of Study • Systems Biology • Statistical Genetics • Text Mining and Information Retrieval • Imaging • Computational Neuroscience

16. Translational Bioinformatics @OHSU Course Overview • BMI 550/650: BMI I: Algorithms in Bioinformatics and Computational Biology • BMI 551/651: BMI II: Statistical Methods in Bioinformatics and Computational Biology • BMI 552/652: Research in Bioinformatics and Computational Biology • BMI 553/653: Readings in Bioinformatics and Computational Biology • Additional Core Coursework • Biostatistics, Computer Science, Genetics • Scientific Programming • Systems Biology • Electives: • Includes Imaging, Functional Genomics, Systems Biology, Information Retrieval and Text Mining, Machine Learning, Mathematical Modeling

17. Translational Bioinformatics @OHSU Integration with Cutting-Edge Institutes • The following institutes at OHSU provide seminars, internships, project development and collaboration for our students • Knight Cancer Institute • Personalized Medicine and Genomics, Therapeutic Interventions • Oregon Clinical & Translational Research Institute • Virtual data warehousing and translational bioinformatics • Pacific NW Regional Center of Excellence • Vaccine development, Systems Biology Common theme is to facilitate the diagnosis, prevention, and treatment of human disease

18. Translational Bioinformatics @OHSU Future Job Prospects • Masters Degree • Staff Bioinformaticist (Academic and Industry) • Analyst (Academic and Industry) • PhD • Postdoctoral Fellowships (Academic and Industry) • Faculty Positions (Academic) • Senior Analyst (Academic and Industry) • Staff Scientist (Academic and Industry) • Group Leader (Industry)

19. Question/Answer Session

20. Thank You for Joining Us • Andrea Ilg, Education Program Manager • ilgan@ohsu.edu • 503-494-2547 Facebook: ww.facebook.com/OHSUBioinformatics Twitter: www.twitter.com/OHSUBCB Website: http://ohsu.edu/bioinformatics