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![Web Logs etc. Are
Not Enough
Structure Summary page activity for
H1N1 Influenza related structures
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Jul. 2008
Jan. 2009
Jul. 2009
Jan. 2010
Jul. 2010
3B7E: Neuraminidase of A/Brevig Mission/1/1918
H1N1 strain in complex with zanamivir
1RUZ: 1918 H1 Hemagglutinin
1/28/14
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* http://www.cdc.gov/h1n1flu/estimates/April_March_13.htm
SIB Biel/Bienne
[Andreas Prlic]](https://image.slidesharecdn.com/sib0114-140128023905-phpapp01/75/Bioinformatics-in-the-Era-of-Open-Science-and-Big-Data-5-2048.jpg)
![automate: workflows, pipeline &
service integrative frameworks
CS
SE
pool, share & collaborate web
systems
scientific software
engineering
semantics & ontologies
machine readable documentation
nanopub
1/28/14
[Carole Goble]
SIB Biel/Bienne
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![http://sagecongress.org/Presentations/Sommer.pdf
[Josh Sommer]
1/28/14
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40](https://image.slidesharecdn.com/sib0114-140128023905-phpapp01/75/Bioinformatics-in-the-Era-of-Open-Science-and-Big-Data-40-2048.jpg)
![http://sagecongress.org/Presentations/Sommer.pdf
[Josh Sommer]
1/28/14
SIB Biel/Bienne
41](https://image.slidesharecdn.com/sib0114-140128023905-phpapp01/75/Bioinformatics-in-the-Era-of-Open-Science-and-Big-Data-41-2048.jpg)
Uploaded byPhilip Bourne
Bioinformatics in the Era of Open Science and Big Data
Philip E. Bourne discusses the evolution of bioinformatics in the context of open science and big data, emphasizing the importance of data quality, sustainability, and the need for collaboration across various disciplines and boundaries. He outlines the transition from a fragmented research lifecycle to a more integrated digital enterprise, where data and information can be accessed and utilized more effectively. The document also highlights the role of the NIH in facilitating open data practices and advancing data science initiatives.
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Bioinformatics in the Era of Open Science and Big Data
- 1. Bioinformatics in theEra of Open Science and Big Data Philip E. Bourne University of California San Diego pbourne@ucsd.edu 1/28/14 SIB Biel/Bienne 1
- 2. My Bias • RCSBPDB/IEDB Database Developer – Views on community, quality, sustainability … • PLOS Journal Co-founder – Open Science Advocate • Associate Vice Chancellor for Innovation – Business models, interaction with the private sector, sustainability • Professor – Mentoring, reward system, value (or not) of research • Associate Director of NIH for Data Science - ?? 1/28/14 SIB Biel/Bienne 2
- 3. The History ofBioinformatics According to Bourne Searls (ed) The Roots in Bioinformatics Series PLOS Comp Biol 1980s 1990s 2000s 2010s 2020 Discipline: Unknown Expt. Driven Emergent Over-sold A Service A Partner A Driver The Raw Material: Non-existent Limited /Poor More/Ontologies Big Data/Siloed Open/Integrated The People: No name 1/28/14 Technicians Industry recognition data scientists SIB Biel/Bienne Academics 3
- 4. We Need toStart By Asking How Are We Using the Data Now! Only Then Can We Make Rational Decisions About Data – Large or Small 1/28/14 SIB Biel/Bienne 4
- 5. Web Logs etc.Are Not Enough Structure Summary page activity for H1N1 Influenza related structures Jan. 2008 Jul. 2008 Jan. 2009 Jul. 2009 Jan. 2010 Jul. 2010 3B7E: Neuraminidase of A/Brevig Mission/1/1918 H1N1 strain in complex with zanamivir 1RUZ: 1918 H1 Hemagglutinin 1/28/14 5 * http://www.cdc.gov/h1n1flu/estimates/April_March_13.htm SIB Biel/Bienne [Andreas Prlic]
- 6. We Need toLearn from Industries Whose Livelihood Addresses the Question of Use 1/28/14 SIB Biel/Bienne 6
- 7. Next Consider WhatWe Do Every Day We take actions on digital data increasingly across boundaries 1/28/14 SIB Biel/Bienne 7
- 8. Actions on DataImplies: • • • • • • • • • Insuring data quality and hence trust Making data sustainable Making data open and accessible Making data findable Providing suitable metadata and annotation Making data queryable Making data analyzable Presenting data as to maximize its value Rewarding good data practices 1/28/14 SIB Biel/Bienne 8
- 9. Actions on DataImplies: • • • • • • • • • Insuring data quality and hence trust Making data sustainable Making data open and accessible Making data findable Providing suitable metadata and annotation Making data queryable Making data analyzable Presenting data as to maximize its value Rewarding good data practices 1/28/14 SIB Biel/Bienne 9
- 10. Boundaries on DataImplies: • Working across biological scales • Working across biomedical disciplines • Working across basic and clinical research and practice • Working across institutional boundaries • Working across public and private sectors • Working across national and international borders • Working across funding agencies 1/28/14 SIB Biel/Bienne 10
- 11. Boundaries on DataImplies: • Working across biological scales • Working across biomedical disciplines • Working across basic and clinical research and practice • Working across institutional boundaries • Working across public and private sectors • Working across national and international borders • Working across funding agencies 1/28/14 SIB Biel/Bienne 11
- 12. These Issues HaveBeen Around Almost As Long As Bioinformatics The Good News is That “Big Data” Has Bought More Attention to the Problem 1/28/14 SIB Biel/Bienne 12
- 13. What Are BigData? • Large datasets from high throughput experiments • Large numbers of small datasets • Data which are “ill-formed” • The why (causality) is replaced by the what • A signal that a fundamental change is taking place – a tipping point? 1/28/14 SIB Biel/Bienne 13
- 14. That Change isEmbodied in: The Digital Enterprise • Consists of digital assets • E.g. datasets, papers, software, lab notes • Each asset is uniquely identified and has provenance, including access control • E.g. publishing simply involves changing the access control • Digital assets are interoperable across the enterprise 1/28/14 SIB Biel/Bienne 14
- 15. The Enterprise IsAlmost Anything.. Your Lab, your Institution, the SIB, the NIH…. 1/28/14 SIB Biel/Bienne 15
- 16. Consider an AcademicInstitution As A Digital Enterprise • Jane scores extremely well in parts of her graduate on-line neurology class. Neurology professors, whose research profiles are on-line and well described, are automatically notified of Jane’s potential based on a computer analysis of her scores against the background interests of the neuroscience professors. Consequently, professor Smith interviews Jane and offers her a research rotation. During the rotation she enters details of her experiments related to understanding a widespread neurodegenerative disease in an on-line laboratory notebook kept in a shared on-line research space – an institutional resource where stakeholders provide metadata, including access rights and provenance beyond that available in a commercial offering. According to Jane’s preferences, the underlying computer system may automatically bring to Jane’s attention Jack, a graduate student in the chemistry department whose notebook reveals he is working on using bacteria for purposes of toxic waste cleanup. Why the connection? They reference the same gene a number of times in their notes, which is of interest to two very different disciplines – neurology and environmental sciences. In the analog academic health center they would never have discovered each other, but thanks to the Digital Enterprise, pooled knowledge can lead to a distinct advantage. The collaboration results in the discovery of a homologous human gene product as a putative target in treating the neurodegenerative disorder. A new chemical entity is developed and patented. Accordingly, by automatically matching details of the innovation with biotech companies worldwide that might have potential interest, a licensee is found. The licensee hires Jack to continue working on the project. Jane joins Joe’s laboratory, and he hires another student using the revenue from the license. The research continues and leads to a federal grant award. The students are employed, further research is supported and in time societal benefit arises from the technology. From What Big Data Means to Me JAMIA 2014 1/28/14 SIB Biel/Bienne 16
- 17. The NIH isStarting to Think About the Digital Enterprise, Witness… bd2k.nih.gov 1/28/14 SIB Biel/Bienne 17
- 18. What Defines theDigital Enterprise • • • • • • • Trans-NIH collaboration – change culture Long-term NIH strategic planning The BD2K Initiative A “hub” of data science activities International cooperation Interagency cooperation Data sharing policies 1/28/14 SIB Biel/Bienne 18
- 19. Consider One NIHScenario • NIH-Drive – Investigator A from the NCI makes frequent reference to the over expression of genes x and y. – Investigator B from the NHLBI makes frequent reference to the under expression of genes x and y – Automatic notification of a potential common interest before publication or database deposition 1/28/14 SIB Biel/Bienne 19
- 20. The NIH Process Anexternal advisory group provided a valuable blueprint for what should be done http://acd.od.nih.gov/Data%20and%20Informatics%20Working%20Group%20Report.pdf 1/28/14 SIB Biel/Bienne 20
- 21. Blueprint Recommendations • Promotecentral and federated catalogs – Establish minimal metadata framework – Tools to facilitate data sharing – Elaborate on existing data sharing policies • Support methods and applications – Fund all phases of software development – Leverage lessons from National Centers • Training – More funding – Enhance review of training apps – Quantitative component to all awards • On campus IT strategic plan – Catalog of existing tools – Informatics laboratory – Ditto big data • Sustainable funding commitment 1/28/14 SIB Biel/Bienne acd.od.nih.gov/diwg.htm 21
- 22. General Features ofNIH Data Science • Lightweight metadata standards • Data & software registries • Expanded policies on data sharing, open source software • Training programs & reward systems • Institutional incentives • Private sector incentives • Data centers serving community needs 1/28/14 SIB Biel/Bienne 22
- 23. What is UnderWay? • Now: – – – – – Data centers (under review) Data science training grants (call Q1 14) Pilot data catalog consortium (call out) Genomic Research Data Alliance (being finalized) Piloting “NIH-drive • What Is Planned: – Extended public-private programs specifically for data science activities – Interagency activities – International exchange programs – Cold Spring Harbor-like training facilities – by-coastal? – Programs for better data descriptions – Reward institutions/communities – Policies to get clinical trial data into the public domain 1/28/14 SIB Biel/Bienne 23
- 24. The History ofBioinformatics According to PEB The Roots in Bioinformatics Series PLOS Comp Biol 1980s 1990s 2000s 2010s 2020 Discipline: Unknown Expt. Driven Emergent Over-sold A Service A Partner Driver The Raw Material: Non-existent Limited /Poor More/Ontologies Big Data/Siloed Open/Integrated The People: No name 1/28/14 Technicians Industry recognition data scientists SIB Biel/Bienne Academics 24
- 25. Why Will ScienceBecome More Open? • The public (and hence the politicians demand it) • Its the right thing to do • Its part of the modern psyche • The scholarly enterprise is broken and more stakeholders are acknowledging it 1/28/14 SIB Biel/Bienne 25
- 26. Personal Evidence • Ihave a paper with 16,000 citations that no one has ever read • I have papers in PLOS ONE that have more citations than ones in PNAS • I have data sets I am proud of but no place to put them • I “cant” reproduce work from my own lab 1/28/14 SIB Biel/Bienne 26
- 27. Politicians Demand It: G8open data charter 1/28/14 SIB Biel/Bienne http://opensource.com/government/13/7/open-data-charter-g8 27
- 28. What Are Someof the Ramifications of Open Science? 1/28/14 SIB Biel/Bienne 28
- 29. Open Science HasThe Potential to Deinstitutionalize Daniel Hulshizer/Associated Press 1/28/14 SIB Biel/Bienne 29
- 30. Open Science HasThe Potential to Deinstitutionalize Daniel Hulshizer/Associated Press 1/28/14 SIB Biel/Bienne 30
- 31. An Example ofThat Potential: The Story of Meredith http://fora.tv/2012/04/20/Congress_Unplugged_Phil_Bourne 1/28/14 SIB Biel/Bienne 31
- 32. Open Science HasThe Potential to Deinstitutionalize Daniel Hulshizer/Associated Press 1/28/14 SIB Biel/Bienne 32
- 33. Open Science HasThe Potential to Deinstitutionalize Daniel Hulshizer/Associated Press 1/28/14 SIB Biel/Bienne 33
- 34. There Still Needsto be a Reward System The Wikipedia Experiment – Topic Pages Identify areas of Wikipedia that relate to the journal that are missing of stubs Develop a Wikipedia page in the sandbox Have a Topic Page Editor Review the page Publish the copy of record with associated rewards Release the living version into Wikipedia 1/28/14 SIB Biel/Bienne 34
- 35. One Possible EndProduct of Open Science 0. Full text of PLoS papers stored in a database 4. The composite view has links to pertinent blocks of literature text and back to the PDB 4. 1. 1. A link brings up figures from the paper 2. 1/28/14 3. A composite view of journal and database content results 3. 2. Clicking the paper figure retrieves data from the PDB which is analyzedSIB Biel/Bienne 1. User clicks on thumbnail 2. Metadata and a webservices call provide a renderable image that can be annotated 3. Selecting a features provides a database/literature mashup 4. That leads to new papers PLoS Comp. Biol. 2005 1(3) e34 35
- 36. Change in theWay we Support the Research Lifecycle Authoring Tools Data Capture Lab Notebooks Software Repositories Analysis Tools Scholarly Communication Visualization IDEAS – HYPOTHESES – EXPERIMENTS – DATA - ANALYSIS - COMPREHENSION - DISSEMINATION Commercial & Public Tools DisciplineBased Metadata Standards Community Portals Git-like Resources By Discipline Data Journals New Reward Systems Training Institutional Repositories 1/28/14 SIB Biel/Bienne Commercial Repositories 36
- 37. Change in theWay we Support the Research Lifecycle Authoring Tools Data Capture Lab Notebooks Software Repositories Analysis Tools Scholarly Communication Visualization IDEAS – HYPOTHESES – EXPERIMENTS – DATA - ANALYSIS - COMPREHENSION - DISSEMINATION Commercial & Public Tools DisciplineBased Metadata Standards Community Portals Git-like Resources By Discipline Data Journals New Reward Systems Training Institutional Repositories 1/28/14 SIB Biel/Bienne Commercial Repositories 37
- 38. automate: workflows, pipeline& service integrative frameworks CS SE pool, share & collaborate web systems scientific software engineering semantics & ontologies machine readable documentation nanopub 1/28/14 [Carole Goble] SIB Biel/Bienne 38
- 39. Why is ThisImportant to Me Personally? • My wife is being treated for stage 1 breast cancer • This highlights for me the disparity between what is happening in the lab and what is happening in the clinic – In the lab cancer is a personalized and treatable condition – In the clinic we are still equally “poisoning” patients with drugs first introduced 10-20 years ago 1/28/14 SIB Biel/Bienne 39
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- 42. Most Laboratories • Weare the long tail • Goodbye to the student is goodbye to the data • Very few of us have complied (or will comply with the data management plans we write into grants) • Too much software is unusable S.Veretnik, J.L.Fink, and P.E. Bourne 2008 Computational Biology Resources Lack Persistence and Usability. PLoS Comp. Biol. . 4(7): e1000136 1/28/14 SIB Biel/Bienne 42
- 43. Today’s Research Lifecycleis Digitally Fragmented at Best • Proof: – I cant immediately reproduce the research in my own laboratory • It took an estimated 280 hours for an average user to approximately reproduce the paper – Workflows are maturing and becoming helpful – Data and software versions and accessibility prevent exact reproducability Daniel Garijo et al. 2013 Quantifying Reproducibility in Computational Biology: The Case of the Tuberculosis Drugome PLOS ONE 8(11) e80278 . 1/28/14 SIB Biel/Bienne 43
- 44. We Have SomeReally Big Problems to Solve – The Commons Can Help 1/28/14 SIB Biel/Bienne 44
- 45. What Really HappensWhen You Take a Drug? • Can we predict drug efficacy and toxicity? • Can we reuse old drugs? • Can we design personalized medicines? 1/28/14 SIB Biel/Bienne 45
- 46. One Drug, OneGene, One Disease Bernard M. Nat Rev Drug Disc 8(2009), 959-968 1/28/14 SIB Biel/Bienne 46
- 47. Polypharmacology • Tykerb –Breast cancer • Gleevac – Leukemia, GI cancers • Nexavar – Kidney and liver cancer • Staurosporine – natural product – alkaloid – uses many e.g., antifungal antihypertensive Collins and Workman 2006 Nature Chemical Biology 2 689-700 1/28/14 SIB Biel/Bienne 47
- 48. Polypharmacology is NotRare but Common • Single gene knockouts only affect phenotype in 10-20% of cases A.L. Hopkins Nat. Chem. Biol. 2008 4:682-690 • 35% of biologically active compounds bind to two or more targets that do not have similar sequences or global shapes Paolini et al. Nat. Biotechnol. 2006 24:805–815 Predict side effects Repurpose drugs Kaiser et al. Nature 462 (2009) 175-81 1/28/14 SIB Biel/Bienne 48
- 49. Drug Binding isDynamic • Drug effect dependents on not only how strong (binding affinity) but also how long the drug is “stuck” in the protein (residence time). • Molecular Dynamics (MD) simulation is powerful but computationally intensive. ~ns 1 day simulation ~ms – hours >106 days D. Huang et al. (2011), PLoS Comp Biol 7(2):e1002002 1/28/14 SIB Biel/Bienne 49
- 50. Systems Pharmacology Systemic response Uptake Enzyme inhibition × ×× × × × Catalytic site Affect protein function × Secretion (orbiomass components) Metabolic network Target binding 1/28/14 Slide from Roger Chang SIB Biel/Bienne Drug molecules 50
- 51. Multiscale Modeling ofDrug Actions Understanding of dynamics and kinetics of proteinligand interactions Traditional Approach Knowledge representation and discovery & model integration 1/28/14 Slide from Lei Xie Prediction of molecular interaction network on a genome scale physiological process Systems-based Approach SIB Biel/Bienne Reconstruction, analysis and simulation of biological networks 51
- 52. More Generally AnyTranslationalbased Research That Involves Modeling at Multiple Scales http://sagebase.org/ 1/28/14 SIB Biel/Bienne 52
- 53. The History ofBioinformatics According to Bourne The Roots in Bioinformatics Series PLOS Comp Biol 1980s 1990s 2000s 2010s 2020 Discipline: Unknown Expt. Driven Emergent Over-sold A Service A Partner A Driver The Raw Material: Non-existent Limited /Poor More/Ontologies Big Data/Siloed Open/Integrated The People: No name 1/28/14 Technicians Industry recognition data scientists SIB Biel/Bienne Academics 53
- 54. In Summary: By theEnd of the Decade Biomedical Research will Be a Truly Digital Enterprise and Computational Scientists Will Be At the Forefront You Have Much to Look Forward Too 1/28/14 SIB Biel/Bienne 54