AIDS CLINICAL ROUNDSThe UC San Diego AntiViral Research Center sponsors weeklypresentations by infectious disease clinicia...
Open Source Software Solutions forClinical Research: Applications forHIV ResearchJason A. Young, Ph.DAssistant ProfessorDe...
UCSD CFAR BIT Core    Bioinformatics and Information Technologies (BIT) CoreAims• Provide bio/informatics expertise• To be...
Outline  1. Web and Mobile Research Services  2. Clinical Data Management  3. Bioinformatics Expertise
Outline  1. Web and Mobile Research Services  2. Clinical Data Management  3. Bioinformatics Expertise
Web and Mobile Research Services                                                  “A powerful, flexible open source        ...
Web and Mobile Research Services                       ARI - ari.ucsd.edu                       CFAR - cfar.ucsd.edu      ...
Web and Mobile Research Services                       ARI - ari.ucsd.edu                       CFAR - cfar.ucsd.edu      ...
Web and Mobile Research Services                       ARI - ari.ucsd.edu                       CFAR - cfar.ucsd.edu      ...
Web and Mobile Research Services                                            ARI - ari.ucsd.edu                            ...
Web and Mobile Research Services                       ARI - ari.ucsd.edu                       CFAR - cfar.ucsd.edu      ...
Web and Mobile Research Services                                       ARI - ari.ucsd.edu                                 ...
Web and Mobile Research ServicesHIVe: HIV e-resource (hive.ucsd.edu)           ARI - ari.ucsd.edu•   Acute and Early HIV (...
Web and Mobile Research ServicesHIVe: HIV e-resource (hive.ucsd.edu)           ARI - ari.ucsd.edu•   Acute and Early HIV (...
Web and Mobile Research ServicesSan Diego Primary Infection Cohort (SDPIC)                                              AR...
Web and Mobile Research Services                         ARI - ari.ucsd.edu                         CFAR - cfar.ucsd.edu  ...
Web and Mobile Research Services                                              ARI - ari.ucsd.edu                          ...
Web and Mobile Research Services                       ARI - ari.ucsd.edu                       CFAR - cfar.ucsd.edu      ...
Web and Mobile Research Services                                              ARI - ari.ucsd.edu                          ...
Web and Mobile Research Services                       ARI - ari.ucsd.edu                       CFAR - cfar.ucsd.edu      ...
Web and Mobile Research Services                       ARI - ari.ucsd.edu                       CFAR - cfar.ucsd.edu      ...
Web and Mobile Research Services                                                 ARI - ari.ucsd.edu                       ...
Web and Mobile Research Services iFormBuilder iOS Mobile DataCollection Platform
Web and Mobile Research Services iFormBuilder iOS Mobile Data      FunctionalityCollection Platform   •   Runs on all iOS ...
Web and Mobile Research Services iFormBuilder iOS Mobile Data      FunctionalityCollection Platform   •   Runs on all iOS ...
Outline  1. Web and Mobile Research Services  2. Clinical Data Management  3. Bioinformatics Expertise
Clinical Data ManagementOpen source Clinical Content Analysis and Management System OCCAMS: Designed to handle all aspects...
Clinical Data ManagementOpen source Clinical Content Analysis and Management System OCCAMS: Designed to handle all aspects...
Clinical Data ManagementOpen source Clinical Content Analysis and Management System OCCAMS: Designed to handle all aspects...
Clinical Data ManagementOpen source Clinical Content Analysis and Management System OCCAMS: Designed to handle all aspects...
Clinical Data ManagementSDPIC pre-OCCAMS workflow...1. Nurse sees patient, completes    3. CRF and source documentation con...
Clinical Data ManagementSDPIC pre-OCCAMS workflow...             Challenges                              1. Rooms full of p...
Clinical Data ManagementSDPIC pre-OCCAMS workflow...             Challenges                              1. Rooms full of p...
Clinical Data ManagementSDPIC pre-OCCAMS workflow...             Challenges                              1. Rooms full of p...
Clinical Data ManagementSDPIC pre-OCCAMS workflow...             Challenges                              1. Rooms full of p...
Clinical Data ManagementSDPIC pre-OCCAMS workflow...             Challenges                              1. Rooms full of p...
Clinical Data ManagementSDPIC pre-OCCAMS workflow...             Challenges                              1. Rooms full of p...
Clinical Data ManagementSDPIC pre-OCCAMS workflow...             Challenges                              1. Rooms full of p...
Clinical Data ManagementSDPIC post-OCCAMS workflow...1. Nurse sees patient, completes          3. Workflow notifies AVRC staf...
Clinical Data Management                                  Challenges                      1. Rooms full of paper and binde...
Clinical Data Management                                  Challenges                      1. Rooms full of paper and binde...
Clinical Data Management                                  Challenges                      1. Rooms full of paper and binde...
Clinical Data Management                                  Challenges                      1. Rooms full of paper and binde...
Clinical Data Management                                  Challenges                      1. Rooms full of paper and binde...
Clinical Data Management                                  Challenges                      1. Rooms full of paper and binde...
Clinical Data Management            OCCAMS Modular Development        Core    occams.clinical   occams.datastore     occam...
Clinical Data Management            OCCAMS Modular Development        Core    occams.clinical   occams.datastore     occam...
Clinical Data Management            OCCAMS Modular Development        Core           •   EAV Database Structure           ...
Clinical Data Management         Form Versioning with OCCAMS
Clinical Data Management         Form Versioning with OCCAMS
Clinical Data Management         Form Versioning with OCCAMS                  Version 1                 1/2010 - ...      ...
Clinical Data Management         Form Versioning with OCCAMS                  Version 1      Version 2                 1/2...
Clinical Data Management         Form Versioning with OCCAMS                  Version 1      Version 2      Version 3     ...
Clinical Data Management         Form Versioning with OCCAMS                  Version 1            Version 2       Version...
Clinical Data Management         Form Versioning with OCCAMS                  Version 1           Version 2        Version...
Clinical Data Management         Form Versioning with OCCAMS                  Version 1           Version 2         Versio...
Clinical Data Management            OCCAMS Modular Development        Core                        Add-ons    occams.clinic...
Clinical Data Management                    OCCAMS Modular Development               Core                          Add-ons...
Outline  1. Web and Mobile Research Services  2. Clinical Data Management  3. Bioinformatics Expertise
Bioinformatics Expertise HyPhy (hyphy.org) A molecular evolution and statistical sequence analysis software package • Posi...
Bioinformatics Expertise HyPhy (hyphy.org) A molecular evolution and statistical sequence analysis software package • Posi...
Bioinformatics Expertise                  Network Analysis         Example: SDPIC Transmission Network     Screening      ...
Bioinformatics Expertise                     SDPIC Transmission Network   Epidemological Link                   Phylogenet...
Bioinformatics Expertise                     SDPIC Transmission Network   Epidemological Link                   Phylogenet...
Bioinformatics Expertise                      SDPIC Transmission Network   Epidemological Link                            ...
Bioinformatics Expertise                     SDPIC Transmission Network   Epidemological Link                   Phylogenet...
Bioinformatics Expertise                      SDPIC Transmission Network   Epidemological Link                            ...
Bioinformatics Expertise                     SDPIC Transmission Network   Epidemological Link                       Phylog...
Bioinformatics Expertise  1. How effective is PCRS in an AEH setting?                                            Number Ne...
Bioinformatics Expertise  1. How effective is PCRS in an AEH setting?                                            Number Ne...
Bioinformatics Expertise  1. How effective is PCRS in an AEH setting?                                            Number Ne...
Bioinformatics Expertise  1. How effective is PCRS in an AEH setting?                                            Number Ne...
Bioinformatics Expertise  2. What is the structure of the SDPIC transmission network?                                     ...
Bioinformatics Expertise  3. Do HIV(+) reported partners represent actual transmission links?                             ...
Bioinformatics Expertise                   Machine LearningExample: HIV bnAb epitope and bnAb resistance prediction       ...
Bioinformatics Expertise                           Machine Learning Example: HIV bnAb epitope and bnAb resistance predicti...
Bioinformatics Expertise                           Machine Learning Example: HIV bnAb epitope and bnAb resistance predicti...
Bioinformatics Expertise                           Machine Learning Example: HIV bnAb epitope and bnAb resistance predicti...
Bioinformatics Expertise      HIV bnAb epitope and bnAb resistance prediction                                             ...
Bioinformatics Expertise      HIV bnAb epitope and bnAb resistance prediction                                             ...
Bioinformatics Expertise      HIV bnAb epitope and bnAb resistance prediction                                             ...
Bioinformatics Expertise      HIV bnAb epitope and bnAb resistance prediction                                             ...
Acknowledgements          BIT Core                               CFARSergei Pond                        Doug RichmanDave M...
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Open Source Software Solutions for Clinical Research: Applications for HIV Research

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Jason A. Young, PhD of the UC San Diego AntiViral Research Center (AVRC) presents "Open Source Software Solutions for Clinical Research: Applications for HIV Research."

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Open Source Software Solutions for Clinical Research: Applications for HIV Research

  1. 1. AIDS CLINICAL ROUNDSThe UC San Diego AntiViral Research Center sponsors weeklypresentations by infectious disease clinicians, physicians andresearchers. The goal of these presentations is to provide the mostcurrent research, clinical practices and trends in HIV, HBV, HCV, TBand other infectious diseases of global significance.The slides from the AIDS Clinical Rounds presentation that you areabout to view are intended for the educational purposes of ouraudience. They may not be used for other purposes without thepresenter’s express permission.
  2. 2. Open Source Software Solutions forClinical Research: Applications forHIV ResearchJason A. Young, Ph.DAssistant ProfessorDepartment of Medicine, UCSD AIDS Clinical Rounds - 8.3.12
  3. 3. UCSD CFAR BIT Core Bioinformatics and Information Technologies (BIT) CoreAims• Provide bio/informatics expertise• To be agile, interactive, affordable• Committed to open sourceResources• The BIT Core team!• 24/7, secure web & data servers• 500+ node compute cluster• A collection of open sourcesoftware solutions and services Website: https://cfar.ucsd.edu/bit E-mail: bitcore@ucsd.edu GitHub: http://github.com/beastcoreClients• Center For AIDS Research (CFAR) • IAVI Neutralizing Antibody Consortium (IAVI-NAC)• AntiViral Research Center (AVRC) • California Collaborative Treatment Group (CCTG)• AIDS Research Institute (ARI) • ... other research investigators and growing ...
  4. 4. Outline 1. Web and Mobile Research Services 2. Clinical Data Management 3. Bioinformatics Expertise
  5. 5. Outline 1. Web and Mobile Research Services 2. Clinical Data Management 3. Bioinformatics Expertise
  6. 6. Web and Mobile Research Services “A powerful, flexible open source Content Management System”Accessible:Non-technical users can create websites and maintain contentusing only a web browser. Comes with built-in workflows,permissions, etc.Widely deployed:Broad user base includes NASA, Nokia, Novell, and majoruniversities (Harvard, MIT and Penn State).Large development and support base:340 core developers and >300 solution providers in 57countries.Mature:First released in 2001. Provides developers a robust frameworkfor custom product development.Secure:Best security record of any major CMS.Extensible:Over 1900 projects extending core functionality currentlyavailable.
  7. 7. Web and Mobile Research Services ARI - ari.ucsd.edu CFAR - cfar.ucsd.edu AVRC - avrc.ucsd.edu HIVe - hive.ucsd.edu ET - theearlytest.ucsd.edu LTW - leadthewaysd.com Doug Richman (UCSD)
  8. 8. Web and Mobile Research Services ARI - ari.ucsd.edu CFAR - cfar.ucsd.edu AVRC - avrc.ucsd.edu HIVe - hive.ucsd.edu ET - theearlytest.ucsd.edu LTW - leadthewaysd.com Doug Richman (UCSD)
  9. 9. Web and Mobile Research Services ARI - ari.ucsd.edu CFAR - cfar.ucsd.edu AVRC - avrc.ucsd.edu HIVe - hive.ucsd.edu ET - theearlytest.ucsd.edu LTW - leadthewaysd.com Doug Richman (UCSD)
  10. 10. Web and Mobile Research Services ARI - ari.ucsd.edu CFAR - cfar.ucsd.edu AVRC - avrc.ucsd.edu HIVe - hive.ucsd.edu ET - theearlytest.ucsd.edu LTW - leadthewaysd.com• Core Service Request Forms• Feedback Forms• Developmental Grant Submission System• Laboratory Experiment Tracking System• Retroviral Seminar Series Calendar Doug Richman (UCSD)
  11. 11. Web and Mobile Research Services ARI - ari.ucsd.edu CFAR - cfar.ucsd.edu AVRC - avrc.ucsd.edu HIVe - hive.ucsd.edu ET - theearlytest.ucsd.edu LTW - leadthewaysd.com Constance Benson (UCSD)
  12. 12. Web and Mobile Research Services ARI - ari.ucsd.edu CFAR - cfar.ucsd.edu AVRC - avrc.ucsd.edu HIVe - hive.ucsd.edu ET - theearlytest.ucsd.edu LTW - leadthewaysd.com• Clinical trial information• Events calendar• AIDS rounds presentation slides* Constance Benson (UCSD)
  13. 13. Web and Mobile Research ServicesHIVe: HIV e-resource (hive.ucsd.edu) ARI - ari.ucsd.edu• Acute and Early HIV (AEH) cohort network CFAR - cfar.ucsd.edu• Data standardization and sharing AVRC - avrc.ucsd.edu• 3 active & 7 legacy AEH sites HIVe - hive.ucsd.edu ET - theearlytest.ucsd.edu LTW - leadthewaysd.com UCSD UCSF MGH AIED AIEDRP AIEDRP AIEDRP AIEDRP AIEDRP AIEDRP
  14. 14. Web and Mobile Research ServicesHIVe: HIV e-resource (hive.ucsd.edu) ARI - ari.ucsd.edu• Acute and Early HIV (AEH) cohort network CFAR - cfar.ucsd.edu• Data standardization and sharing AVRC - avrc.ucsd.edu• 3 active & 7 legacy AEH sites HIVe - hive.ucsd.edu ET - theearlytest.ucsd.edu hive.ucsd.edu LTW - leadthewaysd.com ucsd.hive.ucsd.edu UCSD UCSF MGH AIED ucsf.hive.ucsd.edu AIEDRP AIEDRP AIEDRP AIEDRP mgh.hive.ucsd.edu AIEDRP AIEDRP
  15. 15. Web and Mobile Research ServicesSan Diego Primary Infection Cohort (SDPIC) ARI - ari.ucsd.edu(1996 - Present) CFAR - cfar.ucsd.edu• 2 Screening Programs (~12k screens) AVRC - avrc.ucsd.edu• 7 Research Studies (~2.5k enrollments) HIVe - hive.ucsd.edu• Data: Demographics, risk factors, partner ET - theearlytest.ucsd.eduinformation, labs, viral sequences, and muchmore... LTW - leadthewaysd.com• Specimen: Over 200k UCSD UCSF MGH AIED ucsd.hive.ucsd.edu AIEDRP AIEDRP AIEDRP AIEDRPSusan Little (UCSD) AIEDRP AIEDRP
  16. 16. Web and Mobile Research Services ARI - ari.ucsd.edu CFAR - cfar.ucsd.edu AVRC - avrc.ucsd.edu HIVe - hive.ucsd.edu ET - theearlytest.ucsd.edu LTW - leadthewaysd.com Susan Little & Davey Smith (UCSD)
  17. 17. Web and Mobile Research Services ARI - ari.ucsd.edu CFAR - cfar.ucsd.edu AVRC - avrc.ucsd.edu HIVe - hive.ucsd.edu ET - theearlytest.ucsd.edu LTW - leadthewaysd.com• AEH screening program (Rapid/NAT)• Obtain NAT results online or over the phone in two weeks Susan Little & Davey Smith (UCSD)
  18. 18. Web and Mobile Research Services ARI - ari.ucsd.edu CFAR - cfar.ucsd.edu AVRC - avrc.ucsd.edu HIVe - hive.ucsd.edu ET - theearlytest.ucsd.edu LTW - leadthewaysd.com Susan Little (UCSD)
  19. 19. Web and Mobile Research Services ARI - ari.ucsd.edu CFAR - cfar.ucsd.edu AVRC - avrc.ucsd.edu HIVe - hive.ucsd.edu ET - theearlytest.ucsd.edu LTW - leadthewaysd.com• Hyper-local (92103/92104) HIV testing campaign (Rapid/NAT)• Public media advertising campaign• Storefront & door-to-door testing• Goal: What are the barriers to testing? Susan Little (UCSD)
  20. 20. Web and Mobile Research Services ARI - ari.ucsd.edu CFAR - cfar.ucsd.edu AVRC - avrc.ucsd.edu HIVe - hive.ucsd.edu ET - theearlytest.ucsd.edu LTW - leadthewaysd.com Susan Little (UCSD)
  21. 21. Web and Mobile Research Services ARI - ari.ucsd.edu CFAR - cfar.ucsd.edu AVRC - avrc.ucsd.edu HIVe - hive.ucsd.edu ET - theearlytest.ucsd.edu LTW - leadthewaysd.com Susan Little (UCSD)
  22. 22. Web and Mobile Research Services ARI - ari.ucsd.edu CFAR - cfar.ucsd.edu AVRC - avrc.ucsd.edu HIVe - hive.ucsd.edu ET - theearlytest.ucsd.edu LTW - leadthewaysd.com• Hyper-local (92103/92104) HIV testing campaign (Rapid/NAT)• Public media advertising campaign• Storefront & door-to-door testing• Aim: What are the barriers to HIV testing? Susan Little (UCSD)
  23. 23. Web and Mobile Research Services iFormBuilder iOS Mobile DataCollection Platform
  24. 24. Web and Mobile Research Services iFormBuilder iOS Mobile Data FunctionalityCollection Platform • Runs on all iOS devices • 25+ field widgets • Flexible skip logic • GPS functionality • HIPAA compliant • Encrypted data upload to cloud
  25. 25. Web and Mobile Research Services iFormBuilder iOS Mobile Data FunctionalityCollection Platform • Runs on all iOS devices • 25+ field widgets • Flexible skip logic • GPS functionality • HIPAA compliant • Encrypted data upload to cloud One year for LTW... • 1317 iPad administered surveys • 1062 individuals tested for HIV • 24 newly diagnosed HIV(+) cases
  26. 26. Outline 1. Web and Mobile Research Services 2. Clinical Data Management 3. Bioinformatics Expertise
  27. 27. Clinical Data ManagementOpen source Clinical Content Analysis and Management System OCCAMS: Designed to handle all aspects of complex and evolving clinical research studies William of Ockham (1288-1347)
  28. 28. Clinical Data ManagementOpen source Clinical Content Analysis and Management System OCCAMS: Designed to handle all aspects of complex and evolving clinical research studies June 1996 ~15 years April 2010SDPIC Infection Numerous data management OCCAMSCohort Begins solutions and providers development begins William of Ockham (1288-1347)
  29. 29. Clinical Data ManagementOpen source Clinical Content Analysis and Management System OCCAMS: Designed to handle all aspects of complex and evolving clinical research studies June 1996 ~15 years April 2010SDPIC Infection Numerous data management OCCAMSCohort Begins solutions and providers development begins 1. Web-accessible (eCRFs) 2. Patient centric (no data duplication) 3. Broad data support 4. Integrated specimen handling 5. QA workflows and auditing reports 6. PHI-compliant with granular permissions 7. Modular, flexible and extensible William of Ockham (1288-1347) 8. Open source (Plone/Python-powered)
  30. 30. Clinical Data ManagementOpen source Clinical Content Analysis and Management System OCCAMS: Designed to handle all aspects of complex and evolving clinical research studies June 1996 ~15 years April 2010 September 2010 PresentSDPIC Infection Numerous data management OCCAMS Alpha version +2.5k AEH enrollmentsCohort Begins solutions and providers development begins launched for SDPIC +12k AEH screens 1. Web-accessible (eCRFs) 2. Patient centric (no data duplication) 3. Broad data support 4. Integrated specimen handling 5. QA workflows and auditing reports 6. PHI-compliant with granular permissions 7. Modular, flexible and extensible William of Ockham (1288-1347) 8. Open source (Plone/Python-powered)
  31. 31. Clinical Data ManagementSDPIC pre-OCCAMS workflow...1. Nurse sees patient, completes 3. CRF and source documentation consistency checked source docs by AVRC staff 2. Nurse completes case report form (CRF) 4. CRF entered into database by students
  32. 32. Clinical Data ManagementSDPIC pre-OCCAMS workflow... Challenges 1. Rooms full of paper and binders. 2. Lag time between source doc, CRF, and CRF database entry completion make reporting difficult and incomplete. 3. Several opportunities for transcription errors. 4. No QC after students entered CRF to database. 5. Data duplication in cases where patients on multiple studies. 6. CRF changes not automatically reflected in database. 7. Arduous auditing process.
  33. 33. Clinical Data ManagementSDPIC pre-OCCAMS workflow... Challenges 1. Rooms full of paper and binders. 2. Lag time between source doc, CRF, and CRF database entry completion make reporting difficult and incomplete. 3. Several opportunities for transcription errors. 4. No QC after students entered CRF to database. 5. Data duplication in cases where patients on multiple studies. 6. CRF changes not automatically reflected in database. 7. Arduous auditing process.
  34. 34. Clinical Data ManagementSDPIC pre-OCCAMS workflow... Challenges 1. Rooms full of paper and binders. 2. Lag time between source doc, CRF, and CRF database entry completion make reporting difficult and incomplete. 3. Several opportunities for transcription errors. 4. No QC after students entered CRF to database. 5. Data duplication in cases where patients on multiple studies. 6. CRF changes not automatically reflected in database. 7. Arduous auditing process.
  35. 35. Clinical Data ManagementSDPIC pre-OCCAMS workflow... Challenges 1. Rooms full of paper and binders. 2. Lag time between source doc, CRF, and CRF database entry completion make reporting difficult and incomplete. 3. Several opportunities for transcription errors. 4. No QC after students entered CRF to database. 5. Data duplication in cases where patients on multiple studies. 6. CRF changes not automatically reflected in database. 7. Arduous auditing process.
  36. 36. Clinical Data ManagementSDPIC pre-OCCAMS workflow... Challenges 1. Rooms full of paper and binders. 2. Lag time between source doc, CRF, and CRF database entry completion make reporting difficult and incomplete. 3. Several opportunities for transcription errors. 4. No QC after students entered CRF to database. 5. Data duplication in cases where patients on multiple studies. 6. CRF changes not automatically reflected in database. 7. Arduous auditing process.
  37. 37. Clinical Data ManagementSDPIC pre-OCCAMS workflow... Challenges 1. Rooms full of paper and binders. 2. Lag time between source doc, CRF, and CRF database entry completion make reporting difficult and incomplete. 3. Several opportunities for transcription errors. 4. No QC after students entered CRF to database. 5. Data duplication in cases where patients on multiple studies. 6. CRF changes not automatically reflected in database. 7. Arduous auditing process.
  38. 38. Clinical Data ManagementSDPIC pre-OCCAMS workflow... Challenges 1. Rooms full of paper and binders. 2. Lag time between source doc, CRF, and CRF database entry completion make reporting difficult and incomplete. 3. Several opportunities for transcription errors. 4. No QC after students entered CRF to database. 5. Data duplication in cases where patients on multiple studies. 6. CRF changes not automatically reflected in database. 7. Arduous auditing process.
  39. 39. Clinical Data ManagementSDPIC post-OCCAMS workflow...1. Nurse sees patient, completes 3. Workflow notifies AVRC staff eCRF ready for QC source docs 2. Nurse direct enters data via eCRF that is automatically generated based 4. High volume eCRFs entered by on study and visit week students
  40. 40. Clinical Data Management Challenges 1. Rooms full of paper and binders. 2. Lag time between source doc, CRF, and CRF database entry completion make reporting difficult and incomplete. 3. Several opportunities for transcription errors. 4. No QC after students entered CRF to database. 5. Data duplication in cases where patients on multiple studies. 6. CRF changes not automatically reflected in database. 7. Arduous auditing process.
  41. 41. Clinical Data Management Challenges 1. Rooms full of paper and binders. 2. Lag time between source doc, CRF, and CRF database entry completion make reporting difficult and incomplete. 3. Several opportunities for transcription errors. 4. No QC after students entered CRF to database. 5. Data duplication in cases where patients on multiple studies. 6. CRF changes not automatically reflected in database. 7. Arduous auditing process.
  42. 42. Clinical Data Management Challenges 1. Rooms full of paper and binders. 2. Lag time between source doc, CRF, and CRF database entry completion make reporting difficult and incomplete. 3. Several opportunities for transcription errors. 4. No QC after students entered CRF to database. 5. Data duplication in cases where patients on multiple studies. 6. CRF changes not automatically reflected in database. 7. Arduous auditing process.
  43. 43. Clinical Data Management Challenges 1. Rooms full of paper and binders. 2. Lag time between source doc, CRF, and CRF database entry completion make reporting difficult and incomplete. 3. Several opportunities for transcription errors. 4. No QC after students entered CRF to database. 5. Data duplication in cases where patients on multiple studies. 6. CRF changes not automatically reflected in database. 7. Arduous auditing process.
  44. 44. Clinical Data Management Challenges 1. Rooms full of paper and binders. 2. Lag time between source doc, CRF, and CRF database entry completion make reporting difficult and incomplete. 3. Several opportunities for transcription errors. 4. No QC after students entered CRF to database. 5. Data duplication in cases where patients on multiple studies. 6. CRF changes not automatically reflected in database. 7. Arduous auditing process.
  45. 45. Clinical Data Management Challenges 1. Rooms full of paper and binders. 2. Lag time between source doc, CRF, and CRF database entry completion make reporting difficult and incomplete. 3. Several opportunities for transcription errors. 4. No QC after students entered CRF to database. 5. Data duplication in cases where patients on multiple studies. 6. CRF changes not automatically reflected in database. 7. Arduous auditing process.
  46. 46. Clinical Data Management OCCAMS Modular Development Core occams.clinical occams.datastore occams.form occams.export occams.import
  47. 47. Clinical Data Management OCCAMS Modular Development Core occams.clinical occams.datastore occams.form occams.export occams.import
  48. 48. Clinical Data Management OCCAMS Modular Development Core • EAV Database Structure ~60 SQL tables total occams.clinical • Robust versioning support occams.datastore Data versioning (audit trail) Form versioning (revision history) occams.form occams.export occams.import
  49. 49. Clinical Data Management Form Versioning with OCCAMS
  50. 50. Clinical Data Management Form Versioning with OCCAMS
  51. 51. Clinical Data Management Form Versioning with OCCAMS Version 1 1/2010 - ... A= B= C=
  52. 52. Clinical Data Management Form Versioning with OCCAMS Version 1 Version 2 1/2010 - ... 7/2010 - ... A= A= B= B= C= D=
  53. 53. Clinical Data Management Form Versioning with OCCAMS Version 1 Version 2 Version 3 1/2010 - ... 7/2010 - ... 10/2010 - ... A= A= A= B= B= B= C= D= E=
  54. 54. Clinical Data Management Form Versioning with OCCAMS Version 1 Version 2 Version 3 1/2010 - ... 7/2010 - ... 10/2010 - ... A= A= A= B= B= B= C= D= E= Which Form to Use? Example. Visit on 8/2010
  55. 55. Clinical Data Management Form Versioning with OCCAMS Version 1 Version 2 Version 3 1/2010 - ... 7/2010 - Retract 10/2010 - ... A= A= A= B= B= B= C= D= E= Which Form to Use? Example. Visit on 8/2010
  56. 56. Clinical Data Management Form Versioning with OCCAMS Version 1 Version 2 Version 3 1/2010 - ... 7/2010 - Retract 10/2010 - ... A= A= A= B= B= B= C= D= E= • Multiple versions of a form can exist simultaneously • The correct form for a visit date is auto-presented • Draft forms can be created and edited concurrently
  57. 57. Clinical Data Management OCCAMS Modular Development Core Add-ons occams.clinical occams.lab occams.datastore occams.sequence occams.form occams.symptom occams.export occams.drug occams.import occams.partner occams.edi occams.transmission
  58. 58. Clinical Data Management OCCAMS Modular Development Core Add-ons occams.clinical occams.lab occams.datastore occams.sequence occams.form occams.symptom occams.export occams.drug occams.import occams.partner • Currently undergoing finalization of occams.edi remaining core features and testing •Public Beta release aimed for first occams.transmission half of 2013
  59. 59. Outline 1. Web and Mobile Research Services 2. Clinical Data Management 3. Bioinformatics Expertise
  60. 60. Bioinformatics Expertise HyPhy (hyphy.org) A molecular evolution and statistical sequence analysis software package • Positive/Negative selection detection • Recombination analysis • Nucleotide, protein and codon model selection Some of the most popular functions are implemented in a webserver hosted at datamonkey.org Galaxy (galaxy.psu.edu) A web-based, scalable, framework for genomic tools, data integration, and reproducible analyses. • Filter sequences obtained from public databases by specific traits, i.e. find exons with the greatest number of SNPs. • Deep sequencing analysis tools (read mapping, chip-SEQ, metagenomic taxonomic breakdowns). Custom Bioinformatics Services Examples... • Sequence analysis (traditional and NGS) • Network analysis • Machine Learning
  61. 61. Bioinformatics Expertise HyPhy (hyphy.org) A molecular evolution and statistical sequence analysis software package • Positive/Negative selection detection • Recombination analysis • Nucleotide, protein and codon model selection Some of the most popular functions are implemented in a webserver hosted at datamonkey.org Galaxy (galaxy.psu.edu) A web-based, scalable, framework for genomic tools, data integration, and reproducible analyses. • Filter sequences obtained from public databases by specific traits, i.e. find exons with the greatest number of SNPs. • Deep sequencing analysis tools (read mapping, chip-SEQ, metagenomic taxonomic breakdowns). Custom Bioinformatics Services Examples... • Sequence analysis (traditional and NGS) • Network analysis • Machine Learning
  62. 62. Bioinformatics Expertise Network Analysis Example: SDPIC Transmission Network Screening Observational Programs Studies AEH ET Study NAT/Rapid Testing AEH Partner Counseling & Infection Referral NAT(+)/Rapid(-) Services <70 EDI (PCRS) LTW NAT/Rapid Partner Testing Study
  63. 63. Bioinformatics Expertise SDPIC Transmission Network Epidemological Link Phylogenetic Link Partner Counseling and Referral Genetic distance between Services (PCRS) results in index to HIV pol sequences isolated from partner linkage being identified any two individuals (both persons enrolled on study) is <= 1%
  64. 64. Bioinformatics Expertise SDPIC Transmission Network Epidemological Link Phylogenetic Link Partner Counseling and Referral Genetic distance between Services (PCRS) results in index to HIV pol sequences isolated from partner linkage being identified any two individuals (both persons enrolled on study) is <= 1%
  65. 65. Bioinformatics Expertise SDPIC Transmission Network Epidemological Link Phylogenetic Link Partner Counseling and Referral Genetic distance between Services (PCRS) results in index to HIV pol sequences isolated from partner linkage being identified any two individuals (both persons enrolled on study) is <= 1%AEH Study AEH Partner Study HIV (-) AEH Chronic “Epilinks”
  66. 66. Bioinformatics Expertise SDPIC Transmission Network Epidemological Link Phylogenetic Link Partner Counseling and Referral Genetic distance between Services (PCRS) results in index to HIV pol sequences isolated from partner linkage being identified any two individuals (both persons enrolled on study) is <= 1%
  67. 67. Bioinformatics Expertise SDPIC Transmission Network Epidemological Link Phylogenetic Link Partner Counseling and Referral Genetic distance between Services (PCRS) results in index to HIV pol sequences isolated from partner linkage being identified any two individuals (both persons enrolled on study) is <= 1%AEH Study AEH Partner Study HIV (-) AEH Chronic “Phylolinks”
  68. 68. Bioinformatics Expertise SDPIC Transmission Network Epidemological Link Phylogenetic Link Partner Counseling and Referral Genetic distance between Services (PCRS) results in index to HIV pol sequences isolated from partner linkage being identified any two individuals (both persons enrolled on study) is <= 1% 1. How effective is PCRS in an AEH setting? 2. What is the structure of the SDPIC transmission network? 3. Do HIV(+) reported partners represent likely transmission links?
  69. 69. Bioinformatics Expertise 1. How effective is PCRS in an AEH setting? Number Needed To Interview Previous PCRS studies report... NNTI: 11-15 A single AEH PCRS study reports... NNTI: 25 (25/1) SDPIC... NNTI: 5.9 Sheldon Morris & Susan Little
  70. 70. Bioinformatics Expertise 1. How effective is PCRS in an AEH setting? Number Needed To Interview Previous PCRS studies report... NNTI: 11-15 A single AEH PCRS study reports... NNTI: 25 (25/1) SDPIC... NNTI: 5.9 Sheldon Morris & Susan Little
  71. 71. Bioinformatics Expertise 1. How effective is PCRS in an AEH setting? Number Needed To Interview Previous PCRS studies report... NNTI: 11-15 A single AEH PCRS study reports... NNTI: 25 (25/1) SDPIC... NNTI: 5.9 Sheldon Morris & Susan Little
  72. 72. Bioinformatics Expertise 1. How effective is PCRS in an AEH setting? Number Needed To Interview Previous PCRS studies report... NNTI: 11-15 A single AEH PCRS study reports... NNTI: 25 (25/1) SDPIC... NNTI: 5.9 Sheldon Morris & Susan Little
  73. 73. Bioinformatics Expertise 2. What is the structure of the SDPIC transmission network? Number Needed To Interview Previous PCRS studies report... NNTI: 11-15 A single AEH PCRS study reports... NNTI: 25 (25/1) SDPIC... NNTI: 5.9 Scale-free network structure • Highly-connected nodes critical Sheldon Morris & Susan Little
  74. 74. Bioinformatics Expertise 3. Do HIV(+) reported partners represent actual transmission links? Number Needed To Interview Previous PCRS studies report... NNTI: 11-15 A single AEH PCRS study reports... NNTI: 25 (25/1) SDPIC... NNTI: 5.9 Scale-free network structure • Highly-connected nodes critical Only ~34% of seroconcordant epi- linked pairs are also phylo-linked Sheldon Morris & Susan Little
  75. 75. Bioinformatics Expertise Machine LearningExample: HIV bnAb epitope and bnAb resistance prediction Lance Hepler & IAVI NAC
  76. 76. Bioinformatics Expertise Machine Learning Example: HIV bnAb epitope and bnAb resistance predictionIDEPI: IDentify EPItopes A pipeline for predicting HIV-1 bnAb epitopes from bnAb neutralization titers matched with gp160 sequences.CARTAS: ComputAional Real Time Antibody Surveillance IDEPI extended to predict HIV resistance to bnAb using gp160 sequences Input: IDEPI inferred predictive model based on neutralization titers 23.5k HIV-1 group M gp160 sequences from Los Alamos HIV-1 database Lance Hepler & IAVI NAC
  77. 77. Bioinformatics Expertise Machine Learning Example: HIV bnAb epitope and bnAb resistance predictionIDEPI: IDentify EPItopes A pipeline for predicting HIV-1 bnAb epitopes from bnAb neutralization titers matched with gp160 sequences.CARTAS: ComputAional Real Time Antibody Surveillance IDEPI extended to predict HIV resistance to bnAb using gp160 sequences Input: IDEPI inferred predictive model based on neutralization titers 23.5k HIV-1 group M gp160 sequences from Los Alamos HIV-1 database Lance Hepler & IAVI NAC
  78. 78. Bioinformatics Expertise Machine Learning Example: HIV bnAb epitope and bnAb resistance predictionIDEPI: IDentify EPItopes A pipeline for predicting HIV-1 bnAb epitopes from bnAb neutralization titers matched with gp160 sequences.CARTAS: ComputAional Real Time Antibody Surveillance IDEPI extended to predict HIV resistance to bnAb using gp160 sequences Input: IDEPI inferred predictive model based on neutralization titers 23.5k HIV-1 group M gp160 sequences from Los Alamos HIV-1 database Lance Hepler & IAVI NAC
  79. 79. Bioinformatics Expertise HIV bnAb epitope and bnAb resistance prediction 2F5 Learn More: http://cfar.ucsd.edu/research/croi
  80. 80. Bioinformatics Expertise HIV bnAb epitope and bnAb resistance prediction B12 Learn More: http://cfar.ucsd.edu/research/croi
  81. 81. Bioinformatics Expertise HIV bnAb epitope and bnAb resistance prediction 2F5 + B12 Learn More: http://cfar.ucsd.edu/research/croi
  82. 82. Bioinformatics Expertise HIV bnAb epitope and bnAb resistance prediction 2F5 + B12 Near real-time surveillance Learn More: http://cfar.ucsd.edu/research/croi
  83. 83. Acknowledgements BIT Core CFARSergei Pond Doug RichmanDave Mote Kim SchafferMarco Martinez Bryna BlockJennifer Rodriguez-MuellerSteve Weaver AVRCKonrad Scheffler Susan LittleJoel Wertheim Sheldon MorrisLance Hepler Richard HaubrichMartin Smith Connie Benson Davey Smith IAVI NAC Sanjay MehtaPascal Poignard ... and all the other superheros ... Website: http://cfar.ucsd.edu/bit Twitter: @ucsdbit Email: bitcore@ucsd.edu GitHub: http://github.com/beastcore
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