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."

1. AIDS CLINICAL ROUNDS
The UC San Diego AntiViral Research Center sponsors weekly
presentations by infectious disease clinicians, physicians and
researchers. The goal of these presentations is to provide the most
current research, clinical practices and trends in HIV, HBV, HCV, TB
and other infectious diseases of global significance.
The slides from the AIDS Clinical Rounds presentation that you are
about to view are intended for the educational purposes of our
audience. They may not be used for other purposes without the
presenter’s express permission.

2. Open Source Software Solutions for
Clinical Research: Applications for
HIV Research
Jason A. Young, Ph.D
Assistant Professor
Department of Medicine, UCSD
AIDS Clinical Rounds - 8.3.12

3. UCSD CFAR BIT Core
Bioinformatics and Information Technologies (BIT) Core
Aims
• Provide bio/informatics expertise
• To be agile, interactive, affordable
• Committed to open source
Resources
• The BIT Core team!
• 24/7, secure web & data servers
• 500+ node compute cluster
• A collection of open source
software solutions and services Website: https://cfar.ucsd.edu/bit
E-mail: bitcore@ucsd.edu
GitHub: http://github.com/beastcore
Clients
• 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. Outline
1. Web and Mobile Research Services
2. Clinical Data Management
3. Bioinformatics Expertise

5. Outline
1. Web and Mobile Research Services
2. Clinical Data Management
3. Bioinformatics Expertise

6. Web and Mobile Research Services
“A powerful, flexible open source
Content Management System”
Accessible:
Non-technical users can create websites and maintain content
using only a web browser. Comes with built-in workflows,
permissions, etc.
Widely deployed:
Broad user base includes NASA, Nokia, Novell, and major
universities (Harvard, MIT and Penn State).
Large development and support base:
340 core developers and >300 solution providers in 57
countries.
Mature:
First released in 2001. Provides developers a robust framework
for custom product development.
Secure:
Best security record of any major CMS.
Extensible:
Over 1900 projects extending core functionality currently
available.

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. 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. 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. 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. 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. 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. Web and Mobile Research Services
HIVe: 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. Web and Mobile Research Services
HIVe: 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. Web and Mobile Research Services
San 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.edu
information, labs, viral sequences, and much
more... LTW - leadthewaysd.com
• Specimen: Over 200k
UCSD UCSF MGH AIED
ucsd.hive.ucsd.edu
AIEDRP AIEDRP AIEDRP AIEDRP
Susan Little (UCSD) AIEDRP AIEDRP

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. 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. 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. 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. 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. 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. 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. Web and Mobile Research Services
iFormBuilder
iOS Mobile Data
Collection Platform

24. Web and Mobile Research Services
iFormBuilder
iOS Mobile Data Functionality
Collection Platform • Runs on all iOS devices
• 25+ field widgets
• Flexible skip logic
• GPS functionality
• HIPAA compliant
• Encrypted data upload to cloud

25. Web and Mobile Research Services
iFormBuilder
iOS Mobile Data Functionality
Collection 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. Outline
1. Web and Mobile Research Services
2. Clinical Data Management
3. Bioinformatics Expertise

27. Clinical Data Management
Open 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. Clinical Data Management
Open 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
SDPIC Infection Numerous data management OCCAMS
Cohort Begins solutions and providers development begins
William of Ockham (1288-1347)

29. Clinical Data Management
Open 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
SDPIC Infection Numerous data management OCCAMS
Cohort 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. Clinical Data Management
Open 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 Present
SDPIC Infection Numerous data management OCCAMS Alpha version +2.5k AEH enrollments
Cohort 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. Clinical Data Management
SDPIC 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. Clinical Data Management
SDPIC 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. Clinical Data Management
SDPIC 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. Clinical Data Management
SDPIC 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. Clinical Data Management
SDPIC 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. Clinical Data Management
SDPIC 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. Clinical Data Management
SDPIC 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. Clinical Data Management
SDPIC 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. Clinical Data Management
SDPIC 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. 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. 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. 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. 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. 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. 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. Clinical Data Management
OCCAMS Modular Development
Core
occams.clinical
occams.datastore
occams.form
occams.export
occams.import

47. Clinical Data Management
OCCAMS Modular Development
Core
occams.clinical
occams.datastore
occams.form
occams.export
occams.import

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. Clinical Data Management
Form Versioning with OCCAMS

50. Clinical Data Management
Form Versioning with OCCAMS

51. Clinical Data Management
Form Versioning with OCCAMS
Version 1
1/2010 - ...
A=
B=
C=

52. Clinical Data Management
Form Versioning with OCCAMS
Version 1 Version 2
1/2010 - ... 7/2010 - ...
A= A=
B= B=
C= D=

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. 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. 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. 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. 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. 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. Outline
1. Web and Mobile Research Services
2. Clinical Data Management
3. Bioinformatics Expertise

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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. Bioinformatics Expertise
Machine Learning
Example: HIV bnAb epitope and bnAb resistance prediction
Lance Hepler & IAVI NAC

76. Bioinformatics Expertise
Machine Learning
Example: HIV bnAb epitope and bnAb resistance prediction
IDEPI: 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. Bioinformatics Expertise
Machine Learning
Example: HIV bnAb epitope and bnAb resistance prediction
IDEPI: 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. Bioinformatics Expertise
Machine Learning
Example: HIV bnAb epitope and bnAb resistance prediction
IDEPI: 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. Bioinformatics Expertise
HIV bnAb epitope and bnAb resistance prediction
2F5
Learn More: http://cfar.ucsd.edu/research/croi

80. Bioinformatics Expertise
HIV bnAb epitope and bnAb resistance prediction
B12
Learn More: http://cfar.ucsd.edu/research/croi

81. Bioinformatics Expertise
HIV bnAb epitope and bnAb resistance prediction
2F5 + B12
Learn More: http://cfar.ucsd.edu/research/croi

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. Acknowledgements
BIT Core CFAR
Sergei Pond Doug Richman
Dave Mote Kim Schaffer
Marco Martinez Bryna Block
Jennifer Rodriguez-Mueller
Steve Weaver AVRC
Konrad Scheffler Susan Little
Joel Wertheim Sheldon Morris
Lance Hepler Richard Haubrich
Martin Smith Connie Benson
Davey Smith
IAVI NAC Sanjay Mehta
Pascal Poignard ... and all the other superheros ...
Website: http://cfar.ucsd.edu/bit
Twitter: @ucsdbit
Email: bitcore@ucsd.edu
GitHub: http://github.com/beastcore