open source software solutions for clinical research: applications for hiv research
DESCRIPTION
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."TRANSCRIPT
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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.
AIDS CLINICAL ROUNDS
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Open Source Software Solutions for Clinical Research: Applications for HIV Research
Jason A. Young, Ph.DAssistant ProfessorDepartment of Medicine, UCSD
AIDS Clinical Rounds - 8.3.12
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UCSD CFAR BIT CoreBioinformatics 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
Clients• Center For AIDS Research (CFAR)• AntiViral Research Center (AVRC)• AIDS Research Institute (ARI)
• IAVI Neutralizing Antibody Consortium (IAVI-NAC)• California Collaborative Treatment Group (CCTG)• ... other research investigators and growing ...
Website: https://cfar.ucsd.edu/bitE-mail: [email protected]: http://github.com/beastcore
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Outline
1. Web and Mobile Research Services
2. Clinical Data Management
3. Bioinformatics Expertise
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Outline
1. Web and Mobile Research Services
2. Clinical Data Management
3. Bioinformatics Expertise
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Web and Mobile Research Services
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.
“A powerful, flexible open source Content Management System”
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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)
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Web and Mobile Research Services
Doug Richman (UCSD)
ARI - ari.ucsd.edu CFAR - cfar.ucsd.edu AVRC - avrc.ucsd.edu HIVe - hive.ucsd.edu ET - theearlytest.ucsd.edu LTW - leadthewaysd.com
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Doug Richman (UCSD)
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
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• Core Service Request Forms• Feedback Forms• Developmental Grant Submission System• Laboratory Experiment Tracking System• Retroviral Seminar Series Calendar
Doug Richman (UCSD)
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
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Constance Benson (UCSD)
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
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Web and Mobile Research Services
• Clinical trial information• Events calendar• AIDS rounds presentation slides*
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)
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Web and Mobile Research Services
UCSD UCSF MGH
AIEDRP AIEDRP
AIEDRP
AIEDRP
AIEDRP
AIEDRP AIEDRP
HIVe: HIV e-resource (hive.ucsd.edu)• Acute and Early HIV (AEH) cohort network• Data standardization and sharing• 3 active & 7 legacy AEH sites
ARI - ari.ucsd.edu CFAR - cfar.ucsd.edu AVRC - avrc.ucsd.edu HIVe - hive.ucsd.edu ET - theearlytest.ucsd.edu LTW - leadthewaysd.com
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Web and Mobile Research Services
UCSD UCSF MGH
AIEDRP AIEDRP
AIEDRP
AIEDRP
AIEDRP
AIEDRP AIEDRP
ucsd.hive.ucsd.edu
HIVe: HIV e-resource (hive.ucsd.edu)• Acute and Early HIV (AEH) cohort network• Data standardization and sharing• 3 active & 7 legacy AEH sites
mgh.hive.ucsd.edu
ucsf.hive.ucsd.edu
hive.ucsd.edu
ARI - ari.ucsd.edu CFAR - cfar.ucsd.edu AVRC - avrc.ucsd.edu HIVe - hive.ucsd.edu ET - theearlytest.ucsd.edu LTW - leadthewaysd.com
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Web and Mobile Research ServicesSan Diego Primary Infection Cohort (SDPIC)(1996 - Present)• 2 Screening Programs (~12k screens)• 7 Research Studies (~2.5k enrollments)• Data: Demographics, risk factors, partner information, labs, viral sequences, and much more...• Specimen: Over 200k
UCSD UCSF MGH
AIEDRP AIEDRP
AIEDRP
AIEDRP
AIEDRP
AIEDRP AIEDRP
Susan Little (UCSD)
ucsd.hive.ucsd.edu
ARI - ari.ucsd.edu CFAR - cfar.ucsd.edu AVRC - avrc.ucsd.edu HIVe - hive.ucsd.edu ET - theearlytest.ucsd.edu LTW - leadthewaysd.com
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Susan Little & Davey Smith (UCSD)
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
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Susan Little & Davey Smith (UCSD)
Web and Mobile Research Services
• AEH screening program (Rapid/NAT)• Obtain NAT results online or over the phone in two weeks
ARI - ari.ucsd.edu CFAR - cfar.ucsd.edu AVRC - avrc.ucsd.edu HIVe - hive.ucsd.edu ET - theearlytest.ucsd.edu LTW - leadthewaysd.com
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Susan Little (UCSD)
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
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Susan Little (UCSD)
Web and Mobile Research Services
• 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?
ARI - ari.ucsd.edu CFAR - cfar.ucsd.edu AVRC - avrc.ucsd.edu HIVe - hive.ucsd.edu ET - theearlytest.ucsd.edu LTW - leadthewaysd.com
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Susan Little (UCSD)
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
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Susan Little (UCSD)
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
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Susan Little (UCSD)
Web and Mobile Research Services
• 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?
ARI - ari.ucsd.edu CFAR - cfar.ucsd.edu AVRC - avrc.ucsd.edu HIVe - hive.ucsd.edu ET - theearlytest.ucsd.edu LTW - leadthewaysd.com
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Web and Mobile Research ServicesiFormBuilderiOS Mobile Data
Collection Platform
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Web and Mobile Research ServicesiFormBuilderiOS Mobile Data
Collection PlatformFunctionality • Runs on all iOS devices• 25+ field widgets• Flexible skip logic• GPS functionality• HIPAA compliant• Encrypted data upload to cloud
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Web and Mobile Research ServicesiFormBuilderiOS Mobile Data
Collection Platform
One year for LTW...• 1317 iPad administered surveys• 1062 individuals tested for HIV• 24 newly diagnosed HIV(+) cases
Functionality • Runs on all iOS devices• 25+ field widgets• Flexible skip logic• GPS functionality• HIPAA compliant• Encrypted data upload to cloud
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Outline
1. Web and Mobile Research Services
2. Clinical Data Management
3. Bioinformatics Expertise
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Open source Clinical Content Analysis and Management SystemOCCAMS: Designed to handle all aspects of complex and evolving clinical research studies
Clinical Data Management
William of Ockham (1288-1347)
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Open source Clinical Content Analysis and Management System
April 2010
OCCAMS development begins
~15 years
Numerous data managementsolutions and providers
OCCAMS: Designed to handle all aspects of complex and evolving clinical research studies
June 1996
SDPIC InfectionCohort Begins
Clinical Data Management
William of Ockham (1288-1347)
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Open source Clinical Content Analysis and Management System
April 2010
OCCAMS development begins
~15 years
Numerous data managementsolutions and providers
OCCAMS: Designed to handle all aspects of complex and evolving clinical research studies
1. Web-accessible (eCRFs)2. Patient centric (no data duplication)3. Broad data support4. Integrated specimen handling5. QA workflows and auditing reports6. PHI-compliant with granular permissions7. Modular, flexible and extensible8. Open source (Plone/Python-powered)
June 1996
SDPIC InfectionCohort Begins
Clinical Data Management
William of Ockham (1288-1347)
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Open source Clinical Content Analysis and Management System
April 2010
OCCAMS development begins
Present
+2.5k AEH enrollments+12k AEH screens
~15 years
Numerous data managementsolutions and providers
OCCAMS: Designed to handle all aspects of complex and evolving clinical research studies
1. Web-accessible (eCRFs)2. Patient centric (no data duplication)3. Broad data support4. Integrated specimen handling5. QA workflows and auditing reports6. PHI-compliant with granular permissions7. Modular, flexible and extensible8. Open source (Plone/Python-powered)
June 1996
SDPIC InfectionCohort Begins
September 2010
Alpha version launched for SDPIC
Clinical Data Management
William of Ockham (1288-1347)
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SDPIC pre-OCCAMS workflow...1. Nurse sees patient, completes
source docs
2. Nurse completes case report form (CRF)
3. CRF and source documentation consistency checked by AVRC staff
4. CRF entered into database by students
Clinical Data Management
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Challenges
Clinical Data Management
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.
SDPIC pre-OCCAMS workflow...
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Challenges
Clinical Data Management
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.
SDPIC pre-OCCAMS workflow...
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Challenges
Clinical Data Management
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.
SDPIC pre-OCCAMS workflow...
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Challenges
Clinical Data Management
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.
SDPIC pre-OCCAMS workflow...
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Challenges
Clinical Data Management
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.
SDPIC pre-OCCAMS workflow...
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Challenges
Clinical Data Management
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.
SDPIC pre-OCCAMS workflow...
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Challenges
Clinical Data Management
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.
SDPIC pre-OCCAMS workflow...
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SDPIC post-OCCAMS workflow...1. Nurse sees patient, completes
source docs
2. Nurse direct enters data via eCRF that is automatically generated based
on study and visit week
3. Workflow notifies AVRC staff eCRF ready for QC
4. High volume eCRFs entered by students
Clinical Data Management
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Challenges
Clinical Data Management
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.
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Challenges
Clinical Data Management
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.
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Challenges
Clinical Data Management
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.
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Challenges
Clinical Data Management
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.
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Clinical Data Management
Challenges1. 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.
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Clinical Data Management
Challenges1. 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.
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Clinical Data Management
occams.clinical
occams.import
occams.export
occams.datastore
occams.form
Core
OCCAMS Modular Development
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Clinical Data Management
occams.clinical
occams.import
occams.export
occams.datastore
occams.form
Core
OCCAMS Modular Development
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• EAV Database Structure~60 SQL tables total
• Robust versioning supportData versioning (audit trail)Form versioning (revision history)
Clinical Data Management
occams.clinical
occams.import
occams.export
occams.datastore
occams.form
Core
OCCAMS Modular Development
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Clinical Data ManagementForm Versioning with OCCAMS
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Clinical Data ManagementForm Versioning with OCCAMS
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Version 11/2010 - ...
A = B = C =
Clinical Data ManagementForm Versioning with OCCAMS
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Version 11/2010 - ...
Version 27/2010 - ...
A = B = C =
A = B = D =
Clinical Data ManagementForm Versioning with OCCAMS
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Version 11/2010 - ...
Version 27/2010 - ...
A = B = C =
A = B = D =
A = B = E =
Version 310/2010 - ...
Clinical Data ManagementForm Versioning with OCCAMS
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Version 11/2010 - ...
A = B = C =
A = B = D =
A = B = E =
Version 310/2010 - ...
Version 27/2010 - ...
Clinical Data ManagementForm Versioning with OCCAMS
Which Form to Use?Example. Visit on 8/2010
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Version 11/2010 - ...
Version 27/2010 - Retract
A = B = C =
A = B = D =
A = B = E =
Version 310/2010 - ...
Clinical Data ManagementForm Versioning with OCCAMS
Which Form to Use?Example. Visit on 8/2010
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Version 11/2010 - ...
Version 27/2010 - Retract
A = B = C =
A = B = D =
A = B = E =
Version 310/2010 - ...
• 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
Clinical Data ManagementForm Versioning with OCCAMS
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Clinical Data Management
occams.clinical
occams.import
occams.export
occams.datastore
occams.form
occams.lab
occams.sequence
occams.symptom
occams.drug
occams.partner
occams.edi
occams.transmission
Core Add-ons
OCCAMS Modular Development
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Clinical Data Management
occams.clinical
occams.import
occams.export
occams.datastore
occams.form
occams.lab
occams.sequence
occams.symptom
occams.drug
occams.partner
occams.edi
occams.transmission
Core Add-ons
OCCAMS Modular Development
• Currently undergoing finalization of remaining core features and testing• Public Beta release aimed for first half of 2013
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Outline
1. Web and Mobile Research Services
2. Clinical Data Management
3. Bioinformatics Expertise
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Bioinformatics ExpertiseBioinformatics Expertise
HyPhy (hyphy.org)A molecular evolution and statistical sequence analysis software package• Positive/Negative selection detection• Recombination analysis• Nucleotide, protein and codon model selectionSome 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 ServicesExamples... • Sequence analysis (traditional and NGS) • Network analysis• Machine Learning
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Bioinformatics ExpertiseBioinformatics Expertise
HyPhy (hyphy.org)A molecular evolution and statistical sequence analysis software package• Positive/Negative selection detection• Recombination analysis• Nucleotide, protein and codon model selectionSome 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 ServicesExamples... • Sequence analysis (traditional and NGS) • Network analysis• Machine Learning
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Bioinformatics ExpertiseNetwork Analysis
AEHStudy
PartnerStudy
ETNAT/Rapid
Testing
LTWNAT/Rapid
Testing
ScreeningPrograms
ObservationalStudies
Partner Counseling &
Referral Services(PCRS)
AEHInfection
NAT(+)/Rapid(-)<70 EDI
Example: SDPIC Transmission Network
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Bioinformatics Expertise
Phylogenetic LinkGenetic distance between
HIV pol sequences isolated from any two individuals
is <= 1%
Epidemological LinkPartner Counseling and Referral
Services (PCRS) results in index to partner linkage being identified
(both persons enrolled on study)
SDPIC Transmission Network
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Bioinformatics Expertise
Phylogenetic LinkGenetic distance between
HIV pol sequences isolated from any two individuals
is <= 1%
Epidemological LinkPartner Counseling and Referral
Services (PCRS) results in index to partner linkage being identified
(both persons enrolled on study)
SDPIC Transmission Network
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Bioinformatics Expertise
Phylogenetic LinkGenetic distance between
HIV pol sequences isolated from any two individuals
is <= 1%
Epidemological LinkPartner Counseling and Referral
Services (PCRS) results in index to partner linkage being identified
(both persons enrolled on study)
AEHAEHHIV (-) Chronic
AEH Study Partner Study
“Epilinks”
SDPIC Transmission Network
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Bioinformatics Expertise
Epidemological LinkPartner Counseling and Referral
Services (PCRS) results in index to partner linkage being identified
(both persons enrolled on study)
Phylogenetic LinkGenetic distance between
HIV pol sequences isolated from any two individuals
is <= 1%
SDPIC Transmission Network
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Bioinformatics Expertise
Epidemological LinkPartner Counseling and Referral
Services (PCRS) results in index to partner linkage being identified
(both persons enrolled on study)
AEHAEHHIV (-) Chronic
Partner Study
Phylogenetic LinkGenetic distance between
HIV pol sequences isolated from any two individuals
is <= 1%
“Phylolinks”
AEH Study
SDPIC Transmission Network
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Bioinformatics Expertise
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?
Phylogenetic LinkGenetic distance between
HIV pol sequences isolated from any two individuals
is <= 1%
Epidemological LinkPartner Counseling and Referral
Services (PCRS) results in index to partner linkage being identified
(both persons enrolled on study)
SDPIC Transmission Network
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Bioinformatics Expertise1. How effective is PCRS in an AEH setting?
Number Needed To Interview Previous PCRS studies report...NNTI: 11-15A single AEH PCRS study reports...NNTI: 25 (25/1)SDPIC...NNTI: 5.9
Sheldon Morris & Susan Little
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Bioinformatics Expertise1. How effective is PCRS in an AEH setting?
Number Needed To Interview Previous PCRS studies report...NNTI: 11-15A single AEH PCRS study reports...NNTI: 25 (25/1)SDPIC...NNTI: 5.9
Sheldon Morris & Susan Little
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Bioinformatics Expertise1. How effective is PCRS in an AEH setting?
Number Needed To Interview Previous PCRS studies report...NNTI: 11-15A single AEH PCRS study reports...NNTI: 25 (25/1)SDPIC...NNTI: 5.9
Sheldon Morris & Susan Little
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Bioinformatics Expertise1. How effective is PCRS in an AEH setting?
Number Needed To Interview Previous PCRS studies report...NNTI: 11-15A single AEH PCRS study reports...NNTI: 25 (25/1)SDPIC...NNTI: 5.9
Sheldon Morris & Susan Little
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Bioinformatics Expertise2. What is the structure of the SDPIC transmission network?
Scale-free network structure• Highly-connected nodes critical
Number Needed To Interview Previous PCRS studies report...NNTI: 11-15A single AEH PCRS study reports...NNTI: 25 (25/1)SDPIC...NNTI: 5.9
Sheldon Morris & Susan Little
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Bioinformatics Expertise3. Do HIV(+) reported partners represent actual transmission links?
Only ~34% of seroconcordant epi-linked pairs are also phylo-linked
Number Needed To Interview Previous PCRS studies report...NNTI: 11-15A 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
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Bioinformatics ExpertiseMachine Learning
Lance Hepler & IAVI NAC
Example: HIV bnAb epitope and bnAb resistance prediction
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Bioinformatics ExpertiseMachine Learning
IDEPI: IDentify EPItopesA pipeline for predicting HIV-1 bnAb epitopes from bnAb neutralization titers matched with gp160 sequences.
CARTAS: ComputAional Real Time Antibody SurveillanceIDEPI extended to predict HIV resistance to bnAb using gp160 sequencesInput:
IDEPI inferred predictive model based on neutralization titers23.5k HIV-1 group M gp160 sequences from Los Alamos HIV-1 database
Lance Hepler & IAVI NAC
Example: HIV bnAb epitope and bnAb resistance prediction
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Bioinformatics ExpertiseMachine Learning
IDEPI: IDentify EPItopesA pipeline for predicting HIV-1 bnAb epitopes from bnAb neutralization titers matched with gp160 sequences.
CARTAS: ComputAional Real Time Antibody SurveillanceIDEPI extended to predict HIV resistance to bnAb using gp160 sequencesInput:
IDEPI inferred predictive model based on neutralization titers23.5k HIV-1 group M gp160 sequences from Los Alamos HIV-1 database
Lance Hepler & IAVI NAC
Example: HIV bnAb epitope and bnAb resistance prediction
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Bioinformatics ExpertiseMachine Learning
IDEPI: IDentify EPItopesA pipeline for predicting HIV-1 bnAb epitopes from bnAb neutralization titers matched with gp160 sequences.
CARTAS: ComputAional Real Time Antibody SurveillanceIDEPI extended to predict HIV resistance to bnAb using gp160 sequencesInput:
IDEPI inferred predictive model based on neutralization titers23.5k HIV-1 group M gp160 sequences from Los Alamos HIV-1 database
Lance Hepler & IAVI NAC
Example: HIV bnAb epitope and bnAb resistance prediction
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Bioinformatics Expertise
2F5
HIV bnAb epitope and bnAb resistance prediction
Learn More: http://cfar.ucsd.edu/research/croi
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Bioinformatics Expertise
B12
HIV bnAb epitope and bnAb resistance prediction
Learn More: http://cfar.ucsd.edu/research/croi
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Bioinformatics Expertise
2F5 + B12
HIV bnAb epitope and bnAb resistance prediction
Learn More: http://cfar.ucsd.edu/research/croi
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Bioinformatics Expertise
2F5 + B12
Near real-time surveillance
Learn More: http://cfar.ucsd.edu/research/croi
HIV bnAb epitope and bnAb resistance prediction
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BIT CoreSergei PondDave MoteMarco MartinezJennifer Rodriguez-MuellerSteve WeaverKonrad SchefflerJoel WertheimLance HeplerMartin Smith
AVRCSusan LittleSheldon MorrisRichard HaubrichConnie BensonDavey SmithSanjay Mehta... and all the other superheros ...
CFARDoug RichmanKim SchafferBryna Block
Acknowledgements
Website: http://cfar.ucsd.edu/bitTwitter: @ucsdbitEmail: [email protected]: http://github.com/beastcore
IAVI NACPascal Poignard