1. SATuRN HIV-1 Genotyping System Justen Manasa Africa Centre for Health and Population Studies Virology laboratory based at the Nelson R. Mandela School of Medicine University of KwaZulu Natal

2. SATuRN A collaborative network of scientists and researchers working on HIV and TB drug resistance.

3. Collaborative SATuRN version (currently 3000 patients with genotype information). Users: AC-UKZN (Dr. Tulio de Oliveira). MRC and Jembi Health Systems (Dr. Chris Seebregts) HPP Virology, University of KwaZulu-Natal (Dr. Mitchell Gordon) Stellenbosh Univ. (Dr. Gert Van Zyl), NICD (Prof. Lynn Morris, Dr. Gillian Hunt), NHLS, Witts University (Prof Wendy Stevens/Prof Maria Papathanasopoulos) Inkhosi Albert Luthuli Department of Virology Laboratory NHLS (Dr. Pravi Moodely) UFS (Dr. Cloete van Vurren, Dewald Stein), Pretoria Univ. (Prof. Sharon Cassol and Theresa Russouw), Dept of Medical Virology, University of Pretoria (Prof. Sheila Bowyer/Prof Lynne Webber) Ministry of Health (Botswana) (Dr. Madisa Mine and Dr. Avalos) AIDS and TB Unit -Ministry of Health (Zimbabwe) Stanford-UZ (Prof. David Katzenstein), Policy: 1.No private patient information or public identification number is added to the database. 2.Data is owned by the researchers. 3.Database is password protected and datasets and dataset access is exclusive to the researcher. Researcher can give access to part of the data to its collaborators. 4.After publication researchers can choose to deposit the data in a collaborative area “public area” of the database where other participants of the network have access to the data. 5.Publication of manuscripts from “public” area will include as authors up to two members of the group that deposited data.

4. Sequence Analysis At AC Lab RegaDB database at MRC Data Entries at AC + clinical and treatment info

5. RT-PCR Protocol Protease and RT Reverse transcription RT21 MAW26 1 st Round PCR RT20 Pro1 2 nd Round PCR 1.3 kb fragment

6. SATuRN Data Curation Collaborators SATuRN DB Stanford HIVDB Mirror HIVDB Data Curation Rega DB Data Curation At Stanford Input data Sequences Clinical data Tx history Viral load and CD4 results PASSWORD PROTECTION OF Rega DB

7. –A medical informatics database dedicated to HIV management –Linked to a some of the leading HIV drug resistance analysis tools –Can be used as either a clinical or research tool –The online version provides a very good collaborative platform –To be used by Clinicians Researchers –Data is password protected –All data is de-identified –Data can be published thus accessible to other members of the consortium or unpublished-access limited to the owners only. Developed by: REGA Institute, Leuven and Mybiodata REGA DATABASE (RegaDB)

8. RegaDB Objective A free and open-source database system Facilitate research collaborations –Easily interchange data, keeping track of data origin –Create collaborative databases keeping track of dataset versions –Easily publish data sets to Stanford HIVDB Empower clinicians with analysis tools –Patient history graph –Sequence analyses HIV typing and subtyping Genotypic analyses for resistance interpretation Third party analyses as “web services” –Resistance reports

9. Data Entities Patient Therapy Dataset Test Viral Isolate Attribute Event

10. Attributes Annotate Patient with information –Adapts to “your (research) interest” –Typically clinical and epidemiological information Examples –Gender –HIV transmission risk group –Most of information collected in Collaborative projects Attributes may be grouped (like “SATuRN or SPREAD”) ‏

11. Viral Isolates Viral Isolates Insertion Nucleotide Sequence Amino Acid Sequence Mutation Protein Information automatically inferred ANALYSESANALYSES

12. Analysis Services Built-in services –REGA Subtype tool –Stanford ASI genotypic resistance algorithms ANRS, HIVDB, REGA –Resistance report generation Using your own template documents Integrate with external analysis services –Using “web services” –e.g. connect to geno2pheno or Stanford

13. Synchronize auxiliary data Auto-update services for –Drug definitions –New (versions of) resistance algorithms –Test types / Tests –Attribute types / Attribute groups

15. Data Set Management

16. Patient Management

17. Patient Profile

18. Management of Patient Measurements

20. Management of Therapies

26. Drug resistance report

27. Patient Chart

28. Thank You