Jajah Fachiroh1,2, Ery Kus Dwianingsih2,3, Amirah Ellyza Wahdi2,4, F Linda Tri Pramatasari2, Sunandar Hariyanto2,5, Nenes Pastiwi2, Junaedy Yunus6, Maimuna Mendy7, Bart Scheerder8, Lutfan Lazuardi2,9. 1. Department of Histology and Cell Biology, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada (FK-KMK UGM), Yogyakarta, Indonesia. 2. Biobank Development Team, Molecular Biology Laboratory, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada (FK-KMK UGM), Yogyakarta, Indonesia. 3. Department of Anatomical Pathology, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada (FK-KMK UGM), Yogyakarta, Indonesia. 4. Center for Reproductive Health, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada (FK-KMK UGM), Yogyakarta, Indonesia. 5. Information Technology Unit, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada (FK-KMK UGM), Yogyakarta, Indonesia. 6. Department of Anatomy, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada (FK-KMK UGM), Yogyakarta, Indonesia. 7. Education and Training Program, The International Agency for Research in Cancer (IARC), Lyon, France. 8. Biobank University Medical Center Groningen (UMCG), Groningen, the Netherlands. 9. Department of Health Management and Policy, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada (FK-KMK UGM), Yogyakarta, Indonesia.
Abstract
Introduction: The establishment of a biobank requires specific expertise along with relatively expensive infrastructure and appropriate technology. This causes certain challenges in biobank implementation for research in low-middle-income countries. Biobank development with established specimens and data collection (legacy collection) was an approach used in the Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada. This approach aimed to identify the resources available at present, while providing nontechnical information for further development of a centralized biobank. Materials and Methods: Retrospective modeling was done in 2015 by recruiting existing specimen collections and their associated data. The steps were as follows: (1) informing research stakeholders through discussion with experts and stakeholders; (2) identifying specimen collections to be used; (3) determining the system, infrastructure, and consumables needed; (4) determining inclusion criteria; (5) building an in-house database system; (6) organizing data and physical specimen collections; and (7) validating data and physical sample arrangement. All technical procedures were built into standard operating procedures. Results: The model included specimens from one -80°C freezer. The associated data included demographic, clinical diagnosis, and physical sample information. Samples came from six studies, collected between 2001 and 2014. A web-based database was built based on the MySQL programming system. Information on biospecimens from a total of 4196 subjects collected in 11,358 vials was entered into the database, following physical rearrangement of vials in the -80°C freezer with one-dimensional barcodes taped to vials, boxes, and racks. A validation test was done for data concordance between the database and physical arrangement in the -80°C freezer, showing no discrepancies. Conclusion: This report demonstrated current technical and nontechnical insights to further develop a centralized biobank for health research at an academic institution in Indonesia.
Introduction: The establishment of a biobank requires specific expertise along with relatively expensive infrastructure and appropriate technology. This causes certain challenges in biobank implementation for research in low-middle-income countries. Biobank development with established specimens and data collection (legacy collection) was an approach used in the Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada. This approach aimed to identify the resources available at present, while providing nontechnical information for further development of a centralized biobank. Materials and Methods: Retrospective modeling was done in 2015 by recruiting existing specimen collections and their associated data. The steps were as follows: (1) informing research stakeholders through discussion with experts and stakeholders; (2) identifying specimen collections to be used; (3) determining the system, infrastructure, and consumables needed; (4) determining inclusion criteria; (5) building an in-house database system; (6) organizing data and physical specimen collections; and (7) validating data and physical sample arrangement. All technical procedures were built into standard operating procedures. Results: The model included specimens from one -80°C freezer. The associated data included demographic, clinical diagnosis, and physical sample information. Samples came from six studies, collected between 2001 and 2014. A web-based database was built based on the MySQL programming system. Information on biospecimens from a total of 4196 subjects collected in 11,358 vials was entered into the database, following physical rearrangement of vials in the -80°C freezer with one-dimensional barcodes taped to vials, boxes, and racks. A validation test was done for data concordance between the database and physical arrangement in the -80°C freezer, showing no discrepancies. Conclusion: This report demonstrated current technical and nontechnical insights to further develop a centralized biobank for health research at an academic institution in Indonesia.