Poster #15: Viveknarayanan Padmanabhan MS

From Spreadsheets to Sample Discovery: A Scalable Informatics Framework for Biobank Data Integration

Viveknarayanan Padmanabhan, MS¹; Edward Krause, MS¹; Jessica H. Lee²; Scott W. Canna, MD²˒³ --->¹ DBHi Translational Research Informatics Group, Children’s Hospital of Philadelphia, Philadelphia, PA, USA ² Immune Dysregulation Frontier Program, Department of Pediatrics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA ³ Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

Large-scale biobanking has evolved from simple biospecimen storage into a data-intensive component of precision medicine. However, smaller biobanks and research laboratories often face significant informatics challenges, including limited resources, reliance on error-prone spreadsheets, heterogeneous data structures, and the need to maintain data quality and biospecimen traceability. Comprehensive Laboratory Information Management Systems (LIMS) can address many of these challenges but may be cost-prohibitive or require substantial implementation and informatics support. We developed a reusable informatics pipeline to bridge the gap between spreadsheet-based sample tracking and a dedicated LIMS by transforming heterogeneous legacy data into a standardized, longitudinal REDCap biospecimen data model. Existing datasets are mapped and harmonized into standardized fields and terminology while preserving sample-level granularity. The data model captures participant, collection event, specimen, processing, storage, and associated research metadata and was designed to standardize core biospecimen information while remaining customizable across studies, research groups, and specimen types. Standardized biospecimen records are integrated with electronic health record (EHR)-derived clinical data, extending the system beyond sample inventory management to support cohort and biospecimen discovery based on both clinical and specimen characteristics. The integrated data are presented through an interactive Qlik Sense discovery layer, allowing users to dynamically filter cohorts, evaluate sample availability, visualize key metrics, and identify specimens meeting research-specific criteria. This framework demonstrates how widely available research informatics tools can transform spreadsheet-based biobank data into a structured, interoperable, and queryable resource. The approach provides a practical and scalable pathway for resource-constrained biobanks to improve data standardization, traceability, and accessibility while establishing a foundation for future migration to more comprehensive informatics platforms.