摘要：Graphical abstractDisplay OmittedAbstractObjectiveDatabases and softwares are important to manage modern high-throughput laboratories and store clinical and genomic information for quality assurance. Commercial softwares are expensive with proprietary code issue while academic versions have adaptation issue. Our aim was to develop an adaptable in-house software that can stores specimen and disease-associated genetic information in biobank to facilitate translational research.MethodsPrototype was designed as per the research requirements and computational tools were used to develop software under three tiers; Visual Basic and ASP.net for presentation tier, SQL server for data tier, and Ajax and JavaScript for business tier. We retrieved specimens from biobank using this software and performed microarray based transcriptomic analysis to detect differentialy expressed genes (DEGs) with FC ±2 and P-value <0.05 in triple negative breast cancer cases. Ingenuity pathway analysis tool was used to predict canonical molecular pathways associated with disease. Overall performance and utility of software was evaluated by JMeter software, CRUD function test and set of feedback questioners.ResultsWe developed “Biosearch System”, a web-based software enabling management of biobank samples (tissue, blood, FTTP slides) and their extracts (DNA, RNA and proteins) with clinical and experimental details. The client satisfaction feedback was excellent with score 4.7/5. We identified a total of 1181 DEGs including both upregulated (IFI6, LEF1, FANCI, CASC5, PLXNA3etc.) and down-regulated (ADH1B, LYVE1, ADH1C, ADH1B, ADIPOQ, PLIN1, LYVE1etc.) genes in triple negative breast cancer. Pathway analysis of DEGs revealed significant activation of interferon signaling (z-score 2.646) and kinetochore metaphase signaling pathway (z-score 2.138) in cancer.ConclusionBiosearch System is a user friendly LIMS for collection, storage and retrieval of specimen and clinical information. It is secure, efficient, and very convenient in sample tracking and data analysis. We illustrated its utility in transcriptomic study of breast cancer. Additionally, it can facilitate and speed up any genomic study and translational research publications.
