adLIMS: a customized open source software that allows bridging clinical and basic molecular research studies

BACKGROUND: Many biological laboratories that deal with genomic samples are facing the problem of sample tracking, both for pure laboratory management and for efficiency. Our laboratory exploits PCR techniques and Next Generation Sequencing (NGS) methods to perform high-throughput integration site monitoring in different clinical trials and scientific projects. Because of the huge amount of samples that we process every year, which result in hundreds of millions of sequencing reads, we need to standardize data management and tracking systems, building up a scalable and flexible structure with web-based interfaces, which are usually called Laboratory Information Management System (LIMS). METHODS: We started collecting end-users' requirements, composed of desired functionalities of the system and Graphical User Interfaces (GUI), and then we evaluated available tools that could address our requirements, spanning from pure LIMS to Content Management Systems (CMS) up to enterprise information systems. Our analysis identified ADempiere ERP, an open source Enterprise Resource Planning written in Java J2EE, as the best software that also natively implements some highly desirable technological advances, such as the high usability and modularity that grants high use-case flexibility and software scalability for custom solutions. RESULTS: We extended and customized ADempiere ERP to fulfil LIMS requirements and we developed adLIMS. It has been validated by our end-users verifying functionalities and GUIs through test cases for PCRs samples and pre-sequencing data and it is currently in use in our laboratories. adLIMS implements authorization and authentication policies, allowing multiple users management and roles definition that enables specific permissions, operations and data views to each user. For example, adLIMS allows creating sample sheets from stored data using available exporting operations. This simplicity and process standardization may avoid manual errors and information backtracking, features that are not granted using track recording on files or spreadsheets. CONCLUSIONS: adLIMS aims to combine sample tracking and data reporting features with higher accessibility and usability of GUIs, thus allowing time to be saved on doing repetitive laboratory tasks, and reducing errors with respect to manual data collection methods. Moreover, adLIMS implements automated data entry, exploiting sample data multiplexing and parallel/transactional processing. adLIMS is natively extensible to cope with laboratory automation through platform-dependent API interfaces, and could be extended to genomic facilities due to the ERP functionalities.