SIGLa: An Adaptable LIMS for Multiple Laboratories
News Aug 20, 2012
The need to manage large amounts of data is a clear demand for laboratories nowadays. The use of Laboratory Information Management Systems (LIMS) to achieve this is growing each day. A LIMS is a complex computational system used to manage laboratory data with emphasis in quality assurance. Several LIMS are available currently. However, most of them have proprietary code and are commercialized with a high cost. Moreover, due to its complexity, LIMS are usually designed to comply with the needs of one kind of laboratory, making it very difficult to reuse a LIMS. In this work we describe the Sistema Integrado de Gerência de Laboratórios (SIGLa), an open source LIMS with a new approach designed to allow it to adapt its activities and processes to various types of laboratories.
SIGLa incorporates a workflow management system, making it possible to create and manage customized workflows. For each new laboratory a workflow is defined with its activities, rules and procedures. During the execution, for each workflow created, the values of attributes defined in a XPDL file (which describe the workflow) are stored in SIGLa's database, allowing then to be managed and retrieved upon request. These characteristics increase system's flexibility and extend its usability to include the needs of multiple types of laboratories. To construct the main functionalities of SIGLa a workflow of a proteomic laboratory was first defined. To validate the SIGLa capability of adapting to multiples laboratories, on this paper we study the process and the needs of a microarray laboratory and define its workflow. This workflow has been defined in a period of about two weeks, showing the efficiency and flexibility of the tool.
Using SIGLa it has been possible to construct a microarray LIMS in a few days illustrating the flexibility and power of the method proposed. With SIGLa's development we hope to contribute positively to the area of management of complex data in laboratory by managing its large amounts of data, guaranteeing the consistency of the data and increasing the laboratory productivity. We also hope to make possible to laboratories with little resources to afford a high level system for complex data management.
The article is published online in the journal BMC Genomics and is free to access.
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