Rapid technological evolution is providing biomedical research laboratories with huge amounts of complex and heterogeneous data. The LIMS project Laboratory Assistant Suite (LAS), started by our Institution, aims to assist researchers throughout all of their laboratory activities, providing graphical tools to support decision-making tasks and building complex analyses on integrated data. Thanks to a clinical data management module, linking biological samples analysed by translational research with the originating patients and their clinical history, it can effectively provide insight into tumor development. Furthermore, the LAS tracks molecular experiments and allows automatic annotation of biological samples with their molecular results. A genomic annotation module makes use of semantic web technologies to represent relevant concepts from the genomic domain. The LAS system has helped improve the overall quality of the data and broadened the spectrum of interconnections among the data, offering novel perspectives to the biomedical analyst.
TopIntroduction
In the last years, the advent of automation and high-throughput technologies in biomedical research laboratory activities has introduced numerous issues related to the amount and diversity of the data produced, the selection of robust procedures for sample tracking, and the management of computer-based workflows needed to process and analyze the raw data generated. For these reasons, the adoption of a Laboratory Management Information System (LIMS) can no longer be overlooked as a means to achieve good levels of quality control over laboratory activities and efficiently handle massive volumes of data.
A LIMS aims at helping researchers in their daily laboratory practice by providing different quality control strategies, improving the accessibility of the instruments, and tracking biological samples and their related information. In particular, more and more laboratories resort to LIMS to improve sample management, which usually involves sample handling, registering and locating (e.g. to retrieve where a particular sample is stored). Moreover, these systems can enforce security policies to restrict data access only to authorized users, by complying with current regulations, such as the ISO/IEC 17025 standard.
Core functionalities commonly found in most LIMS systems include the following:
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Sample tracking
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Stocks management
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Support to the implementation of experimental workflows/protocols
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Management of heterogeneous data
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Built-in features and interfaces that assist researchers throughout laboratory activities.
Nowadays a wide choice of LIMSs is available, both open-source projects and proprietary software. Commercial solutions are large, complex and feature-rich products designed to be sold to large laboratories. Their license fees are often prohibitive, and each extra feature or module they provide might come at additional costs (Wood, 2007). To reduce these costs, the last generation of commercial LIMSs adopt web-oriented software technologies, and in particular the Software as a Service (SaaS) distribution model, lowering the end user’s total expenditure on licensing fees, IT assets and maintenance. Examples of commercial solutions are STARLIMS (Abbott, 2013), Exemplar LIMS (Sapio, 2010), LABVANTAGE SAPPHIRE (Labvantage, 2011).
Some institutions rather opt to invest in the development of in-house solutions and/or to adapt open-source projects to their own requirements. Even though costs may turn out very similar to those of commercial products, laboratories may still prefer in-house development in favor of software functionalities that meet the specific needs of their researchers. Indeed, many of these solutions target specific sub-domains, such as molecular experiments, and tracking of in vivo and/or in vitro experiments. The very graphical interfaces, as well as any data entry strategies, are usually designed to track all the details of the experimental procedures defined by the laboratory technicians, and to simulate the laboratory environment. From a developer’s point of view, in-house solutions also permit to explore and adopt new technologies, to define new and complex data models, and to fine-tune the overall system performance. Last, the developer team may design new software functionalities to keep up with how research needs evolve within the laboratory.