Dataset Information

Baobab Laboratory Information Management System: Development of an Open-Source Laboratory Information Management System for Biobanking.

ABSTRACT: A laboratory information management system (LIMS) is central to the informatics infrastructure that underlies biobanking activities. To date, a wide range of commercial and open-source LIMSs are available and the decision to opt for one LIMS over another is often influenced by the needs of the biobank clients and researchers, as well as available financial resources. The Baobab LIMS was developed by customizing the Bika LIMS software ( www.bikalims.org ) to meet the requirements of biobanking best practices. The need to implement biobank standard operation procedures as well as stimulate the use of standards for biobank data representation motivated the implementation of Baobab LIMS, an open-source LIMS for Biobanking. Baobab LIMS comprises modules for biospecimen kit assembly, shipping of biospecimen kits, storage management, analysis requests, reporting, and invoicing. The Baobab LIMS is based on the Plone web-content management framework. All the system requirements for Plone are applicable to Baobab LIMS, including the need for a server with at least 8 GB RAM and 120 GB hard disk space. Baobab LIMS is a server-client-based system, whereby the end user is able to access the system securely through the internet on a standard web browser, thereby eliminating the need for standalone installations on all machines.

SUBMITTER: Bendou H

PROVIDER: S-EPMC5397207 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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Baobab Laboratory Information Management System: Development of an Open-Source Laboratory Information Management System for Biobanking.

Bendou Hocine H Sizani Lunga L Reid Tim T Swanepoel Carmen C Ademuyiwa Toluwaleke T Merino-Martinez Roxana R Meuller Heimo H Abayomi Akin A Christoffels Alan A

Biopreservation and biobanking 20170404 2

A laboratory information management system (LIMS) is central to the informatics infrastructure that underlies biobanking activities. To date, a wide range of commercial and open-source LIMSs are available and the decision to opt for one LIMS over another is often influenced by the needs of the biobank clients and researchers, as well as available financial resources. The Baobab LIMS was developed by customizing the Bika LIMS software ( www.bikalims.org ) to meet the requirements of biobanking be ...[more]

PMID: 28375759

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Project description:BACKGROUND: In many laboratories, researchers store experimental data on their own workstation using spreadsheets. However, this approach poses a number of problems, ranging from sharing issues to inefficient data-mining. Standard spreadsheets are also error-prone, as data do not undergo any validation process. To overcome spreadsheets inherent limitations, a number of proprietary systems have been developed, which laboratories need to pay expensive license fees for. Those costs are usually prohibitive for most laboratories and prevent scientists from benefiting from more sophisticated data management systems. RESULTS: In this paper, we propose the EnzymeTracker, a web-based laboratory information management system for sample tracking, as an open-source and flexible alternative that aims at facilitating entry, mining and sharing of experimental biological data. The EnzymeTracker features online spreadsheets and tools for monitoring numerous experiments conducted by several collaborators to identify and characterize samples. It also provides libraries of shared data such as protocols, and administration tools for data access control using OpenID and user/team management. Our system relies on a database management system for efficient data indexing and management and a user-friendly AJAX interface that can be accessed over the Internet. The EnzymeTracker facilitates data entry by dynamically suggesting entries and providing smart data-mining tools to effectively retrieve data. Our system features a number of tools to visualize and annotate experimental data, and export highly customizable reports. It also supports QR matrix barcoding to facilitate sample tracking. CONCLUSIONS: The EnzymeTracker was designed to be easy to use and offers many benefits over spreadsheets, thus presenting the characteristics required to facilitate acceptance by the scientific community. It has been successfully used for 20 months on a daily basis by over 50 scientists. The EnzymeTracker is freely available online at http://cubique.fungalgenomics.ca/enzymedb/index.html under the GNU GPLv3 license.

Project description:The National Cancer Institute (NCI) Cancer Biomedical Informatics Grid® (caBIG®) program established standards and best practices for biorepository data management by creating an infrastructure to propagate biospecimen resource sharing while maintaining data integrity and security. caTissue Suite, a biospecimen data management software tool, has evolved from this effort. More recently, the caTissue Suite continues to evolve as an open source initiative known as OpenSpecimen. The essential functionality of OpenSpecimen includes the capture and representation of highly granular, hierarchically-structured data for biospecimen processing, quality assurance, tracking, and annotation. Ideal for multi-user and multi-site biorepository environments, OpenSpecimen permits role-based access to specific sets of data operations through a user-interface designed to accommodate varying workflows and unique user needs. The software is interoperable, both syntactically and semantically, with an array of other bioinformatics tools given its integration of standard vocabularies thus enabling research involving biospecimens. End-users are encouraged to share their day-to-day experiences in working with the application, thus providing to the community board insight into the needs and limitations which need be addressed. Users are also requested to review and validate new features through group testing environments and mock screens. Through this user interaction, application flexibility and interoperability have been recognized as necessary developmental focuses essential for accommodating diverse adoption scenarios and biobanking workflows to catalyze advances in biomedical research and operations. Given the diversity of biobanking practices and workforce roles, efforts have been made consistently to maintain robust data granularity while aiding user accessibility, data discoverability, and security within and across applications by providing a lower learning curve in using OpenSpecimen. Iterative development and testing cycles provide continuous maintenance and up-to-date capabilities for this freely available, open-access, web-based software application that is globally-adopted at over 25 institutions.

Project description:BackgroundHigh-quality plant phenotyping and climate data lay the foundation for phenotypic analysis and genotype-environment interaction, providing important evidence not only for plant scientists to understand the dynamics between crop performance, genotypes, and environmental factors but also for agronomists and farmers to closely monitor crops in fluctuating agricultural conditions. With the rise of Internet of Things technologies (IoT) in recent years, many IoT-based remote sensing devices have been applied to plant phenotyping and crop monitoring, which are generating terabytes of biological datasets every day. However, it is still technically challenging to calibrate, annotate, and aggregate the big data effectively, especially when they were produced in multiple locations and at different scales.FindingsCropSight is a PHP Hypertext Pre-processor and structured query language-based server platform that provides automated data collation, storage, and information management through distributed IoT sensors and phenotyping workstations. It provides a two-component solution to monitor biological experiments through networked sensing devices, with interfaces specifically designed for distributed plant phenotyping and centralized data management. Data transfer and annotation are accomplished automatically through an hypertext transfer protocol-accessible RESTful API installed on both device side and server side of the CropSight system, which synchronize daily representative crop growth images for visual-based crop assessment and hourly microclimate readings for GxE studies. CropSight also supports the comparison of historical and ongoing crop performance while different experiments are being conducted.ConclusionsAs a scalable and open-source information management system, CropSight can be used to maintain and collate important crop performance and microclimate datasets captured by IoT sensors and distributed phenotyping installations. It provides near real-time environmental and crop growth monitoring in addition to historical and current experiment comparison through an integrated cloud-ready server system. Accessible both locally in the field through smart devices and remotely in an office using a personal computer, CropSight has been applied to field experiments of bread wheat prebreeding since 2016 and speed breeding since 2017. We believe that the CropSight system could have a significant impact on scalable plant phenotyping and IoT-style crop management to enable smart agricultural practices in the near future.

Dataset Information

Baobab Laboratory Information Management System: Development of an Open-Source Laboratory Information Management System for Biobanking.

Publications

Baobab Laboratory Information Management System: Development of an Open-Source Laboratory Information Management System for Biobanking.

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