Journal:Efficient sample tracking with OpenLabFramework

Full article title	Efficient sample tracking with OpenLabFramework
Journal	Scientific Reports
Author(s)	List, Markus; Schmidt, Steffen; Trojnar, Jakub; Thomas, Jochen; Thomassen, Mads; Kruse, Torben A.; Tan, Qihua; Baumbach, Jan; Mollenhauer, Jan
Author affiliation(s)	University of Southern Denmark, io-consultants GmbH & Co. KG
Primary contact	Email: http://www.nature.com/articles/srep04278/email/correspondent/c1/new (Requires login)
Year published	2015
Volume and issue	4
Page(s)	4278
DOI	10.1038/srep04278
ISSN	2045-2322
Distribution license	Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported
Website	http://www.nature.com/articles/srep04278
Download	http://www.nature.com/articles/srep04278.pdf (PDF)

Abstract

The advance of new technologies in biomedical research has led to a dramatic growth in experimental throughput. Projects therefore steadily grow in size and involve a larger number of researchers. Spreadsheets traditionally used are thus no longer suitable for keeping track of the vast amounts of samples created and need to be replaced with state-of-the-art laboratory information management systems. Such systems have been developed in large numbers, but they are often limited to specific research domains and types of data. One domain so far neglected is the management of libraries of vector clones and genetically engineered cell lines. OpenLabFramework is a newly developed web-application for sample tracking, particularly laid out to fill this gap, but with an open architecture allowing it to be extended for other biological materials and functional data. Its sample tracking mechanism is fully customizable and aids productivity further through support for mobile devices and barcoded labels.

Introduction

With the development of high-throughput technologies, laboratory work has seen a paradigm shift from small projects involving single or few researchers towards large-scale projects involving several laboratories and often hundreds or thousands of samples. Sample management is therefore a growing issue, especially since most laboratories still attempt to keep track of their samples using spreadsheet tools. A high turn-over of academic staff coupled with maintenance of individual files that are often locked or outdated, as well as inconsistent nomenclature and labeling, can lead to tedious repetition of previously existing work. The significant amount of time that is often spent on locating samples would be better used for performing experiments. Moreover, expensive storage space is wasted, since samples are often not labeled properly and cannot be identified. Even if a label is given, it usually does not include a standardized minimal amount of information that allows unambiguous identification of the materials or the experiments they were derived from. Numerous commercial and open-source solutions have been developed in an attempt to overcome these problems.

Although solutions are offered by commercial companies like LabVantage, most academic laboratories find it difficult to afford the license costs, which usually rise with additional users and technical features. The focus of this paper is thus open-source systems.

As Table 1 shows, open-source laboratory information management systems (LIMS) are often customized towards specific types of biomaterials or research data, as for instance genotyping, protein production, protein-protein-interaction, 2D gel electrophoresis, or protein crystallography data. Some generic LIMS target specific laboratory tasks, such as sample management, laboratory work-flows and protocols, documentation, management of lab stocks, or clinical studies. Further solutions exist for molecular genetics and the creation of vector libraries. There is, however, no dedicated LIMS for the management of large vector construct and cell line libraries. At our Lundbeck Foundation Center of Excellence in Nanomedicine (NanoCAN) at the University of Southern Denmark in Odense such large-scale libraries need to be handled efficiently (see Mollenhauer et al. for a short overview about our work). This motivated us to develop a novel open-source LIMS platform: OpenLabFramework (OLF).

Table 1. Some examples of existing browser-based LIMS solutions. Corresponding project URLs can be found in Supplemental Table 1. Project name Ref. Main purpose Built with MMP-LIMS Genome mapping in maize Java AGL-LIMS Genotyping work-flow Java SMS Gene mutation screening & biobanking Java PiMS Sample & experiment tracking for protein production Java ProteinTracker Protein production & purification Java PARPs Database Protein-protein interaction data and data-mining Perl/Java LIPAGE 2D gel electrophoresis based proteomics PHP LISA Protein crystallography PHP EnzymeTracker Data analysis, sample management, spreadsheet functionality PHP FreeLIMS Sample management, reports Java YourLabData Sample tracking and lab notebook - Open-LIMS Experimental work-flow, sample & document management PHP OpenFreezer Sample management & tracking PHP/Python iLAP Data management, analysis, experimental protocol design Java SIGLa Customized experimental work-flows Java Bika Whole lab work-flow for clinical studies Python MicroGen Mircoarray information and work-flow MS-Access LabLog Project documentation Java LabStoRe Chemical lab stocks PHP SIMBioMS Linking experimental, patient, and high-throughput data PHP MolabIS Molecular genetics data Perl

Notes

This presentation is faithful to the original, with only a few minor changes to presentation. In some cases important information was missing from the references, and that information was added.