Journal:Efficient sample tracking with OpenLabFramework

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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 A high turn-over of academic staff coupled with maintenance of individual files that are often locked or outdated, as well as inconsistent nomenclature and labeling, can lead to tedious repetition of previously existing work.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[1][2][3], protein production[4][5], protein-protein-interaction[6], 2D gel electrophoresis[7], or protein crystallography[8] data. Some generic LIMS target specific laboratory tasks, such as sample management[3][9][10], laboratory work-flows and protocols[2][11][12][13], documentation, management of lab stocks, or clinical studies.[14] Further solutions exist for molecular genetics and the creation of vector libraries.[15] 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.[16] for a short overview about our work). This motivated us to develop a novel open-source LIMS platform: OpenLabFramework (OLF).

References

  1. ↑ Sanchez-Villeda, H.; Schroeder, S.; Polacco, M. et al. (2003). "Development of an integrated laboratory information management system for the maize mapping project". Bioinformatics 19 (16): 2022-2030. doi:10.1093/bioinformatics/btg274. PMID 14594706. 
  2. ↑ 2.0 2.1 Jayashree, B.; Reddy, P.T.; Leeladevi, Y. et al. (2006). "Laboratory information management software for genotyping workflows: Applications in high throughput crop genotyping". BMC Bioinformatics 7: 383. doi:10.1186/1471-2105-7-383. PMC PMC1559653. PMID 16914063. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1559653. 
  3. ↑ 3.0 3.1 Voegele, C.; Alteyrac, L.; Caboux, E. et al. (2010). "A sample storage management system for biobanks". Bioinformatics 26 (21): 2798-2800. doi:10.1093/bioinformatics/btq502. PMID 20807837. 
  4. ↑ Morris, C.; Pajon, A.; Griffiths, S.L. et al. (2011). "The Protein Information Management System (PiMS): A generic tool for any structural biology research laboratory". Acta Crystallographica Section D 67 (4): 249–260. doi:10.1107/S0907444911007943. PMC PMC3069740. PMID 21460443. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3069740. 
  5. ↑ Ponko, S.C.; Bienvenue, D. (2012). "ProteinTracker: An application for managing protein production and purification". BMC Research Notes 5: 224. doi:10.1186/1756-0500-5-224. PMC PMC3436699. PMID 22574679. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3436699. 
  6. ↑ Droit, A.; Hunter, J.M.; Rouleau, M. et al. (2007). "PARPs database: A LIMS systems for protein-protein interaction data mining or laboratory information management system". BMC Bioinformatics 8: 483. doi:10.1186/1471-2105-8-483. PMC PMC2266781. PMID 18093328. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2266781. 
  7. ↑ Morisawa, H.; Hirota, M.; Toda, T. (2006). "Development of an open source laboratory information management system for 2-D gel electrophoresis-based proteomics workflow". BMC Bioinformatics 7: 430. doi:10.1186/1471-2105-7-430. PMC PMC1599757. PMID 17018156. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1599757. 
  8. ↑ Haebel, P.W.; Arcus, V.L.; Baker, E.N.; Metcalf, P. (2001). "LISA: An intranet-based flexible database for protein crystallography project management". Acta Crystallographica Section D 57 (Pt 9): 1341-1343. doi:10.1107/S0907444901009295. PMID 11526339. 
  9. ↑ Triplet, T.; Butler, G. (2012). "The EnzymeTracker: An open-source laboratory information management system for sample tracking". BMC Bioinformatics 13 (15): 1341-1343. doi:10.1186/1471-2105-13-15. PMC PMC3353834. PMID 22280360. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3353834. 
  10. ↑ Olhovsky, M.; Williton, K.; Dai, A.Y. et al. (2011). "OpenFreezer: A reagent information management software system". Nature Methods 8 (8): 612–613. doi:10.1038/nmeth.1658. PMID 21799493. 
  11. ↑ Stocker, G.; Fischer, M.; Rieder, D. et al. (2009). "iLAP: a workflow-driven software for experimental protocol development, data acquisition and analysis". BMC Bioinformatics 10: 390. doi:10.1186/1471-2105-10-390. PMC PMC2789074. PMID 19941647. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2789074. 
  12. ↑ Melo, Alexandre; Alessandra Faria-Campos; Daiane M DeLaat; Rodrigo Keller; Vinícius Abreu; Sérgio Campos (2010). "SIGLa: an adaptable LIMS for multiple laboratories". BMC Genomics 11 (Suppl 5): S8. doi:10.1186/1471-2164-11-S5-S8. PMC PMC3045801. PMID 21210974. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3045801. 
  13. ↑ Burgarella, S.; Cattaneo, D.; Pinciroli, F.; Masseroli, M. (2005). "MicroGen: a MIAME compliant web system for microarray experiment information and workflow management". BMC Bioinformatics 6 (Suppl 4): S6. doi:10.1186/1471-2105-6-S4-S6. PMC PMC1866379. PMID 16351755. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1866379. 
  14. ↑ Krestyaninova, M.; Zarins, A.; Viksna, J. et al. (2009). "A system for information management in biomedical studies – SIMBioMS". Bioinformatics 25 (20): 2768-2769. doi:10.1093/bioinformatics/btp420. PMC PMC2759553. PMID 19633095. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2759553. 
  15. ↑ Truong, C.V.C.; Groeneveld, L.F.; Morgenstern, B.; Groeneveld, E. (2011). "MolabIS - An integrated information system for storing and managing molecular genetics data". BMC Bioinformatics 12: 425. doi:10.1186/1471-2105-12-425. PMC PMC3268772. PMID 22040322. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3268772. 
  16. ↑ Mollenhauer, J.; Stamou, D.; Flyvbjerg, A. et al. (2010). "David versus Goliath". Nanomedicine 6 (4): 504–509. doi:10.1016/j.nano.2010.04.002. PMID 20417315. 

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.

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