Sep 9, 2016 - Image Source: https: // awritersden. files. wordpress. com. Towards ... A normal experimental setup. Example: ... Extended the OMERO server.
Towards Reproducibility of Microscopy Experiments Sheeba Samuel, Frank Taubert, Daniel Walther, Birgitta K¨onig-Ries, H. Martin B¨ ucker Institute for Computer Science Friedrich-Schiller Universit¨ at Jena http://www.receptorlight.uni-jena.de/
RepScience, September 9, 2016
Towards Reproducibility of Microscopy Experiments
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Motivation
An environment facilitating reproducibility for microscope experiments for Collaborative Research Center ReceptorLight (CRC). Aim: A data management platform for the CRC that safekeeps the data produced in the individual subprojects allows sharing of data among subprojects supports data reuse by other CRC scientists
Conventional way to use (analog) lab notebooks in the scientific community.
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How good are we at performing experiments?
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A normal experimental setup
Example: confocal patch clamp fluorometry (cPCF) experiment [1].
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Repeatability and Reproducibility of Experiments
Repeatability: capability of getting the same results carried out by the same experimenter using the same conditions of measurement. [2] Reproducibility: capability of getting the same results carried out by an independent experimenter using different conditions of measurement.
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Why Reproducibility is so important? Studies show only 10% of published science articles are reproducible. Scientists at the pharmaceutical company, Bayer, could reproduce only 14 out of 67 projects. [3] Studies conducted by the biotech company, Amgen, reveals that only 6 of the 53 studies were reproduced in Cancer Research. [4] The US government gives nearly $31 billion every year in science funding through NIH.
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How to achieve reproducibility of experiments?
Data and methods used for the experiment Workflow Provenance User annotations, labeling and tagging Software used, data technologies and version control Experiment environment parameters Data sharing, archiving and distribution Data storage Machine-readable integration and documentation [5]
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Related Work
Scientific workflow management system [6] Pimentel’s work [8] PROV-O [9] OMERO [10]
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Current Prototype and First Results
Towards Reproducibility of Microscopy Experiments
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Current Prototype and First Results
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Current Prototype and First Results
Documentation of all necessary information regarding experiments while performing them. The data model consists of various input classes like Experiment Plasmid Protein Chemical Substance Vector
Sharing and Improvement of this information by other scientists. Extensible system - integration with less effort.
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Current Prototype and First Results
The current prototype is developed based on OMERO. Features of OMERO Hierarchical organization of data Projects, Datasets and images Extended the OMERO server An “Experiment“ tab to document and view all the information regarding an experiment.
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Current Prototype and First Results
Towards Reproducibility of Microscopy Experiments
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Current Prototype and First Results
Developed a desktop client to deploy on workstations without internet. In the desktop client, a researcher while conducting an experiment can input all the data. upload the images, files and measurements obtained from the devices during the experiment to the server when an internet connection is available.
Minimizing the loss of data naturally occurring when recording these things from memory.
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Current Prototype and First Results
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Features of Current Prototype
Retreive and correlate all measurements obtained from different devices belonging to one experiment. View the images and data of the experimental protocol at one place organized in a hierarchical manner. Provides access control. Different roles and permissions to restrict modification of data. Sharing of data between users. The version management of the scientific data. Track modifications Correction of mistakes revert back to previous versions if needed
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First Results
Manual validation by a group of biological scientists. Being continuously extended and improved with the feedback received from the scientists.
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Conclusion
Reproducibility of scientific experiments. Ensure validation and correctness of biological scientist’s work. A software platform supporting reproducibility. Can spend more time on their biological work rather than focusing on writing scripts and worrying about the storage of their huge data. Specifically designed keeping in mind the requirements of biological scientists working in the field of microscopy.
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Future Work
Addition of more attributes to the current set based on the specific requirements of further working groups of biological scientists. To represent data entered by the scientists in a machine-readable format. Integration with existing tools to export the provenance information from the system.
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Acknowledgement
This research is partially supported by the “Deutsche Forschungsgemeinschaft“ (DFG) of the CRC “High-end light microscopy elucidates membrane receptor function ReceptorLight“. We thank Christoph Biskup and Kathrin Groeneveld from the Biomolecular Photonics Group at University Hospital Jena, Germany, for providing the requirements to develop the proposed approach and validating the system.
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Thanks
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References I Biskup, C., Kusch, J., Schulz, E., Nache, V., Schwede, F., Lehmann, F., Hagen, V., Benndorf, K.: Relating ligand binding to activation gating in cnga2 channels. Nature 446(7134), 440–443 (2007) Taylor, B., Kuyatt, C.: Nist technical note 1279: Guidelines for evaluating and expressing the uncertainty of nist measurements results. National Institute of Standards and Technology, Washington DC (1994) Prinz, F., Schlange, T., Asadullah, K.: Believe it or not: how much can we rely on published data on potential drug targets? Nature reviews Drug discovery 10(9), 712–712 (2011) Begley, C.G., Ellis, L.M.: Drug development: Raise standards for preclinical cancer research. Nature 483(7391), 531–533 (2012)
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References II Goecks, J., Nekrutenko, A., Taylor, J.: Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. Genome biology 11(8), 1 (2010) Curcin, V., Ghanem, M.: Scientific workflow systems-can one size fit all? In: 2008 Cairo International Biomedical Engineering Conference, pp. 1–9 (2008). IEEE Abramson, D., Bethwaite, B., Dinh, M.N., Enticott, C., Firth, S., Garic, S., Harper, I., Lackmann, M., Nguyen, H., Ramdas, T., et al.: Virtual microscopy and analysis using scientific workflows. In: e-Science, 2009. e-Science’09. Fifth IEEE International Conference On, pp. 239–246 (2009). IEEE
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References III Pimentel, J.F., Dey, S., McPhillips, T., Belhajjame, K., Koop, D., Murta, L., Braganholo, V., Lud¨ascher, B.: Yin & yang: demonstrating complementary provenance from noworkflow & yesworkflow. In: International Provenance and Annotation Workshop, pp. 161–165 (2016). Springer Lebo, T., Sahoo, S., McGuinness, D., Belhajjame, K., Cheney, J., Corsar, D., Garijo, D., Soiland-Reyes, S., Zednik, S., Zhao, J.: Prov-o: The prov ontology. W3C Recommendation 30 (2013) Allan, C., Burel, J.-M., Moore, J., Blackburn, C., Linkert, M., Loynton, S., MacDonald, D., Moore, W.J., Neves, C., Patterson, A., et al.: Omero: flexible, model-driven data management for experimental biology. Nature methods 9(3), 245–253 (2012)
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