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Sharing the knowledge of electrophysiology data

Sharing the knowledge of electrophysiology data. Phillip Lord, Frank Gibson and the CARMEN Consortium. “In the standard model, one collects data, publishes a paper or papers and then gradually loses the original dataset.”

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Sharing the knowledge of electrophysiology data

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  1. Sharing the knowledge of electrophysiology data Phillip Lord, Frank Gibson and the CARMEN Consortium

  2. “In the standard model, one collects data, publishes a paper or papers and then gradually loses the original dataset.” THE NEW KNOWLEDGE ECONOMY AND SCIENCE AND TECHNOLOGY POLICYGeoffrey Bowker, University of California, San Diego

  3. The need for clear metadata • Most neurosciences data is relative simple in structure • But often contextually complex • Sometimes associated with behavioural features

  4. Neuroscience spike data • The raw data is normally a waveform • But, advances in instrumentation • High-throughput methods • But what is the experiment for? • What stimulus is the organism/tissue receiving? • Which channel is which • The data sets being produced are (reasonably) large (10’s of Gb, or 1Tb in three months)

  5. http://en.wikipedia.org/wiki/Image:Brain_090407.jpg istockphoto.com Information Extraction • How do we get extract the information? http://en.wikipedia.org/wiki/Image:ATTtelephone-large.jpg

  6. Multi-Author data From Katherine James, NCL

  7. Author PMID Type Size 1 Davierwala et al 16155567 Synthetic_Lethality 627 2 Krogan et al 14759368 Affinity_Capture-MS 164 3 Hazbun et al 14690591 Affinity_Capture-MS 3210 4 Gavin et al 11805826 Affinity_Capture-MS 3596 5 Ho et al 11805837 Affinity_Capture-MS 733 6 Ito et al 11283351 Two-hybrid 275 7 Tong et al 11743205 Synthetic_Lethality 3411 8 Tong et al 14764870 Synthetic_Lethality 823 9 Uetz et al 10688190 Two-hybrid 1941 10 Miller et al 16093310 Two-hybrid 104 11 Lindstrom et al 12556496 Affinity_Capture-MS 134 12 Nissan et al 12374754 Affinity_Capture-MS 456 13 Grandi et al 12150911 Affinity_Capture-MS 150 14 Ohi et al 11884590 Affinity_Capture-MS 630 15 Krogan et al 14690608 Affinity_Capture-MS 370 16 Sanders et al 12052880 Affinity_Capture-MS 102 17 Baetz et al 14729968 Affinity_Capture-MS 258 18 Fromont-Racine et al 10900456 Two-hybrid 160 19 Fromont-Racine et al 9207794 Two-hybrid 182 20 Drees et al 11489916 Two-hybrid 232 21 Tong et al 11743162 Affinity_Capture-Western 125 22 Allen et al 11387327 Affinity_Capture-MS 116 23 Panse et al 15292183 Affinity_Capture-MS 181 24 Krogan et al 15353583 Affinity_Capture-MS 113 25 Kong et al 15563457 Protein-peptide 157 26 Hannich et al 15590687 Two-hybrid 134 27 Newman et al 11087867 Two-hybrid 464 28 Zhao et al 15766533 Reconstituted_Complex 125 29 Millson et al 15879519 Affinity_Capture-Western 369 30 Ubersax et al 14574415 Biochemical_Activity 138 31 Ingvarsdottir et al 15657441 Affinity_Capture-Western 175 32 Lesage et al 15166135 Synthetic_Lethality 292 33 Lesage et al 15715908 Synthetic_Lethality 323 34 Pan et al 15525520 Synthetic_Lethality 124 35 Loeillet et al 15725626 Synthetic_Lethality 214 36 Daniel et al 16157669 Synthetic_Lethality 4535 37 Pan et al 16487579 Synthetic_Growth_Defect 7076 38 Krogan et al 16554755 Affinity_Capture-MS 6531 39 Gavin et al 16429126 Affinity_Capture-MS 107 40 Milgrom et al 16118188 Synthetic_Lethality 215 41 Measday et al 16172405 Two-hybrid 477 42 Graumann et al 14660704 Affinity_Capture-MS 4179 43 Ptacek et al 16319894 Biochemical_Activity 103 44 Frazier et al 16476776 Co-fractionation 290 45 Ye et al 16729061 Synthetic_Rescue 3416 46 Schuldiner et al 16269340 Phenotypic_Enhancement 14421 47 Collins et al 17314980 Phenotypic_Enhancement 9064 48 Collins et al 17200106 Affinity_Capture-MS 117 49 Aronova et al 17507646 Co-fractionation 576 50 Wong et al 17634282 Two-hybrid 234

  8. How do we represent… In silico Analysis Derived data Laboratory Experiments

  9. View from microarrays Content Standard – Minimal Information MO -- Terminology MAGE -- Structure From the MGED society

  10. The CARMEN approach Content Standard – Minimal Information about a Neuroscience Investigation OBI – Ontology for Biomedical Investigations FuGE -- Structure

  11. Minimal Information About a Neuroscience Investigation:What do I have to tell you, for you to understand what I did? • Subdivided as; • Contact and context • Study subject • Recording location • Task • Stimulus • Behavioural event • Recording • Time series data Study inputs Assay inputs Assay procedures Data

  12. MINI in relation to the life-science reporting requirements

  13. MIAOWS • Describe essential metadata for your analysis code • What does it do? Objective • What type of input does it need • What type of output does it produce • If this information is not described, your code is of most value to yourself and much less value to the community

  14. The CARMEN approach Content Standard – Minimal Information about a Neuroscience Investigation OBI – Ontology for Biomedical Investigations FuGE -- Structure

  15. Functional Genomics Experiment (FuGE)How do I tell you, for you to understand? • Model of common components in science investigations, such as materials, data, protocols, equipment and software. • Provides a framework for capturing complete laboratory workflows, enabling the integration of pre-existing data formats.

  16. Model Driven Architecture -- SyMBA UML XML Java objects database

  17. FuGE community of users • MGED (transcriptomics) • Proteomics Standards Initiative • Metabolomics Standards Initiative (NMR and sample processing groups) • Genomics Standards Consortium (MIGS) • CARMEN, Code Analysis, Repository and Modelling for e-Neuroscience • Flow Informatics and Computational Cytometry Society • MIARE: Minimum Information About an RNAi Experiment

  18. Functional Genomics Experiment (FuGE)How do I tell you, for you to understand? • Model of common components in science investigations, such as materials, data, protocols, equipment and software. • Provides a framework for capturing complete laboratory workflows, enabling the integration of pre-existing data formats.

  19. The CARMEN approach Content Standard – Minimal Information about a Neuroscience Investigation OBI – Ontology for Biomedical Investigations FuGE -- Structure

  20. OBI – Ontology of Biomedical Investigations Diversity communities from Nutrition to Metabolomics, from Environmental to genomics to Immunology, Imaging and Data analysis OBI branches: development work Protocol application branch Data Transformation branch Instrument branch Biomaterial branch Role branch Function branch Molecular entities Digital entity branch Adapted from Philippe Rocca-Serra, 2008

  21. Summary • We are generating metadata “standards” for neurosciences • We are following a well-trodden path from bioinformatics • We adopted FuGE and have built MINI

  22. Future Work • More neurosciences experimental datatypes. • Minimal Information about a Service • Describe analysis software as well as lab experiments. • Outreach!

  23. Acknowledgements MINI: Frank Gibson, Paul G Overton, Tom V Smulders, Simon R Schultz, Stephen J Eglen, Colin D Ingram, Stefano Panzeri, Phil Bream, Evelyne Sernagor, Mark Cunningham, Christopher Adams, Christoph Echtermeyer, Jennifer Simonotto, Marcus Kaiser, Daniel C Swan, Martyn Fletcher, Phillip Lord CISBAN: Anil Wipat (PI), Allyson Lister (Research Associate), FuGE: The FuGE consortium OBI: The OBI consortium CARMEN: http://www.carmen.org.uk SyMBA: http://symba.sourceforge.net FuGE: http://fuge.sourceforge.net OBI: http://obi.sourceforge.net

  24. CARMEN Consortium

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