Integrating the Allen Brain Institute Cell Types Database into Automated Neuroscience Workflow

Software Original Article

Abstract

We developed software tools to download, extract features, and organize the Cell Types Database from the Allen Brain Institute (ABI) in order to integrate its whole cell patch clamp characterization data into the automated modeling/data analysis cycle. To expand the potential user base we employed both Python and MATLAB. The basic set of tools downloads selected raw data and extracts cell, sweep, and spike features, using ABI’s feature extraction code. To facilitate data manipulation we added a tool to build a local specialized database of raw data plus extracted features. Finally, to maximize automation, we extended our NeuroManager workflow automation suite to include these tools plus a separate investigation database. The extended suite allows the user to integrate ABI experimental and modeling data into an automated workflow deployed on heterogeneous computer infrastructures, from local servers, to high performance computing environments, to the cloud. Since our approach is focused on workflow procedures our tools can be modified to interact with the increasing number of neuroscience databases being developed to cover all scales and properties of the nervous system.

Keywords

Computer simulation Allen brain institute Database NEURON Computational neuroscience NeuroManager 

References

  1. Git (2017). Website. https://git-scm.com/.
  2. ABI (2015a). Allen cell types database - overview. technical report, allen brain institute. http://help.brain-map.org/download/attachments/8323525/CellTypesOverview.pdf?version=1&modificationDate=1456188760121.
  3. ABI (2015b). Allen cell types database - electrophysiology. technical report, allen brain institute. http://help.brain-map.org/download/attachments/8323525/EphysOverview.pdf?version=1&modificationDate=1456188786670.
  4. ABI (2015c). Allen cell types database - morphology. technical report, allen brain institute. http://help.brain-map.org/download/attachments/8323525/MorphOverview.pdf?version=1&modificationDate=1456525256645.
  5. ABI (2015d). Allen cell types database - glif models. technical report, allen brain institute. http://help.brain-map.org/download/attachments/8323525/GLIFModels.pdf?version=1&modificationDate=1456188812960.
  6. ABI (2015e). Allen cell types database - biophysical modeling - perisomatic. technical report, allen brain institute. http://help.brain-map.org/download/attachments/8323525/BiophysModelPeri.pdf?version=1&modificationDate=1456188760131.
  7. ABI (2017a). Allen brain institute cell types database application programmer’s interface. http://help.brain-map.org/display/celltypes/API.
  8. ABI (2017b). Allen brain institute cell types webpage. http:/celltypes.brain-map.org.
  9. ABI (2017c). Allen brain atlas portal - news and upyears. http://www.brain-map.org/announcements/index.
  10. ABI (2017d). Allen brain institute restful model access (RMA). http://help.brain-map.org/pages/viewpage.action?pageId=5308449.
  11. ABI (2017e). Allen brain institute allen brain atlas software development kit. http://alleninstitute.github.io/AllenSDK/.
  12. ABI (2017f). Allen brain institute software development kit ephys code webpage. http://alleninstitute.github.io/AllenSDK/allensdk.ephys.html.
  13. ABI (2017g). Allen brain institute SDK ephys features. http://help.brain-map.org/display/celltypes/API#API-ephys_features.
  14. Antolík, J., & Davison, A.P. (2013). Integrated workflows for spiking neuronal network simulations. Frontiers in Neuroinformatics, 7(34), 1–15.Google Scholar
  15. Autism Brain Imaging Data Exchange (2017). Autism brain imaging data exchange I – ABIDE I. http://fcon_1000.projects.nitrc.org/indi/abide/abide_I.html.
  16. Baek, K., Shim, W.H., Jeong, J., Radhakrishnan, H., Rosen, B.R., Boas, D., Franceschini, M., Biswal, B.B., & Kim, Y.R. (2016). Layer-specific interhemispheric functional connectivity in the somatosensory cortex of rats: resting state electrophysiology and fMRI studies. Brain Structure and Function, 221(5), 2801–2815.CrossRefPubMedGoogle Scholar
  17. Bargmann, C., Newsome, W., Anderson, A., Brown, E., Deisseroth, K., Donoghue, J., MacLeish, P., Marder, E., Normann, R., Sanes, J., & et al (2014). Brain 2025: a scientific vision. Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Working Group Report to the Advisory Committee to the Director, NIH. https://www.braininitiative.nih.gov/2025/.
  18. Chacon, S. (2014). Pro Git 2. Apress 2nd edn.Google Scholar
  19. Davison, A. (2012). Automated capture of experiment context for easier reproducibility in computational research. Computing in Science & Engineering, 14(4), 48–56.CrossRefGoogle Scholar
  20. Davison, A.P., Hines, M.L., & Muller, E. (2009). Trends in programming languages for neuroscience simulations. Frontiers in Neuroscience, 3(3), 374–380. doi:10.3389/neuro.01.036.2009.CrossRefPubMedPubMedCentralGoogle Scholar
  21. Delorme, A., & Makeig, S. (2004). EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods, 134(1), 9–21. ISSN 0165-0270. doi:10.1016/j.jneumeth.2003.10.009.CrossRefPubMedGoogle Scholar
  22. Englitz, B., Sorenson, M.D., & Shamma, S.A. (2013). MANTA — an open-source, high density electrophysiology recording suite for MATLAB. Frontiers in Neural Circuits, 7, 69. doi:10.3389/fncir.2013.00069, http://journal.frontiersin.org/article/10.3389/fncir.2013.00069/full.CrossRefPubMedPubMedCentralGoogle Scholar
  23. Felice, C.J., Albarracín, A.L., Farfán, F.D., Coletti, M.A., & Teruya, P.Y. (2016). Electrophysiology for biomedical engineering students. Advances in Physiology Education, 40, 402– 409.CrossRefPubMedGoogle Scholar
  24. Folk, M., Heber, G., Koziol, Q., Pourmal, E., & Robinson, D. (2011). An overview of the HDF5 technology suite and its applications. In Proceedings of the EDBT/ICDT 2011 Workshop on Array Databases, AD ’11. ISBN 978-1-4503-0614-0. doi:10.1145/1966895.1966900 (pp. 36–47). New York, NY, USA: ACM.CrossRefGoogle Scholar
  25. Fox, P., & Laird, A. (2017). BrainMap Website. http://brainmap.org/.
  26. George Mason University (2017). NeuroMorpho.Org. http://neuromorpho.org/index.jsp.
  27. Gleeson, P., Steuber, V., & Silver, R.A. (2007). neuroConstruct: A Tool for modeling networks of neurons in 3d space. Neuron, 54(2), 219–235.CrossRefPubMedPubMedCentralGoogle Scholar
  28. Grillner, S., Ip, N., Koch, C., Koroshetz, W., Okano, H., Polachek, M., Poo, M.-m, & Sejnowski, T.J. (2016). Worldwide initiatives to advance brain research. Nature Neuroscience, 19(9), 1118– 1122.CrossRefPubMedGoogle Scholar
  29. Günay, C. (2007). PANDORA Neural Analysis Toolbox. https://senselab.med.yale.edu/simtooldb/.
  30. Günay, C. (2012). Plotting and analysis for neural database-oriented research applications (PANDORA) toolbox — User’s and Programmer’s Manual Rev 1293. https://senselab.med.yale.edu/SimToolDB/showTool.cshtml?tool=112112&file=%5cpandora-1:3b%5cdoc%5cprog-manual:pdf.
  31. Günay, C., Edgerton, J.R., Li, S., Sangrey, T., Prinz, A.A., & Jaeger, D. (2009). Database analysis of simulated and recorded electrophysiological datasets with PANDORA’s toolbox. Neuroinformatics, 7(2), 93–111. doi:10.1007/s12021-009-9048-z.CrossRefPubMedPubMedCentralGoogle Scholar
  32. Hines, M.L., Davison, A.P., & Muller, E. (2009). NEURON and Python. Frontiers in Neuroinformatics, 3, 1. doi:10.3389/neuro.11.001.2009.CrossRefPubMedPubMedCentralGoogle Scholar
  33. International Neuroinformatics Coordinating Facility (2017). INCF Website. https://www.incf.org/.
  34. Lawhern, V., Hairston, W.D., & Robbins, K. (2013). DETECT: A MATLAB toolbox for event detection and identification in time series, with applications to artifact detection in EEG signals. PLOS ONE, 8(4), 1–13.Google Scholar
  35. Lytton, W.W. (2006). Neural query system. Neuroinformatics, 4(2), 163–175.CrossRefPubMedPubMedCentralGoogle Scholar
  36. Mathworks (2017). MATLAB HDF5 files webpage. https://www.mathworks.com/help/matlab/hdf5-files.html.
  37. MathWorks (2017). MATLAB database toolbox. https://www.mathworks.com/products/database.html.
  38. Mattioni, M., Cohen, U., & Le Novere, N. (2012). Neuronvisio: a graphical user interface with 3d capabilities for neuron. Frontiers in Neuroinformatics, 6(20). ISSN 1662-5196. doi:10.3389/fninf.2012.00020. http://www.frontiersin.org/neuroinformatics/10.3389/fninf.2012.00020/abstract.
  39. McDougal, R.A. , Morse, T.M. , Carnevale, T., Marenco, L., Wang, R., Migliore, M., Miller, P.L., Shepherd, G.M. , & Hines, M.L. (2017). Twenty years of ModelDB and beyond: building essential modeling tools for the future of neuroscience. Journal of Computational Neuroscience, 42(1), 1–10. ISSN 1573-6873. doi:10.1007/s10827-016-0623-7.CrossRefPubMedGoogle Scholar
  40. Miyasho, T., Takagi, H., Suzuki, H., Watanabe, S., Inoue, M., Kudo, Y., & Miyakawa, H. (2001). Low-threshold potassium channels and a low-threshold calcium channel regulate Ca2+ spike firing in the dendrites of cerebellar Purkinje neurons: a modeling study. Brain Research, 891(1–2), 106–115.CrossRefPubMedGoogle Scholar
  41. Muller, E., Bednar, J.A., Diesmann, M., Gewaltig, M.-O., Hines, M., & Davison, A.P. (2015). Python in neuroscience. Frontiers in Neuroinformatics, 9, 11.CrossRefPubMedPubMedCentralGoogle Scholar
  42. MySQL (2017a). MySQL website. https://www.mysql.com/.
  43. MySQL (2017b). MySQL Connector/Python Developer Guide. https://dev.mysql.com/doc/connector-python/en/.
  44. MySQL (2017c). MySQL Workbench. https://www.mysql.com/products/workbench/.
  45. NeurodataWithoutBorders (2016). NWB file format specification version 1.0.3. https://github.com/NeurodataWithoutBorders/specification.
  46. NSG (2017a). Neuroscience gateway website. https://www.nsgportal.org/.
  47. NSG (2017b). NSG REST Api (NSG-R) website. https://www.nsgportal.org/guide.html.
  48. NWB-CN Project (2015). Neurodata without borders — computational neuroscience project. http://crcns.org/NWB.
  49. Schrouff, J., Rosa, M.J., Rondina, J.M., Marquand, A.F., Chu, C., Ashburner, J., Phillips, C., Richiardi, J., & Mourão-miranda, J. (2013). PRoNTo Pattern recognition for neuroimaging toolbox. Neuroinformatics, 11(3), 319–337. doi:10.1007/s12021-013-9178-1.CrossRefPubMedPubMedCentralGoogle Scholar
  50. SenseLab (2017). ModelDB Website. https://senselab.med.yale.edu/ModelDB/default.cshtml.
  51. Shamlo, N., Mullen, T., Kothe, C., Su, K.M., & Robbins, K.A. (2015). The PREP Pipeline: Standardized preprocessing for large-scale EEG analysis. Frontiers in Neuroinformatics, 9(16), 1662–5196. ISSN 10.3389/fninf.2015.00016, http://www.frontiersin.org/neuroinformatics/10.3389/fninf.2015.00016/abstract.Google Scholar
  52. Stockton, D., & Santamaria, F. (2017). NeuroManager Website. https://github.com/SantamariaLab/NeuroManager.
  53. Stockton, D.B., & Santamaria, F. (2016). Automating NEURON simulation deployment in cloud resources. Neuroinformatics. ISSN 1559-0089. doi:10.1007/s12021-016-9315-8.
  54. Stockton, D.B., & Santamaria, F. (2015). NeuroManager: A workflow analysis based simulation management engine for computational neuroscience. Frontiers in Neuroinformatics, 9(24). ISSN 1662-5196. doi:10.3389/fninf.2015.00024, http://www.frontiersin.org/neuroinformatics/10.3389/fninf.2015.00024/abstract.
  55. Teka, W., Marinov, T.M., & Santamariam, F. (2014). Neuronal spike timing adaptation described with a fractional leaky integrate-and-fire model. PLos Computational Biology, 10 (3), e1003526. doi:10.1371/journal.pcbi.1003526.CrossRefPubMedPubMedCentralGoogle Scholar
  56. Teka, W., Stockton, D.B., & Santamaria, F. (2016). Power-law dynamics of membrane conductances increase spiking diversity in a Hodgkin–Huxley model. PLos Computational Biology, 12(3). 1–23. doi:10.1371/journal.pcbi.1004776.
  57. Tripathy, S.J., & Gerkin, R.C. (2015). NeuroElectro Project, (pp. 1915–1916). New York, NY: Springer New York. ISBN 978-1-4614-6675-8. doi:10.1007/978-1-4614-6675-8_477.Google Scholar
  58. Tripathy, S.J., Savitskaya, J., Burton, S.D., Urban, N.N., & Gerkin, R.C. (2014). Neuroelectro: a window to the world’s neuron electrophysiology data. Frontiers in neuroinformatics, 8, 1–11.Google Scholar
  59. Van Geit, W., Gevaert, M., Chindemi, G., Rössert, C., Courcol, J.-D., Muller, E.B., Schürmann, F., Segev, I., & Henry, M. (2016). BluePyOpt: Leveraging Open source software and cloud infrastructure to optimise model parameters in neuroscience. Frontiers in Neuroinformatics, 10.Google Scholar
  60. Vidaurre, C., Sander, T.H., & Schlögl, A. (2011). BioSig: the free and open source software library for biomedical signal processing. Computational Intelligence and Neuroscience.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Department of Biomedical EngineeringThe University of Texas at San AntonioSan AntonioUSA
  2. 2.Department of BiologyThe University of Texas at San AntonioSan AntonioUSA

Personalised recommendations