Automated analysis of neuronal morphologies via python and (collaborative) open source databases

Abstract

Event Abstract Back to Event Automated analysis of neuronal morphologies via python and (collaborative) open source databases Nina Hubig1, Hans J. Ohlbach1, Thomas Wachtler2 and Philipp L. Rautenberg2* 1 Ludwig-Maximilians-Universität München, Institute for Computer Science, Germany 2 Ludwig-Maximilians-Universität München, Department of Biology, Germany Neuronal morphology plays a fundamental role for the information processing capabilities of neurons. To fully exploit the information gained by neuron reconstructions, it is crucial for neuroscientific researchers to analyze a large amount of data in a fast, effective, and automated way. We developed a new system for the analysis of morphological data that is based on a high-level object-oriented programming language that was integrated into an object-relational database management system. This approach enables to connect automatized data analysis procedures directly to the stored data. In our (experimental) system we used Python as object oriented language which is commonly used in the neuroscientific community. It is a versatile language that can be easily applied to solve several tasks that research laboratories face almost every day: data manipulation, biological data retrieval and parsing, automation, or simulations of single cells or neural networks [1]. As a database system we used PostgreSQL [2]. PostgreSQL is a powerful, open source object-relational database system. Its SQL implementation strongly conforms to the ANSI-SQL:2008 standard and therefore is widely compatible with other database systems. For efficient data access, we defined a data schema representing SWC-specifications [3]. Based on this schema, we implemented views and analysis functions in SQL, to enable data analysis directly within the database. Analyses were performed using database and computing services at the German INCF Node (www.g-node.org). Different properties of the morphology of neurons, such as total length, number of branch points, surface area, and volume were calculated and compared across neurons in the database, taking into account other properties, like age or species of the corresponding animal. For example, we found that the ratio between surface area and volume is an important measurement of the influence of a neurons morphology on electrophysiological properties. The results can be accessed from the database for visualization by various well-known software-systems like MATLAB or OpenOffice. Our results are presented in a portable and easy way via database access and web interface. This yields the requisite for efficient data analysis: Morphologies can be easily exchanged and thus a great variety of data analysis can be performed in a systematic and non-redundant fashion. This eliminates the need of doing the same analysis by hand again for each morphology. It also in turn facilitates analysis and visualization of results. The method is not restricted to analysis of neuron morphology, but could also be applied for the analysis of physiological data. Acknowledgments: The German INCF Node is supported byBMBF grant 01GQ0801