Yoshihiro Okumura

Parallelized analysis of spatial gene expression patterns by database integration

We developed a computational framework for automated integration of a large number of two-dimensional (2D) images with three-dimensional (3D) image datasets located in the standard 3D coordinate. We applied the framework to 2,810 para-sagittal sectioned mouse brain 2D images of in situ hybridization (ISH), archived in the BrainTx database (http://www.cdtdb.neuroinf.jp). We registered the ISH images into the mouse standard coordinate space for MR images, Waxholm space (WHS, https://www.nitrc.org/projects/incfwhsmouse) by linearly transforming them into each of a series of para-sagittal MR image slices, and identifying the best-fit slice by calculating the similarity metric value (δ). Transformed 2D images were compared with 3D gene expression image datasets, which were made using a microtomy-based microarray assay system, Transcriptome Tomography, and archived in the ViBrism DB (http://vibrism.neuroinf.jp): the 3D images are located in the WHS. We first transformed ISH images of 10 regionally expressed genes and compared them to signals of corresponding 3D expression images in ViBrism DB for evaluating the integration schema: two types of data, produced with different modalities and originally located in different dimensions, were successfully compared after enhancing ISH signals against background noise. Then, for the massive transformation of BrainTx database images, we parallelized our framework, using the IPython cluster package, and implemented it on the PC cluster provided for the Brain Atlasing Hackathon activity hosted by Neuroinformatics Japan Center in Japan. We could identify the best-fit positions for all of the ISH images. All programs were made available through the GitHub repository, at the web site of neuroinformatics/bah2016_registration (https://github.com/neuroinformatics/bah2016_registration).

Simulation platform: cloud-computing meets computational neuroscience

Computational models and theoretical tools are essential components in computational neuroscience. A number of models and tools have been developed and registered at various online databases such as ModelDB and J-Node Platforms. Yet, the reuse of such resources still remains quite difficult. For example, to carry out a computer simulation of a model, we have to download the program from the database, extract, read instructions, compile if the program is written in a general programming language such as C, install the appropriate neural simulator if it is written for a simulator such as GENESIS, NEURON, and NEST, and finally we may be ready to do it, if no problems occurs during all the setup mentioned above. How can we avoid this hustle? As a solution of it, we introduce a cloud-based system for online computer simulation called Simulation Platform. Simulation Platform is a cloud of virtual machines running GNU/Linux. On a virtual machine, various software including developer tools such as compilers and libraries, popular neural simulators, and scientific software such as Gnuplot, R and Octave, are pre-installed. When a user posts a request, a virtual machine is assigned to the user, and the simulation starts on that machine. The user can remotely access the virtual machine through a web browser and carries out the simulation interactively (a screenshot is shown in Fig. ​Fig.1).1). There is no need to install any software. It only requires a web browser. Therefore, Simulation Platform provides an ubiquitous computing environment for computational neuroscience research so as to free neuroscientists from tedious computer administration tasks and allow them to solely concentrate on their science. A demo site is open at http://sf4.sim.neuroinf.jp/~tyam/cns11/. Figure 1 A screenshot of a web browser during a computer simulation.