Tools

VTK-m

One of the biggest recent changes in high-performance computing is the increasing use of accelerators. Accelerators contain processing cores that independently are inferior to a core in a typical CPU, but these cores are replicated and grouped such that their aggregate execution provides a very high computation rate at a much lower power. Current and future CPU processors also require much more explicit parallelism. Each successive version of the hardware packs more cores into each processor, and technologies like hyperthreading and vector operations require even more parallel processing to leverage each core’s full potential. VTK-m is a toolkit of scientific visualization algorithms for emerging processor architectures. VTK-m supports the fine-grained concurrency for data analysis and visualization algorithms required to drive extreme scale computing by providing abstract models for data and execution that can be applied to a variety of algorithms across many different processor architectures.

Data analysis and visualisation

WebAlign

EBRAINS users can create their own workspace for analysing 2D histological section images.In this space, the data can be uploaded and registered to a common reference atlas using the webAlign service. Several other tools are available as well that can be combined in different workflows for further analysis.

Brain atlasesData integration

WebWarp

WebWarp is an online tool for nonlinear refinement of spatial registration of histological section images from rodent brains to reference 3D atlases. Webwarp is compatible with registration performed with the WebAlign tool. Different experimental datasets registered to the same reference atlas allows you to spatially integrate, analyse and navigate these datasets within a standardised coordinate system. The output of Webwarp can be used for analysis in the online QUINT workflow.

Brain atlasesData integration

Whole-brain linear effective connectivity (WBLEC) estimation

These Python notebooks reproduce some figures in the following preprint using the libraries pyMOU and NetDynFlow: https://www.biorxiv.org/content/10.1101/531830v2 The notebook 1_MOUEC_Estimation.ipynb should be executed first to tune the model to the fMRI data. The other notebooks can be used for classification and interpretation of the model (using the flow for network analysis). The data files are: BOLD time series in ts_emp.npy structural connectivity in SC_anat.npy ROI labels in ROI_labels.npy --- ####Notebook 1_MOUEC_Estimation.ipynb This notebook calculates the functional connectivity and the model-based effective connectivity for each session (or run) and subject from the BOLD time series. The model is a multivariate Ornstein-Uhlenbeck (MOU) process, whose estimation procedure is implemented in the pyMOU library. The model estimates and other measures are stored in the form of arrays in the model_param_movie folder. --- ####Notebooks 2a_ClassificationTasks.ipynb and 2b_ClassificationSubjects.ipynb These notebooks compare the performances of the several type of connectivity measures (including functional and effective connectivity) in identifying cognitive tasks and subjects. They rely on the scikit.learn library. --- ####Notebook 3a_Flow.ipynb This notebook uses the NetDynFlow library to calculate the flow, which is network-oriented analysis of the MOU model fitted to the BOLD data. The flow corresponds to the input response of the network to perturbation (or stimulation of given regions). The flow captures network effects that arise from the recurrent connectivity, i.e. also taking into account indirect paths between all pairs of regions. --- ####Notebook 3b_Communities.ipynb This notebook detects communities based on the flow, namely brain regions are grouped together if they exchange strong flow in the network. It also compares the community structure between rest and movie.

Modelling and simulation

Woken

Woken provides a web service and an API to execute on demand data analytics and machine learning algorithms encapsulated in Docker containers. Algorithms and their runtime are fetched from the Algorithm Repository, a Docker registry containing approved and compatible algorithms and their runtimes. Woken provides the algorithms with data loaded from a database, monitors the execution of the algorithm other one machine of a cluster, then it collects the result formatted as a PFA document and returns a response to the client. Woken tracks provenance information, runs cross-validation of the models produced by the ML algorithms.

Data analysis and visualisation

ZetaStitcher

ZetaStitcher was designed to stitch the large volumetric datasets that are produced, for example, with Light-Sheet Microscopy when imaging large samples (such as a whole mouse brain) at high resolution. This tool computes optimal alignment of adjacent tiles by evaluating the cross-correlation of overlapping areas at selected stack depths. This ensures a high throughput, since a large dataset need not be processed in its entirety. Cross-correlation is computed efficiently by means of FFT. The software is fully written in Python and exposes an Application Programming Interface (API) that can be used to perform queries on the stitched dataset for further processing.

Data

τ-RAMD

The τRAMD (τ-Random Acceleration Molecular Dynamics) technique makes use of RAMD simulations to compute relative residence times (or dissociation rates) of protein-ligand complexes. In the RAMD method, the egress of a small molecule from a target receptor is accelerated by the application of an adaptive randomly oriented force on the ligand. This enables ligand egress events to be observed in short, nanosecond timescale simulations without imposing any bias regarding the ligand egress route taken. Apart from the estimation of relative residence times, the τRAMD method can be used to investigate dissociation mechanisms and characterize transition states by analysing the RAMD trajectories with the MD-IFP (Molecular Dynamics - Interaction Fingerprint) tool. The combined use of τRAMD and MD-IFP may assist the early stages of drug discovery campaigns for the design of new molecules or ligand optimization.

Modelling and simulation

τRAMD

τRAMD is a computationally efficient procedure that enables the computation of relative residence times (τ) or dissociation rates of protein-ligand complexes. It makes use of random acceleration molecular dynamics (RAMD) simulations to facilitate ligand egress in a short timescale and without imposing any bias regarding the ligand egress route. τRAMD is a powerful tool for ranking drug candidates according to their residence times and it can be used with the MD-IFP (Molecular Dynamics - Interaction Fingerprint) tool to investigate dissociation mechanisms and pathways. The combined use of τRAMD and MD-IFP may assist the design of new molecules or ligand optimization.

Modelling and simulationMolecular and subcellular simulation

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