QuickNII
Software for 2D image registration to 3D atlas.
Brain atlasesData integration
Tools
Extract, quantify and analyse features from rodent histological images
The QUINT workflow comprises a suite of software designed to support atlas-based quantification. All the software have user interfaces, with no coding ability required. It generates object counts and percentage coverage per atlas-region, in addition to point clouds for visualising the features in 3D.
Brain atlases
Rat Brain Atlas
The Waxholm Space rat brain atlas is a detailed volumetric atlas of the rat brain, to which a wide range of anatomical and functional data have been registered, including detailed data showing cellular distributions, axonal pathways, and gene expression patterns. EBRAINS provides a visualization interface, enabling researchers to explore and compare different aspects of the rat brain in 3D space.
Brain atlases
Remote Connection Manager
The Remote Connection Manager (RCM) is an application that wraps vnc client. It allows HPC-users to perform remote visualization on HPC clusters. The tool offers to: Visualize the data produced on Cineca’s HPC systems (scientific visualization); Analyse and inspect data directly on the systems; Debug and profile parallel codes running on the HPC clusters. Debugging and profiling tools have to be interfaced to the compute nodes which execute the parallel code; they can benefit from tools enabling a graphic connection to the compute nodes. Scientific visualization can exploit the hardware (GPUs, memory and CPUs) available on the server side, enabling the user to remotely access their data and display them in an efficient way on their local client. The graphical interface of RCM allows the HPC users to easily create remote displays and to manage them (connect, kill, refresh).
Data analysis and visualisation
ReMoToo
ReMoToo is a system service that is able to stream the desktop to web remote clients, making it possible to have interactive sessions over remote high performance systems or even regular systems through a standard web browser. The key aspects of ReMoToo are the high quality visualization it provides as well as its low latency. To achieve it, ReMoToo uses video compression on the server side and sends the generated video stream to the client in a transparent and easy way. On the server side, several ReMoToo instances are managed by another service called ReMoLON. This service is in charge of the initiation, control and stop of ReMoToo visualization streams. The ReMoLON system service is connected through the ReMoLON_FrontEnd, a simple web server running on the login node/s. This FrontEnd is in charge of the user authentication and configuration of the ReMoToo instances through the ReMoLON system services.
Data analysis and visualisation
rgsl_odeiv2
An R package that solves a series of initial value problems in C via the GNU scientific library (ode solvers). The C code calls gsl_odeiv2 module functions to solve the problem or problems. The goal is to offload as much work as possible to the C code and keep the overhead minimal. That is why this package expects to solve a set of problems, rather than one, for the same model file (varying in e.g.: initial conditions, or parameters). The package contains 3 interface functions, they accept different ways of defining a set of problems,each with their own drawbacks and advantages. The interface functions are described in the following Sections. The ODE has to exist as a shared library (.so) file (currently in the current working directory: ?setwd and ?getwd ). There are some assumptions we make about the contents of the shared library file. Here we assume that the solutions serve some scientific purpose and the lab experiments come with observables, some measureable values that depend on the system's state (but are not the full state vector). We call the part of the model that calculates the observables ${ModelName}_func() (vfgen also calls them Functions of the model).
Data
rsHRF
This toolbox is aimed to retrieve the onsets of pseudo-events triggering an hemodynamic response from resting state fMRI BOLD voxel-wise signal. It is based on point process theory, and fits a model to retrieve the optimal lag between the events and the HRF onset, as well as the HRF shape, using a choice of basis functions (the canonical shape with two derivatives, (smoothed) Finite Impulse Response, mixture of gammas).
Data analysis and visualisation
RTNeuron
RTNeuron is a scalable real-time rendering tool for the visualisation of neuronal simulations based on cable models. Its main utility is twofold: the interactive visual inspection of structural and functional features of the cortical column model and the generation of high quality movies and images for presentations and publications. The package provides three main components: A high level C++ library. A Python module that wraps the C++ library and provides additional tools. The Python application script rtneuron-app.py A wide variety of scenarios is covered by rtneuron-app.py. In case the user needs a finer control of the rendering, such as in movie production or to speed up the exploration of different data sets, the Python wrapping is the way to go. The Python wrapping can be used through an IPython shell started directly from rtneuron-app.py or importing the module rtneuron into own Python programs. GUI overlays can be created for specific use cases using PyQt and QML.
Data analysis and visualisationModelling and simulation
SBtabVFGEN
Convert a model that has been hand written in the Sbtab format to VFGEN's format, NEURON's MOD file format, and optionally SBML (this is done if libsbml is installed with R bindings). This model conversion tool can be used by scientists working in the field of systems biology and all adjacent fields that work with ordinary differential equation (ODE) models. It can be helpful when collaborating with other researchers as it keeps the model separate from any programming language choice. The user writes the model in SBtab form, a simple, human readable format; afterwards this SBtab model can be converted to an ODE and further processed via vfgen (an alternative to vfgen is being worked on, if needed). The final result is code for the ODE right hand side function and analytical jacobian function (among other things) in the chosen programming language. This tool prepares a model M for use in numerical analysis application such as parameter estimation
Modelling and simulation
SCAIView-NEURO
SCAIView-NEURO is an semantic search engine especially built for translational neurodegeneration research. It supports literature mining in PubMed abstracts and PubMedCentral full text publications.
Data
Scalasca
Scalasca is a software tool that supports the performance optimization of parallel programs by measuring and analyzing their runtime behavior. The analysis identifies potential performance bottlenecks – in particular those concerning communication and synchronization – and offers guidance in exploring their causes. Scalasca supports the performance optimization of simulation codes on a wide range of current HPC platforms. Its powerful analysis and intuitive result presentation guides the developer through the tuning process. Scalasca targets mainly scientific and engineering applications based on the programming interfaces MPI and OpenMP, including hybrid applications based on a combination of the two. The tool has been specifically designed for use on large-scale systems including IBM Blue Gene and Cray XT, but is also well suited for small- and medium-scale HPC platforms.
Modelling and simulation
Score-P
Score-P is a software system that provides a measurement infrastructure for profiling, event trace recording, and online analysis of High Performance Computing (HPC) applications. It is being developed with the objective of creating a common basis for several complementary optimization tools in the service of enhanced scalability, improved interoperability, and reduced maintenance cost. Currently, it works with the analysis tools Cube, Extra-P, Periscope, Scalasca Trace Tools, Vampir, and Tau and is open for other tools.
Modelling and simulation
SDA
SDA (Simulation of Diffusional Association) is a Brownian dynamics simulation software package for the simulation of the diffusion of biomacromolecules in aqueous solution. SDA can be used to compute bimolecular diffusional association rate constants and to predict the structures of diffusional encounter complexes. It can also be used to simulate dilute or concentrated protein solutions and to investigate the adsorption of proteins to solid surfaces. SDA7 is available for standalone use and a subset of the functionality is implemented in the webSDA webserver.
Modelling and simulationMolecular and subcellular simulation
SDA: Simulation of Diffusional Association
SDA7 can be used to carry out Brownian dynamics simulations of the diffusional association in a continuum aqueous solvent of two solute molecules, e.g. proteins, or of a solute molecule to an inorganic surface. SDA7 can also be used to simulate the diffusion of multiple proteins, in dilute or concentrated solutions, e.g., to study the effects of macromolecular crowding. If the 3D structure of the bound complex is unknown, SDA can be used for rigid-body docking to predict the structure of the diffusional encounter complex or the orientation in which a protein binds to a surface. The configurations obtained from SDA can subsequently be refined by running molecular dynamics simulations to obtain structures for fully bound complexes. If the 3D structure of the bound complex is known, SDA can be used to calculate bimolecular association rate constants. It can also be used to record Brownian dynamics trajectories or encounter complexes and to calculate bimolecular electron transfer rate constants. While these Brownian dynamics simulations are usually carried out with rigid solutes, in SDA7 we give a possibility to assign more than one conformation to each solute molecule. This allows some large-scale internal dynamics of macromolecules to be considered in the simulations. In this SDA distribution, there is a single executable, sda_flex, which will execute different types of simulation: Compute the bimolecular diffusional association rate constant for 2 solutes using a user-defined set of intermolecular contact distances as reaction criteria Compute the rate constants for electron transfer from the relative diffusion of two proteins Perform rigid-body docking of two macromolecules Perform rigid-body docking of a solute and a surface Calculate the time during which user-defined contacts are maintained; this gives an approximation for the lifetimes of a complex. The starting configurations may be from a crystal structure or recorded from a simulation Re-calculate energies for a recorded set of configurations Compute PMFs for protein/surface binding Perform simulations of the diffusion of multiple proteins The simulations can be run in serial or in parallel mode on a shared-memory computer architecture.
Modelling and simulation
siibra-api
siibra-api is an HTTP API for querying and retrieving contents of EBRAINS atlases. Originally built as a backend service for the interactive atlas viewer siibra-explorer, the API has been documented for connecting the brain atlases to other applications and web services.
Brain atlases
siibra-explorer
siibra-explorer is built around an interactive 3D view of the brain displaying a unique selection of detailed templates and parcellation maps for the human, macaque, rat or mouse brain, including BigBrain as a microscopic resolution human brain model at its full resolution of 20 micrometres.
Data analysis and visualisationBrain atlases
siibra-python
The Python library siibra-python is designed for integrating EBRAINS atlases into scripts and computational workflows. Besides providing programmatic access to all functionalities available in the interactive viewer siibra-explorer, it enables more advanced utilization of atlas information.
Brain atlases
Single Cell Model Builder Notebook
The current version of the Single Cell Model Builder Notebook implements a Use Case of the Brain Simulation Platform. It allows to select among self-consistent configuration files from previous optimizations. The user may choose and visualize an existing morphology from HBP data, choose a self-consistent set of configuration files for the chosen morphology, visualize the electrophysiological features that will be used as reference by the optimization process, visualize and change the parameters of an existing optimization, configure the BluePyOpt optimization algorithm and run the optimization procedure on CSCS and NSG systems. The Use Case allows the user also to choose either a previous optimization from a CSCS container; or choose the result of his/her own optimization from the Collab storage, and then run and save an analysis of the results.
Modelling and simulation
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