Tools

libsonata

C++ / Python reader for SONATA circuit files

Livre

Livre (Large-scale Interactive Volume Rendering Engine) is an out-of-core, multi-node, multi-gpu, OpenGL volume rendering engine to visualise large volumetric data sets. It provides the following major features to facilitate rendering of large volumetric data sets: Visualisation of pre-processed UVF format (source code) volume data sets. Real-time voxelisation of different data sources (surface meshes, BBP morphologies, local-field potentials, etc) through the use of plugins. Multi-node, multi-gpu rendering (Currently only sort-first rendering)

LocaliZoom

LocaliZoom allows the viewing and exploring of high-resolution images with superimposed atlas overlays, and the extraction of coordinates of annotated points within those images for viewing in 3D brain atlas space. It is well suited for the extraction of a limited number of coordinates, e.g. representing an electrode track or labelling within a small region of interest.

MD-IFP

The MD-IFP is a python workflow for the generation and analysis of protein-ligand interaction fingerprints from Molecular Dynamics trajectories. If used for the analysis of RAMD (Random Accelaration Molecular Dynamics) trajectories, it can help to investigate dissociation mechanisms by characterizing transition states as well as the determinants and hot-spots for dissociation. As such, 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.

Medical Informatics Platform

The Medical Informatics Platform (MIP) is designed to help clinicians, clinical scientists, and clinical data scientists aiming to adopt advanced analytics for clinical research. Users can explore harmonized medical data extracted from pre-processed neuroimaging, neurophysiological and medical records and research cohort datasets without transferring original clinical data.

MEDIUM

MEDIUM is a Python tool that uses the DF matrices to extract global information on the protein. It works directly on DF images and uses a Convolutional Neural Network (CNN) machine learning approach to train the model in recognising specific patterns in the DF matrix capable of classifying the protein in a specific state in the presence of a ligand (e.g., HOLO or APO states). Then, the trained model can be used to classify the protein state in presence of different ligands, by testing new DF matrices arising from short simulations performed on the new system.

MeLVin

The application of data visualization to exploratory analysis has proven its effectiveness in different scientific fields. Unfortunately, each discipline suffers from specific problems, making it difficult to apply general solutions. MeLVin is a graphical meta-framework for our MEta Language for Visualization. It was created to facilitate the design of interactive coordinated views applications for visual exploration of data using different technologies. The platform is conceived as an abstraction layer placed over existing data visualization and processing environments, enabling their integration. The data analysis workflow can be seamlessly defined using data-flow diagrams. Unlike most data-flow diagram-based data mining applications, MeLVin allows users to include visualizations as part of the analysis process and not just as a tool to display final results. Please, find additional information on our project web page: https://gmrv.es/gmrvvis/melvin/ If you are interested in the project, you can test its functionalities at: https://gmrv.es/gmrvvis/melvin/app/auth

MeshView

MeshView is a web application for real-time 3D display of surface mesh data representing structural parcellations from volumetric atlases, such as the Waxholm Space Atlas of the Sprague Dawley Rat Brain.

MIP Microservice Infrastructure

Platform for rapidly deploying globally distributed services. It supports clustering, security, monitoring and more out of the box. It is based on Cisco's Mantl cloud project (https://github.com/CiscoCloud/mantl). The aim of this project is to support the deployment of many services in the MIP and provide an environment for big data software such as Apache Spark.

MLCE

MLCE stands for Matrix of Lowest Coupling Energies and they can be used to represent the region of the protein lowest dynamically coupled. This means that they are also the most prone to be involved in interaction with external partners.

MMCG Webserver

The Hybrid Molecular Mechanics/Coarse-Grained (MM/CG) is a server that helps in the preparation and running of multiscale molecular dynamics simulations of G protein-coupled receptors (GPCR) in complex with their ligands. The Hybrid MM/CG Webserver requires the structure of a GPCR/ligand complex as input and then guides the user through the preparation and running of the simulation.

MoDEL_CNS

MoDEL_CNS is a platform designed to provide web-access to atomistic molecular dynamics trajectories for relevant signal transduction proteins. MoDEL_CNS expands the Molecular Dynamics Extended Library (MoDEL) database of atomistic Molecular Dynamics trajectories with proteins involved in Central Nervous System (CNS) processes, including membrane proteins. MoDEL_CNS web server interface presents the resulting trajectories, analyses, and protein properties. All data produced by the project is available to download. MoDEL_CNS will contribute to the improvement of the understanding of neuronal signalling cascades by protein structure-based simulations, calculating molecular flexibility and dynamics, and guiding systems level modelling.

Multilevel Macaque Monkey Brain Atlas

The EBRAINS Monkey Brain Atlas is a comprehensive resource that provides in-depth insights into the anatomy, connectivity, and functions of the macaque monkey brain. It includes detailed information about the organization of the monkey brain at multiple levels, ranging from the microscopic level to the macroscopic level of the entire brain.

Monsteer

Monsteer is a library for Interactive Supercomputing in the neuroscience domain. Monsteer facilitates the coupling of running simulations (currently NEST) with interactive visualization and analysis applications. Monsteer supports streaming of simulation data to clients (currenty only spikes) as well as control of the simulator from the clients (also kown as computational steering). Monsteer's main components are a C++ library, a MUSIC-based application and Python helpers.

MorphIO

MorphIO is a library for reading and writing neuron morphology files. It supports the following formats: SWC ASC (aka. neurolucida) H5 v1 H5 v2 is not supported anymore, see H5v2_ It provides 3 C++ classes that are the starting point of every morphology analysis: Soma: contains the information related to the soma. Section: a section is the succession of points between two bifurcations. To the bare minimum the Section object will contain the section type, the position and diameter of each point. Morphology: the morphology object contains general information about the loaded cell but also provides accessors to the different sections. One important concept is that MorphIO is split into a read-only part and a read/write one.

Morphology Alignment Tool

MorphoUnit

MorphoUnit is a SciUnit library for data-driven testing of neuronal morphologies. Employed for testing via the HBP Validation Framework.

MorphTool

A toolbox for morphology editing. It aims to provide small helper programs that perform simple tasks. Utility toolkit for morphologies. Different usable functions that can't be fit into NeuroR or NeuroM. MorphTool provides a morphology diffing tool (via CLI or python), a file converter: to convert morphology files from/to the following formats: SWC, ASC, H5 and a soma area calculator (as computed by NEURON, requires the NEURON python module).