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https://python-elephant.org

The Electrophysiology Analysis Toolkit (Elephant) is a Python library that provides a modular framework for the analysis of experimental and simulated neuronal activity data, such as spike trains, local field potentials, and intracellular data. Elephant builds on the Neo data model to facilitate usability, to enable interoperability, and to support data from dozens of file formats and network simulation tools. Its analysis functions are continuously validated against reference implementations and reports in the literature. Visualizations of analysis results are made available via the Viziphant companion library. Elephant aims to act as a platform for sharing analysis methods across the field.

Proper citation: Elephant (RRID:SCR_003833) Copy   

•   Source: SciCrunch Registry

http://www.qub.ac.uk/schools/BioimagingCoreTechnologyUnit/NIVTA/

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 29, 2016. A pan European network for virtual tissue archiving aimed at supporting clinical trials, biomarker research, tissue microarray analysis and virtual slide based education. NIVTA has state-of-the-art digital scanning systems including an Aperio CS system, Aperio OS system (one of only two currently available in Europe) and a Hamamatsu system with fluorescent scanning capability.

Proper citation: Northern Ireland Virtual Tissue Archive (RRID:SCR_004452) Copy   

•   Source: SciCrunch Registry

http://discovery.hsci.harvard.edu/

An online database of curated cancer stem cell (CSC) experiments coupled to the Galaxy analytical framework. Driven by a need to improve our understanding of molecular processes that are common and unique across cancer stem cells (CSCs), the SCDE allows users to consistently describe, share and compare CSC data at the gene and pathway level. The initial focus has been on carefully curating tissue and cancer stem cell-related experiments from blood, intestine and brain to create a high quality resource containing 53 public studies and 1098 assays. The experimental information is captured and stored in the multi-omics Investigation/Study/Assay (ISA-Tab) format and can be queried in the data repository. A linked Galaxy framework provides a comprehensive, flexible environment populated with novel tools for gene list comparisons against molecular signatures in GeneSigDB and MSigDB, curated experiments in the SCDE and pathways in WikiPathways. Investigation/Study/Assay (ISA) infrastructure is the first general-purpose format and freely available desktop software suite targeted to experimentalists, curators and developers and that: * assists in the reporting and local management of experimental metadata (i.e. sample characteristics, technology and measurement types, sample-to-data relationships) from studies employing one or a combination of technologies; * empowers users to uptake community-defined minimum information checklists and ontologies, where required; * formats studies for submission to a growing number of international public repositories endorsing the tools, currently ENA (genomics), PRIDE (proteomics) and ArrayExpress (transcriptomics). Galaxy allows you to do analyses you cannot do anywhere else without the need to install or download anything. You can analyze multiple alignments, compare genomic annotations, profile metagenomic samples and much much more. Best of all, Galaxy''''s history system provides a complete analyses record that can be shared. Every history is an analysis workflow, which can be used to reproduce the entire experiment. The code for this Galaxy instance is available for download from BitBucket.

Proper citation: Stem Cell Discovery Engine (RRID:SCR_004453) Copy   

•   Source: SciCrunch Registry

http://www.trex.uqam.ca/

A web server dedicated to the reconstruction of phylogenetic trees, reticulation networks and to the inference of horizontal gene transfer (HGT) events.

Proper citation: Tree and reticulogram REConstruction (RRID:SCR_004497) Copy   

•   Source: SciCrunch Registry

https://www.fieldtriptoolbox.org

Software toolbox for analysis of MEG, EEG, and other electrophysiological data. Used by experimental neuroscientists.

Proper citation: FieldTrip (RRID:SCR_004849) Copy   

•   Source: SciCrunch Registry

http://mga.bionet.nsc.ru/soft/maia-1.0/

Software package of programs for complex segregation analysis in animal pedigrees.

Proper citation: MAIA (RRID:SCR_007153) Copy   

•   Source: SciCrunch Registry

http://www.biomanta.org/

This project encompasses development of novel biological network analysis methods and infrastructure for querying biological data in a semantically-enabled format, and aims to create a semantic interactome model. Research within the BioMANTA project will focus on computational modelling and analysis, primarily using Semantic Web technologies and Machine Learning methods, of large-scale protein-protein interaction and compound activity networks across a wide variety of species. A range of information such as kinetic activity, tissue expression, and subcellular localization and disease state attributes will be included in the resulting data model. Protein interactions are a fundamental component of biological processes. Many proteins are functional only in multimeric complexes, or require interaction partners to achieve their correct localisation or function. For this reason, the study of protein-protein interaction (PPI) networks has become an area of growing interest in computational biology. Through the use of Semantic Web technologies such as Resource Description Framework (RDF) and Web Ontology Language (OWL), interaction data is modelled to create a knowledge representation in which meaning is vested in the ontology rather than instances of data. Stochastic and computational intelligence methods are applied to this data to infer high coverage networks. Semantic inferencing is used to infer previously unknown and meaningful pathways. Major project components: - The BioMANTA Ontology:- An OWL DL ontology incorporating the PSI-MI Ontology, the NCBI Taxonomy, and elements of BioPax ontology and Gene Ontology (describing subcellular localisation). This allows us to re-use existing ontologies, thereby reducing overheads associated with knowledge acquisition in the ontology development process. We are able to integrate existing public data that contain annotation in these formats. - Data conversion & semantic protein integration:- A set of software components that convert protein-protein databases (DIP, MPact, IntAct, etc.) from PSI-MI XML to RDF compliant with the BioMANTA ontology. These software allow us to make these protein-protein interaction datasets (and more generally, any PSI-MI XML data) semantically available for querying and inference within BioMANTA. - A RDF triple store based on RDF Molecules and the MapReduce architecture:- A proof-of-concept RDF triple store using RDF molecules and Hadoop scale-out architectures. Regular RDF graphs are deconstructed into RDF molecules, which are distributed over distributed compute nodes in the MapReduce architecture, and are subsequently combined to form equivalent RDF graphs. Such an approach makes the distributed SPARQL querying and reasoning on RDF triple stores possible. - A quantitative framework to integrate networks extracted from independent data sources (gene expression, subcellular localization, and ortholog mapping):- The model is multi-layer, with a first layer based on Decision Trees where each Decision tree is built on each dataset independently. The tree nodes are cut using Shannon''s entropy (mutual information); the decision of these independent trees is integrated using logistic regression, and the parameters are optimised using maximum likelihood. Sponsors: This resource is supported by the Pfizer Global Research and Development, the Institute for Molecular Bioscience (IMB), and the University of Queensland, Australia.

Proper citation: BioMANTA (RRID:SCR_007177) Copy   

•   Source: SciCrunch Registry

http://www.icpsr.umich.edu/SAMHDA/

Database of the nation''s substance abuse and mental health research data providing public use data files, file documentation, and access to restricted-use data files to support a better understanding of this critical area of public health. The goal is to increase the use of the data to most accurately understand and assess substance abuse and mental health problems and the impact of related treatment systems. The data include the U.S. general and special populations, annual series, and designs that produce nationally representative estimates. Some of the data acquired and archived have never before been publicly distributed. Each collection includes survey instruments (when provided), a bibliography of related literature, and related Web site links. All data may be downloaded free of charge in SPSS, SAS, STATA, and ASCII formats and most studies are available for use with the online data analysis system. This system allows users to conduct analyses ranging from cross-tabulation to regression without downloading data or relying on other software. Another feature, Quick Tables, provides the ability to select variables from drop down menus to produce cross-tabulations and graphs that may be customized and cut and pasted into documents. Documentation files, such as codebooks and questionnaires, can be downloaded and viewed online.

Proper citation: Substance Abuse and Mental Health Data Archive (RRID:SCR_007002) Copy   

•   Source: SciCrunch Registry

http://www.ccp4.ac.uk/

Portal for Macromolecular X-Ray Crystallography to produce and support an integrated suite of programs that allows researchers to determine macromolecular structures by X-ray crystallography, and other biophysical techniques. Used in the education and training of scientists in experimental structural biology for determination and analysis of protein structure.

Proper citation: CCP4 (RRID:SCR_007255) Copy   

•   Source: SciCrunch Registry

http://www.mbio.ncsu.edu/BioEdit/bioedit.html

Software tool as biological sequence alignment editor written for Windows 95/98/NT/2000/XP/7 and sequence analysis program. Provides sequence manipulation and analysis options and links to external analysis programs to view and manipulate sequences with simple point and click operations., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: BioEdit (RRID:SCR_007361) Copy   

•   Source: SciCrunch Registry

https://www.bi.mpg.de/borst

Merger of the Max Planck Institute of Neurobiology and the Max Planck Institute of Ornithology and has been renamed to Circuits - Computation – Models. Department devoted to the study of how the brain computes to understand neural information processing at the level of individual neurons and small neural circuits.

Proper citation: Max Planck Institute for Biological Intelligence Circuits - Computation – Models (RRID:SCR_008048) Copy   

•   Source: SciCrunch Registry

https://wiki.med.harvard.edu/SysBio/Megason/GoFigure

GoFigure is a software platform for quantitating complex 4d in vivo microscopy based data in high-throughput at the level of the cell. A prime goal of GoFigure is the automatic segmentation of nuclei and cell membranes and in temporally tracking them across cell migration and division to create cell lineages. GoFigure v2.0 is a major new release of our software package for quantitative analysis of image data. The research focuses on analyzing cells in intact, whole zebrafish embryos using 4d (xyzt) imaging which tends to make automatic segmentation more difficult than with 2d or 2d+time imaging of cells in culture. This resource has developed an automatic segmentation pipeline that includes ICA based channel unmixing, membrane nuclear channel subtraction, Gaussian correlation, shape models, and level set based variational active contours. GoFigure was designed to meet the challenging requirements of in toto imaging. In toto imaging is a technology that we are developing in which we seek to track all the cell movements and divisions that form structures during embryonic development of zebrafish and to quantitate protein expression and localization on top of this digital lineage. For in toto imaging, GoFigure uses zebrafish embryos in which the nuclei and cell membranes have been marked with 2 different color fluorescent proteins to allow cells to be segmented and tracked. A transgenic line in a third color can be used to mark protein expression and localization using a genetic approach that this resource developed called FlipTraps or using traditional transgenic approaches. Embryos are imaged using confocal or 2-photon microscopy to capture high-resolution xyzt image sets used for cell tracking. The GoFigure GUI will provide many tools for visualization and analysis of bioimages. Since fully automatic segmentation of cells is never perfect, GoFigure will provide easy to use tools for semi-automatically and manually adding, deleting, and editing traces in 2d (figures-xy, xz, or yz), 3d (meshes- xyz), 4d (tracks- xyzt) and 4d+cell division (lineages). GoFigure will also provide a number of views into complex image data sets including 3d XYZ and XYT image views, tabular list views of traces, histograms, and scattergrams. Importantly, all these views will be linked together to allow the user to explore their data from multiple angles. Data will be easily sorted and color-coded in many ways to explore correlations in higher dimensional data. The GoFigure architecture is designed to allow additional segmentation, visualization, and analysis filters to be plugged in. Sponsors: GoFigure is developed by Harvard University., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: Harvard Medical School, Department of Systems Biology: The Megason Lab -GoFigure Software (RRID:SCR_008037) Copy   

•   Source: SciCrunch Registry

http://pdbfun.uniroma2.it/

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. PDBfun is a web server for structural and functional analysis of proteins at the residue level. pdbFun gives fast access to the whole Protein Data Bank (PDB) organized as a database of annotated residues. The available data (features) range from solvent exposure to ligand binding ability, location in a protein cavity, secondary structure, residue type, sequence functional pattern, protein domain and catalytic activity. PDBfun is an integrated web tool for querying the PDB at the residue level and for local structural comparison. It integrates knowledge on single residues in protein structures coming from other databases or calculated with available or in-house developed instruments for structural analysis. Each set of different annotations represents a feature. Features are listed in PDBfun main page in orange. Features can be used for building residues selections.

Proper citation: Protein Databank Fun (RRID:SCR_008226) Copy   

•   Source: SciCrunch Registry

http://www.rad.upenn.edu/sbia/braid/braid_web/index.html

Large-scale archive of normalized digital spatial and functional data with an analytical query mechanism. One of its many applications is the elucidation of brain structure-function relationships. BRAID stores spatially defined data from digital brain images which have been mapped into normalized Cartesian coordinates, allowing image data from large populations of patients to be combined and compared. The database also contains neurological data from each patient and a query mechanism that can perform statistical structure-function correlations. The project is developing database technology for the manipulation and analysis of 3-dimensional brain images derived from MRI, PET, CT, etc. BRAID is based on the PostgreSQL server, an object/relational DBMS, which allows a standard relational DBMS to be augmented with application-specific datatypes and operators. The BRAID project is adding operations and datatypes to support querying, manipulation and analysis of 3D medical images, including: * Image Datatypes: BRAID supports a family of 3D image datatypes, each having an abstract type and an implementation type. Abstract types include boolean (for regions of interest), integer, float, vector (for representing morphological changes), tensor (for representing derivatives and standard deviations of vector images) and color. Implementation types at present include line-segment format and voxel array. * Image Operators: BRAID supports addition of images, multiplication (which is interpreted as intersection for boolean images), coercion of an image''s abstract or implementation type to another value, and determination of volumes of regions of interest. * Statistical Operators: A chi-squared test has been added to SQL as an aggregate operator on pairs of boolean values. * Web Interface: A general-purpose Web gateway allows the results of queries that return computed images to be displayed. You can download the BRAID source code 2.0. This version is developed under postgreSQL 7.3.4., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: BRAID (RRID:SCR_008702) Copy   

•   Source: SciCrunch Registry

http://www.broad.mit.edu/cancer/software/genecluster2/gc2.html

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 17, 2013. A software package for analyzing gene expression and other bioarray data, giving users a variety of methods to build and evaluate class predictors, visualize marker lists, cluster data and validate results. GeneCluster 2.0 greatly expands the data analysis capabilities of GeneCluster 1.0 by adding supervised classification, gene selection, class discovery and permutation test methods. It includes algorithms for building and testing supervised models using weighted voting (WV) and k-nearest neighbor (KNN) algorithms, a module for systematically finding and evaluating clustering via self-organizing maps, and modules for marker gene selection and heat map visualization that allow users to view and sort samples and genes by many criteria. It enhances the clustering capabilities of GeneCluster 1.0 by adding a module for batch SOM clustering, and also includes a marker gene finder based on a KNN analysis and a visualization module. GeneCluster 2.0 is a stand-alone Java application and runs on any platform that supports the Java Runtime Environment version 1.3.1 or greater.

Proper citation: GeneCluster 2: An Advanced Toolset for Bioarray Analysis (RRID:SCR_008446) Copy   

•   Source: SciCrunch Registry

http://connectomics.org/viewer

Extensible, scriptable, pythonic software tool for visualization and analysis in structural neuroimaging research on many spatial scales. Employing the Connectome File Format, diverse data such as networks, surfaces, volumes, tracks and metadata are handled and integrated. The field of Connectomics research benefits from recent advances in structural neuroimaging technologies on all spatial scales. The need for software tools to visualize and analyze the emerging data is urgent. The ConnectomeViewer application was developed to meet the needs of basic and clinical neuroscientists, as well as complex network scientists, providing an integrative, extensible platform to visualize and analyze Connectomics data. With the Connectome File Format, interlinking different datatypes such as hierarchical networks, surface data, volumetric data is easy and might provide new ways of analyzing and interacting with data. Furthermore, ConnectomeViewer readily integrates with: * ConnectomeWiki: a semantic knowledge base representing connectomics data at a mesoscale level across various species, allowing easy access to relevant literature and databases. * ConnectomeDatabase: a repository to store and disseminate Connectome files.

Proper citation: ConnectomeViewer: Multi-Modal Multi-Level Network Visualization and Analysis (RRID:SCR_008312) Copy   

•   Source: SciCrunch Registry

http://www.scienceexchange.com/facilities/high-throughput-sequencing-and-microarray-facility-princeton

Core facility provides researchers with access to high-throughput sequencing technologies. The staff provide consultation on experimental design, library preparation, and data analysis. The Sequencing Core Facility works closely with Bioinformatics staff in the Center for Quantitative Biology to provide researchers with computing power and consulting services to analyze sequencing data.

Proper citation: Princeton High Throughput Sequencing and Microarray Facility (RRID:SCR_012619) Copy   

•   Source: SciCrunch Registry

https://www.immport.org/home

Data sharing repository of clinical trials, associated mechanistic studies, and other basic and applied immunology research programs. Platform to store, analyze, and exchange datasets for immune mediated diseases. Data supplied by NIAID/DAIT funded investigators and genomic, proteomic, and other data relevant to research of these programs extracted from public databases. Provides data analysis tools and immunology focused ontology to advance research in basic and clinical immunology.

Proper citation: The Immunology Database and Analysis Portal (ImmPort) (RRID:SCR_012804) Copy   

•   Source: SciCrunch Registry

https://github.com/macs3-project/MACS

Software Python package for identifying transcript factor binding sites. Used to evaluate significance of enriched ChIP regions. Improves spatial resolution of binding sites through combining information of both sequencing tag position and orientation. Can be used for ChIP-Seq data alone, or with control sample with increase of specificity.

Proper citation: MACS (RRID:SCR_013291) Copy   

•   Source: SciCrunch Registry

http://www.mrc-lmb.cam.ac.uk/genomes/dolop/

DOLOP is an exclusive knowledge base for bacterial lipoproteins by processing information from 510 entries to provide a list of 199 distinct lipoproteins with relevant links to molecular details. Features include functional classification, predictive algorithm for query sequences, primary sequence analysis and lists of predicted lipoproteins from 43 completed bacterial genomes along with interactive information exchange facility. This website along will have additional information on the biosynthetic pathway, supplementary material and other related figures. DOLOP also contains information and links to molecular details for about 278 distinct lipoproteins and predicted lipoproteins from 234 completely sequenced bacterial genomes. Additionally, the website features a tool that applies a predictive algorithm to identify the presence or absence of the lipoprotein signal sequence in a user-given sequence. The experimentally verified lipoproteins have been classified into different functional classes and more importantly functional domain assignments using hidden Markov models from the SUPERFAMILY database that have been provided for the predicted lipoproteins. Other features include: primary sequence analysis, signal sequence analysis, and search facility and information exchange facility to allow researchers to exchange results on newly characterized lipoproteins.

Proper citation: DOLOP: A Database of Bacterial Lipoproteins (RRID:SCR_013487) Copy   

•   Source: SciCrunch Registry