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http://wiki.c2b2.columbia.edu/honiglab_public/index.php/Software:Jackal

Jackal is a collection of programs designed for the modeling and analysis of protein structures. Its core program is a versatile homology modeling package. It contains twelve individual programs, each with their own function.

Proper citation: Jackal (RRID:SCR_008665) Copy   

•   Source: SciCrunch Registry

Ratings or validation data are available for this resource

http://www.ingenuity.com/products/pathways_analysis.html

A web-based software application that enables users to analyze, integrate, and understand data derived from gene expression, microRNA, and SNP microarrays, metabolomics, proteomics, and RNA-Seq experiments, and small-scale experiments that generate gene and chemical lists. Users can search for targeted information on genes, proteins, chemicals, and drugs, and build interactive models of experimental systems. IPA allows exploration of molecular, chemical, gene, protein and miRNA interactions, creation of custom molecular pathways, and the ability to view and modify metabolic, signaling, and toxicological canonical pathways. In addition to the networks and pathways that can be created, IPA can provide multiple layering of additional information, such as drugs, disease genes, expression data, cellular functions and processes, or a researchers own genes or chemicals of interest.

Proper citation: Ingenuity Pathway Analysis (RRID:SCR_008653) Copy   

•   Source: SciCrunch Registry

http://rgd.mcw.edu/rgdCuration/?module=portal&func=show&name=nuro

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 12,2023. Portal that provides researchers with easy access to data on rat genes, QTLs, strain models, biological processes and pathways related to neurological diseases. This resource also includes dynamic data analysis tools.

Proper citation: Rat Genome Database: Neurological Disease Portal (RRID:SCR_008685) Copy   

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http://www.neurostruct.org

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 16, 2013. A built-in toolbox for the tracing and analysis of neuroanatomy from nanoscale (high-resolution) imaging. It is a project under ongoing development. The name is originating by merging the words Neuron + reconstruct. The working concept is organized in filters applied successively on the image stack to be processed (pipeline). Currently, the focus of the software is the extraction of detailed neuroanatomical profiles from nanoscale imaging techniques, such as the Serial Block-Face Scanning Electron Microscopy (SBFSEM). The techniques applied, however, may be used to analyze data from various imaging methods and neuronal versatility. The underlying idea of Neurostruct is the use of slim interfaces/filters allowing an efficient use of new libraries and data streaming. The image processing follows in voxel pipelines by using the CUDA programming model and all filters are programmed in a datasize-independent fashion. Thus Neurostruct exploits efficiency and datasize-independence in an optimal way. Neurostruct is based on the following main principles: * Image processing in voxel pipelines using the general purpose graphics processing units (GPGPU) programming model. * Efficient implementation of these interfaces. Programming model and image streaming that guarantees a minimal performance penalty. * Datasize-independent programming model enabling independence from the processed image stack. * Management of the filters and IO data through shell scripts. The executables (filters) are currently managed through shell scripts. The application focuses currently in the tracing of single-biocytin filled cells using SBFSEM imaging. : * Extraction of neuroanatomical profiles: 3D reconstrution and 1D skeletons of the imaged neuronal structure. * Complete tracing: Recognition of the full neuronal structure using envelope techniques, thereby remedying the problem of spines with thin necks of an internal diameter approaching the SBFSEM resolution. * Separation (Coloring) of subcellular structures: Algorithms for the separation of spines from their root dendritic stem. * Evaluation and analysis of the imaged neuroanatomy: Calculation of the dendritic and spine membrane''s surface, spine density and variation, models of dendrites and spines

Proper citation: Neurostruct (RRID:SCR_008861) Copy   

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http://spotfire.tibco.com/

The Spotfire Gene Ontology Advantage Application integrates GO annotations with gene expression analysis in Spotfire DecisionSite for Functional Genomics. Researchers can select a subset of genes in DecisionSite visualizations and display their distribution in the Gene Ontology hierarchy. Similarly, selection of any process, function or cellular location in the Gene Ontology hierarchy automatically marks the corresponding genes in DecisionSite visualizations. Platform: Windows compatible

Proper citation: Spotfire (RRID:SCR_008858) Copy   

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https://www.brainproducts.com/

Commercial organization for hardware and software for neurophysiological research. Provides EEG and ERP amplifier systems, EEG recording caps, Data recording and analysis software, TMS Stimulator for combined EEG/TMS coregistrations and more.

Proper citation: Brain Products (RRID:SCR_009443) Copy   

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http://wbiomed.curtin.edu.au/genepop/

Population genetic data analysis software package. Used to perform exact Hardy Weinberg Equilibrium test. Used for population differentiation and for genotypic disequilibrium among pairs of loci. Computes estimates of F-statistics, null allele frequencies, allele size-based statistics for microsatellites, etc. and performs analyses of isolation by distance from pairwise comparisons of individuals or population samples., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: GENEPOP (RRID:SCR_009194) Copy   

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http://bioinf.cs.ucl.ac.uk/psipred/

Web tool as secondary structure prediction method, incorporating two feed forward neural networks which perform analysis on output obtained from PSI-BLAST. Web server offering analyses of protein sequences.

Proper citation: PSIPRED (RRID:SCR_010246) Copy   

•   Source: SciCrunch Registry

http://www.illumina.com/

American company incorporated that develops, manufactures and markets integrated systems for the analysis of genetic variation and biological function. Provides a line of products and services that serve the sequencing, genotyping and gene expression and proteomics markets. Its headquarters are located in San Diego, California.

Proper citation: Illumina (RRID:SCR_010233) Copy   

•   Source: SciCrunch Registry

http://treestoolbox.org/

Software package, written in Matlab (Mathworks, Natick, MA), providing tools to automatically reconstruct neuronal branching from microscopy image stacks and to generate synthetic axonal and dendritic trees. It provides the basic tools to edit, visualize and analyze dendritic and axonal trees, methods for quantitatively comparing branching structures between neurons, and tools for exploring how dendritic and axonal branching depends on local optimization of total wiring and conduction distance.

Proper citation: TREES toolbox (RRID:SCR_010457) Copy   

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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

http://www.cephb.fr/

The Centre d''Etude du Polymorphisme Humain (CEPH) is a research laboratory, the main activities of which are the setting up, storage, processing and distribution of DNA collections for the identification of genetic factors conferring susceptibility to complex disorders. These collections are established in partnership and full collaboration with external French or international research groups. The Foundation currently hosts the CEPH reference panel, the HGDP panel (Human genome Diversity Cell Line Panel) and several collections amounting mid-2008 to more than 250 000 samples. The goal of CEPH is to understand complex multifactorial disorders necessitates the establishment of structures facilitating access to large and integrated collection of individuals, characterized by a large number of variables emanating from different technologies and platforms. To achieve this goal, CEPH facilitates the setting up of integrated analyses combining clinical, genetic and environmental data, for the identification of susceptibility factors to complex multifactorial disorders Additionally, CEHP allows the reception, storage, processing and distribution of biological sample collections. At the same time, it promotes and participates in the design and setting up of genetic studies: - in partnership and full collaboration with external research groups - giving access to a large number of variables - in a sufficient number of subjects - allowing large scale integrated analyses

Proper citation: Centre dEtude du Polymorphisme Humain (RRID:SCR_008026) 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

http://www.imagwiki.nibib.nih.gov/

Special interest group that brings together program officers who have a shared interest in applying modeling and analysis methods to biomedical systems. The meetings are formatted to facilitate an open discussion of what is currently being supported, and for planning future directions in these areas. At each meeting, time is allotted to hear focused presentations from one or two participants to discuss issues relating to modeling and analysis across the government agencies. Discussions also occur online, and participants are informed of talks, conferences and other activities of interest to the group. IMAG recognized that the modeling community is on the forefront of thinking across the biological continuum, rather than just focusing at one scale or level of resolution. In addition IMAG identified a strong desire among modelers to form multi-disciplinary partnerships across varied research communities. Overall Intent of IMAG through the MSM Consortium is: * To develop new methodologies that span across biological scales * To develop multiscale methodologies applicable to biomedical, biological and behavioral research * To develop methodologies within the local multidisciplinary team and within the larger Framework environment * To further promote multiscale modeling through model sharing This wiki contains information relevant to the IMAG (Interagency Modeling and Analysis Group) and the MSM (Multi-scale Modeling Consortium).

Proper citation: Interagency Modeling and Analysis Group and Multi-scale Modeling Consortium Wiki (RRID:SCR_008046) 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://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.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://www.genboree.org/java-bin/EpigenomeAtlas/workbench.jsp?isPublic=yes&context=EpigenomeAtlas

Service where users are able to upload and store data, access bioinformatics tools, and perform analyses.

Proper citation: Genboree Workbench (RRID:SCR_011864) Copy   

•   Source: SciCrunch Registry