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http://bio.math.berkeley.edu/eXpress/index.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented January 29, 2018.
From website: "Note that the eXpress software is also no longer being developed. We recommend you use kallisto instead." Kallisto can be found at http://pachterlab.github.io/kallisto/.

Software for streaming quantification for high-throughput DNA/RNA sequencing.
Can be used in any application where abundances of target sequences need to be estimated from short reads sequenced from them.

Proper citation: eXpress (RRID:SCR_006873) Copy   

•   Source: SciCrunch Registry

http://intramural.nimh.nih.gov/sscc/index.html

Scientific and Statistical Computing Core of the NIMH Intramural Research Program supporting functional neuroimaging research at the NIH. This includes development of new data analysis techniques, their implementation in the AFNI software, advising researchers on the analysis methods, and instructing them in the use of software tools. Support methods: A. Provision of software for analysis for FMRI data (AFNI package: http://afni.nimh.nih.gov) * AFNI has been developed for the last 10 years by Dr Cox, et al. (6 years in Milwaukee, 4 years at NIMH) * Formal and informal instruction in the use of AFNI, including outlines of the statistical methods used in the programs * Installation of AFNI on NIH computers (Mac OS X, Unix, Linux) approximately 120 NIH systems have used AFNI in the last month (80 NIMH, 20 NINDS, 20 other) * Realtime monitoring of FMRI data at scanners * Continuing development of new modules for AFNI to meet needs of NIH researchers B. Consulting with NIH researchers about FMRI data analysis issues, concerns, and methods

Proper citation: NIMH DIRP Scientific and Statistical Computing Core (RRID:SCR_006958) 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://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.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://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

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://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://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://bdtnp.lbl.gov/Fly-Net/index.jsp?w=home

The goal of this project is to decipher the transcriptional information contained in the extensive cis-acting DNA sequences that direct the patterns of gene expression that underlie animal development. Using the early embryo of the fruitfly Drosophila melanogaster as a model, these researchers are developing experimental and computational methods to systematically characterize and dissect the complex expression patterns and regulatory interactions already present prior to gastrulation. They have identified 37 principal regulatory factors within this network for initial analysis together with their target genes. Sponsors: This project is chiefly funded by a grant from NIGMS and NHGRI, R01 GM070444. Additional funding comes from grants to Michael Eisen, Sue Celniker, and Bernd Hamann.

Proper citation: Berkeley Drosophila Transcription Network Project (RRID:SCR_008640) 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.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://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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

http://www.plexon.com/products/omniplex-software

Neural data acquisition software for signal visualization in neuron recordings along with spike wave-form classification methods. It is made up of the PlexControl software program and the OmniPlex Server.

Proper citation: OmniPlex Software (RRID:SCR_014803) Copy   

•   Source: SciCrunch Registry

http://www.sbpdiscovery.org/technology/sr/bioinformatics/Pages/LaJolla_BioinformaticsandDataManagement.aspx

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on 11132025. Facility that provides database development and management and bioinformatic network building services by utilizing on-site hardware and software. Three members of the facility are available to assist researchers with advanced bioinformatics and biostatistics analysis to help put data into biological context across various disease areas to create testable hypotheses and understand biology of the process. The bulk of support includes connecting functional genomic data with pathways and networks, connecting gene/protein expression and disease state and consultations on statistical aspects of the research with the team statistician.

Proper citation: Sanford Burnham Prebys Medical Discovery Institute Bioinformatics and Data Management Facility (RRID:SCR_014868) Copy   

•   Source: SciCrunch Registry