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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   

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http://rana.lbl.gov/drosophila

A single source for sequences, assemblies, annotations and analyses of the genomes of members of the fruitfly genus Drosophlia. It is meant as resource for Drosophilists and other researchers interested in comparative analysis of these species and their genomes. There are pages for each species, as well as pages for different types of multi-species resources (e.g. alignments). If you have a public resource that will help this project, please consider making it available through this page by emailing multiple_at_fruitfly.org.

Proper citation: Assembly/Alignment/Annotation of 12 Related Drosophila Species (RRID:SCR_002921) Copy   

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http://www.brainscape.org/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on May 23, 2013. Database for resting state functional connectivity studies. Functional connectivity has shown tremendous promise in mapping the intrinsic functional topography of the brain, evaluating neuroanatomical models, and investigating neurological and psychiatric disease. Brainscape includes a repository of public and private data and an analysis engine for exploring the correlation structure of spontaneous fluctuations in the fMRI BOLD signal. (DICOM data is the image format that can be uploaded.) With Brainscape you can upload, analyze, and share your own data. You can search for, download, and analyze studies in the repository of shared data. The analysis engine works by selecting one or more studies, typing in the coordinates of a brain region of interest, and the seed-region correlation engine computes the correlation structure across the whole brain. (T1, T2 and EPI data are the scan types Brainscape can process.) You decide who can access your data. You can keep it to yourself, share with select colleagues, or share it with everyone. The Brainscape database and analysis tools are open source and freely available.

Proper citation: Brainscape (RRID:SCR_002962) Copy   

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http://www.bioinformatics.babraham.ac.uk/projects/chipmonk/

Software tool to visualize and analyse ChIP-on-chip array data. Main features: * Import of data from Nimblegen arrays (other formats can be added if people send us examples) * Normalization of data (both per array and per probe) * Various data plotting options to assess data quality and the effectiveness of normalization * Creation of data groups for visualization and analysis * Visualization of data against an annotated genome. * Statistical analysis of data to find probes of interest * Creation of reports containing probes, data and genome annotation Note: This project is no longer being developed, but critical bug fixes will still be provided

Proper citation: ChIPMonk (RRID:SCR_002975) Copy   

•   Source: SciCrunch Registry

http://www.atgc-montpellier.fr/

A bioinformatics platform that is a joint project of several South of France laboratories with available services based on their expertise, issued from their research activities which involve phylogenetics, population genetics, molecular evolution, genome dynamics, comparative and functional genomics, and transcriptome analysis. Most of the software and databases on ATGC are (co)authored by researchers from South of France teams. Some are widely used and highly cited. South of France laboratories: * CRBM (transcriptomes and stem cells). * IBC (computational biology). * MiVEGEC (evolution and phylogeny). * LGDP (plant genomics). * LIRMM (computer science). * South Green (plant genomics).

Proper citation: ATGC: Montpellier bioinformatics platform (RRID:SCR_002917) Copy   

•   Source: SciCrunch Registry

http://www.broadinstitute.org/scientific-community/science/programs/medical-and-population-genetics/haploview/haploview

A Java based software tool designed to simplify and expedite the process of haplotype analysis by providing a common interface to several tasks relating to such analyses. Haploview currently allows users to examine block structures, generate haplotypes in these blocks, run association tests, and save the data in a number of formats. All functionalities are highly customizable. (entry from Genetic Analysis Software) * LD & haplotype block analysis * haplotype population frequency estimation * single SNP and haplotype association tests * permutation testing for association significance * implementation of Paul de Bakker's Tagger tag SNP selection algorithm. * automatic download of phased genotype data from HapMap * visualization and plotting of PLINK whole genome association results including advanced filtering options Haploview is fully compatible with data dumps from the HapMap project and the Perlegen Genotype Browser. It can analyze thousands of SNPs (tens of thousands in command line mode) in thousands of individuals. Note: Haploview is currently on a development and support freeze. The team is currently looking at a variety of options in order to provide support for the software. Haploview is an open source project hosted by SourceForge. The source can be downloaded at the SourceForge project site.

Proper citation: Haploview (RRID:SCR_003076) Copy   

•   Source: SciCrunch Registry

http://www.mrc-cbu.cam.ac.uk/Imaging

Portal where neuroimaging studies are carried out using a Siemens 3T Tim Trio Magnetic Resonance Imaging (or MRI) scanner that is wholly dedicated to studies in Cognitive Neuroscience. From emotions and memories to language and learning, functional neuroimaging is being applied in many different areas of Cognitive Neuroscience. In many cases, this research relies upon support from healthy volunteers although neuroimaging studies are also being conducted in various clinical populations, including depression, anxiety, Parkinson's disease and Alzheimer's disease.

Proper citation: CBU Imaging Wiki (RRID:SCR_003014) Copy   

•   Source: SciCrunch Registry

http://cytoscape.org

Software platform for complex network analysis and visualization. Used for visualization of molecular interaction networks and biological pathways and integrating these networks with annotations, gene expression profiles and other state data.

Proper citation: Cytoscape (RRID:SCR_003032) Copy   

•   Source: SciCrunch Registry

https://bioimagesuiteweb.github.io/webapp/index.html

Web applications for analysis of multimodal/multispecies neuroimaging data. Image analysis software package. Has facilities for DTI and fMRI processing. Capabilities for both neuro/cardiac and abdominal image analysis and visualization. Many packages are extensible, and provide functionality for image visualization and registration, surface editing, cardiac 4D multi-slice editing, diffusion tensor image processing, mouse segmentation and registration, and much more. Can be intergrated with other biomedical image processing software, such as FSL, AFNI, and SPM.

Proper citation: BioImage Suite (RRID:SCR_002986) Copy   

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http://www.bioperl.org

BioPerl is a community effort to produce Perl code which is useful in biology. This toolkit of perl modules is useful in building bioinformatics solutions in Perl. It is built in an object-oriented manner so that many modules depend on each other to achieve a task. The collection of modules in the bioperl-live repository consist of the core of the functionality of bioperl. Additionally auxiliary modules for creating graphical interfaces (bioperl-gui), persistent storage in RDMBS (bioperl-db), running and parsing the results from hundreds of bioinformatics applications (Run package), software to automate bioinformatic analyses (bioperl-pipeline) are all available as Git modules in our repository. The BioPerl toolkit provides a library of hundreds of routines for processing sequence, annotation, alignment, and sequence analysis reports. It often serves as a bridge between different computational biology applications assisting the user to construct analysis pipelines. This chapter illustrates how BioPerl facilitates tasks such as writing scripts summarizing information from BLAST reports or extracting key annotation details from a GenBank sequence record. BioPerl includes modules written by Sohel Merchant of the GO Consortium for parsing and manipulating OBO ontologies. Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: BioPerl (RRID:SCR_002989) Copy   

•   Source: SciCrunch Registry

http://www.broadinstitute.org/gsea/

Software package for interpreting gene expression data. Used for interpretation of a large-scale experiment by identifying pathways and processes.

Proper citation: Gene Set Enrichment Analysis (RRID:SCR_003199) Copy   

•   Source: SciCrunch Registry

http://iimcb.genesilico.pl/MetaLocGramN/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 5, 2023.A tool for subcellular localization prediction of Gram-negative proteins. You can also use MetaGramLocN via SOAP. SOAP enables you to invoke our method from scripts written in your programming language of choice.

Proper citation: MetaLocGramN (RRID:SCR_003154) Copy   

•   Source: SciCrunch Registry

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

A suite of client / server programs that control spike sorting in the Multichannel Acquisition Processor (MAP) Data Acquisition System (MAP System) and provides real-time data visualization and analysis. Plexon's original program supporting multichannel data acquisition is a suite of programs referred to as the Real-Time Acquisition System Programs for Unit Timing in Neuroscience software (RASPUTIN). This combination of software and hardware enables users to view waveforms, acquire action potential waveforms around a voltage-threshold crossing, sort them in real time according to their shape, record continuous analog signals, such as field potentials, eye position, blood pressure, as well as capture external digital-event data, such as individual TTL lines or multi-bit strobed word data. RASPUTIN utilizes a client/server architecture on a Microsoft Windows operating system. The server program runs the MAP box and distributes the data among a set of cooperating client programs. The program can record analog signals and spike and digital-event data in a single data file, and supports 16, 32, 48, 64, 96 and 128 channel configurations. RASPUTIN's operation is based on two primary programs: Sort Client and MAP Server. The Sort Client is the primary control program for the MAP System hardware and may be used to adjust the MAP operating parameters (e.g., amplification, filtering) and to set the specific sorting parameters for each channel. MAP Server is the low-level interface for configuring the MAP, which transfers commands such as gain and filter changes or parameter settings from the various clients to the MAP box. MAP Server also accumulates data coming from the MAP box in a circular buffer memory. The client programs connect to MAP Server to gain access to that data. MAP Server also mediates communication between the clients, keeping them informed of commands sent to the MAP from other clients. RASPUTIN is not sold separately, but rather arrives pre-loaded on the MAP Control Computer with the purchase of any MAP System. As the MAP System has been replaced by the advanced OmniPlex D Neural Data Acquisition System, Plexon is no longer developing the RASPUTIN software program.

Proper citation: MAP Software (RRID:SCR_003170) Copy   

•   Source: SciCrunch Registry

http://www.scripps.edu/researchservices/dna_array/pages/Data_Analysis_GCOS.htm

Affymetrix has recently released a new software for the acquisition, management, and analysis of gene expression data. The new GeneChip Operating Software (GCOS) platform enables researchers to perform gene expression, SNP mapping and resequencing analysis with integrated data management and scalable client server configurations. * Compatible with additional Affymetrix analysis software such as Data Mining Tool (DMT) and GeneChip DNA Analysis Software (GDAS) * Supports Gene Expression, Resequencing and Genotyping Applications * Baseline Comparison Analysis Input: Affymetrix .DAT file Output: Affymetrix files (.CEL, .CHP, .RPT, .EXP, .TXT) Availability: The Core Facility has a copy of GCOS, as well as an older version of the Affymetrix software, Microarray Suite (MAS), available for use upon request.

Proper citation: GeneChip Operating Software (RRID:SCR_003408) Copy   

•   Source: SciCrunch Registry

http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/

Software R package for weighted correlation network analysis. WGCNA is also available as point-and-click application. Unfortunately this application is not maintained anymore. It is known to have compatibility problems with R-2.8.x and newer, and the methods it implements are not all state of the art., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: Weighted Gene Co-expression Network Analysis (RRID:SCR_003302) Copy   

•   Source: SciCrunch Registry

http://socialbrain.wordpress.com/2009/09/30/neurovia-download-neuro-imaging-tools-software-brain-project-matlab-program-package-for-the-analysis-of-functional-neuroimages/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on June 24, 2013. These distribution sets contain software modules and/or data sets extracted from the Visualization and Analysis Software Tools (VAST) library developed at the Minneapolis VA Medical Center, the University of Minnesota and/or the International Consortium for Neuroimaging (INC) (partially funded by the Human Brain Project )

Proper citation: Software Distribution Sets (RRID:SCR_003465) 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.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