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http://yogo.msu.montana.edu/

A set of software tools created to rapidly build scientific data-management applications. These applications will enhance the process of data annotation, analysis, and web publication. The system provides a set of easy-to-use software tools for data sharing by the scientific community. It enables researchers to build their own custom-designed data management systems. The problem of scientific data management rests on several challenges. These include flexible data storage, a way to share the stored data, tools to curate the data, and history of the data to show provenance. The Yogo Framework gives you the ability to build scientific data management applications that address all of these challenges. The Yogo software is being developed as part of the NeuroSys project. All tools created as part of the Yogo Data Management Framework are open source and released under an OSI approved license.

Proper citation: Yogo Data Management System (RRID:SCR_004239) Copy   

•   Source: SciCrunch Registry

http://openconnectomeproject.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 9, 2023. Connectomes repository to facilitate the analysis of connectome data by providing a unified front for connectomics research. With a focus on Electron Microscopy (EM) data and various forms of Magnetic Resonance (MR) data, the project aims to make state-of-the-art neuroscience open to anybody with computer access, regardless of knowledge, training, background, etc. Open science means open to view, play, analyze, contribute, anything. Access to high resolution neuroanatomical images that can be used to explore connectomes and programmatic access to this data for human and machine annotation are provided, with a long-term goal of reconstructing the neural circuits comprising an entire brain. This project aims to bring the most state-of-the-art scientific data in the world to the hands of anybody with internet access, so collectively, we can begin to unravel connectomes. Services: * Data Hosting - Their Bruster (brain-cluster) is large enough to store nearly any modern connectome data set. Contact them to make your data available to others for any purpose, including gaining access to state-of-the-art analysis and machine vision pipelines. * Web Viewing - Collaborative Annotation Toolkit for Massive Amounts of Image Data (CATMAID) is designed to navigate, share and collaboratively annotate massive image data sets of biological specimens. The interface is inspired by Google Maps, enhanced to allow the exploration of 3D image data. View the fork of the code or go directly to view the data. * Volume Cutout Service - RESTful API that enables you to select any arbitrary volume of the 3d database (3ddb), and receive a link to download an HDF5 file (for matlab, C, C++, or C#) or a NumPy pickle (for python). Use some other programming language? Just let them know. * Annotation Database - Spatially co-registered volumetric annotations are compactly stored for efficient queries such as: find all synapses, or which neurons synapse onto this one. Create your own annotations or browse others. *Sample Downloads - In addition to being able to select arbitrary downloads from the datasets, they have also collected a few choice volumes of interest. * Volume Viewer - A web and GPU enabled stand-alone app for viewing volumes at arbitrary cutting planes and zoom levels. The code and program can be downloaded. * Machine Vision Pipeline - They are building a machine vision pipeline that pulls volumes from the 3ddb and outputs neural circuits. - a work in progress. As soon as we have a stable version, it will be released. * Mr. Cap - The Magnetic Resonance Connectome Automated Pipeline (Mr. Cap) is built on JIST/MIPAV for high-throughput estimation of connectomes from diffusion and structural imaging data. * Graph Invariant Computation - Upload your graphs or streamlines, and download some invariants. * iPad App - WholeSlide is an iPad app that accesses utilizes our open data and API to serve images on the go.

Proper citation: Open Connectome Project (RRID:SCR_004232) Copy   

•   Source: SciCrunch Registry

http://neuro.debian.net/

Collection based on a collaborative effort of popular neuroscience research software for the Debian operating system as well as Ubuntu and other derivatives. Popular packages include AFNI, FSL, PyMVPA and many others. It contains both unofficial or prospective packages which are not (yet) available from the main Debian archive, as well as backported or simply rebuilt packages also available elsewhere. A listing of current and planned projects is available if you want to get involved. The main goal of the project is to provide a versatile and convenient environment for neuroscientific research that is based on open-source software. To this end, the project offers a package repository that complements the main Debian (and Ubuntu) archive. NeuroDebian is not yet another Linux distribution, but rather an effort inside the Debian project itself. Software packages are fully integrated into the Debian system and from there will eventually migrate into Ubuntu as well. With NeuroDebian, installing and updating neuroscience software is no different from any other part of the operating system. Maintaining a research software environment becomes as easy as installing an editor. There is also virtual machine to test NeuroDebian on Windows or Mac OS. If you want to see your software packaged for Debian, please drop them a note.

Proper citation: neurodebian (RRID:SCR_004401) Copy   

•   Source: SciCrunch Registry

http://www.sysmo-db.org/

SysMO-DB is a project that is creating a web-based platform, and tooling, for finding, sharing and exchanging Data, Models and Processes in Systems Biology. It was designed to support the SysMO Consortium (Systems Biology for Micro-Organisms), but the principles and methods employed are equally applicable to other multi-site Systems Biology projects. All code is open source and available for download. SEEK, a component of SysMO-DB, is a private community collaboration and asset sharing platform for Systems Biology models, data and protocols serving 120 research institutions throughout Europe. SEEK is the main web-based access point to the system and provides an access control layer to enable researchers to restrict access to collaborators, colleagues or other individuals until they are ready to share with the whole consortium or the wider community. The main objectives of SysMO-DB are to: facilitate the web-based exchange of data between research groups within- and inter- consortia, and to provide an integrated platform for the dissemination of the results of the SysMO projects to the scientific community. We aim to devise a progressive and scalable solution to the data management needs of the SysMO initiative, that: * facilitates and maximizes the potential for data exchange between SysMO research groups; * maximizes the ''shelf life'' and utility of data generated by SysMO; * provides an integrated platform for the dissemination of the results of the SysMO projects to the scientific community; and * facilitates standardization of practices in Systems Biology for the interfacing of modeling and experimentation. We follow several key principles: * exploit what is already available, both within the consortium and outside it, and do not reinvent; * identify the least we can do to make a benefit and do this incrementally. SysMO-DB will soon be opening it up to the wider scientific community, but for now it is currently only available for those within the SysMO consortium.

Proper citation: SysMO-DB (RRID:SCR_004479) Copy   

•   Source: SciCrunch Registry

http://stemcellcommons.org/

Open source environment for sharing, processing and analyzing stem cell data bringing together stem cell data sets with tools for curation, dissemination and analysis. Standardization of the analytical approaches will enable researchers to directly compare and integrate their results with experiments and disease models in the Commons. Key features of the Stem Cell Commons * Contains stem cell related experiments * Includes microarray and Next-Generation Sequencing (NGS) data from human, mouse, rat and zebrafish * Data from multiple cell types and disease models * Carefully curated experimental metadata using controlled vocabularies * Export in the Investigation-Study-Assay tabular format (ISA-Tab) that is used by over 30 organizations worldwide * A community oriented resource with public data sets and freely available code in public code repositories such as GitHub Currently in development * Development of Refinery, a novel analysis platform that links Commons data to the Galaxy analytical engine * ChIP-seq analysis pipeline (additional pipelines in development) * Integration of experimental metadata and data files with Galaxy to guide users to choose workflows, parameters, and data sources Stem Cell Commons is based on open source software and is available for download and development.

Proper citation: Stem Cell Commons (RRID:SCR_004415) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/thornton-srv/databases/archschema/

ArchSchema is a java webstart application that generates dynamic plots of related Pfam domain architectures. The protein sequences having each architecture can be displayed on the plot and separately listed. Where there is 3D structural information in the PDB, the relevant PDB codes can be shown on the plot. Sequences can be be filtered by organism, or the output can be limited to just those protein sequences for which there is structural information in the PDB. Search by UniProt sequence id, or by Pfam domain id. Red underlines indicate the extent to which 3D structures of the domains and architectures are available in the PDB. Left-clicking on a node shows a panel containing information about the constituent domains, the protein sequences having the given architecture, and any sequences that have whole or partial structures in the PDB. You can display protein sequence (or, alternatively, the protein structures) associated with each architecture. You can download ArchSchema to run locally from your own machine. Note, however, you only download the code and not the data. Thus you will need to be connected to the Internet whenever you perform a search from within ArchSchema. The search initiates a call to the EBI which returns the data to ArchSchema for graphing.

Proper citation: ArchSchema (RRID:SCR_004947) Copy   

•   Source: SciCrunch Registry

http://www.methodbox.org/

Collaboration environment for sharing variable sets and statistical methods for analysis across social science survey data. MethodBox enables you to browse and download datasets, share methods and scripts, find fellow researchers with similar interests and share your knowledge. MethodBox source available on Google code. Finding the variables you need to support a particular research question can be time consuming. Wading through hundreds of pages of PDF documents, codebooks and metadata and then trying to find the exact column in a huge spreadsheet can be very frustrating. MethodBox gets you to the variables faster and lets you download only the data you need. You can also share your scripts with others to allow them to adopt best practice quicker than before.

Proper citation: MethodBox (RRID:SCR_004928) Copy   

•   Source: SciCrunch Registry

http://ctsaconnect.org/

THIS RESOURCE IS NO LONGER IS SERVICE. Documented on December 5th, 2022. Semantic framework to integrate information about research activities, clinical activities, and scientific resources to facilitate the production and consumption of Linked Open Data about investigators, physicians, biomedical research resources, services, and clinical activities. The goal is to enable software to consume data from multiple sources and allow the broadest possible representation of researchers'''' and clinicians'''' activities and research products. Current research tracking and networking systems rely largely on publications, but clinical encounters, reagents, techniques, specimens, model organisms, etc., are equally valuable for representing expertise. CTSAConnect will provide linkage between semantic representations of a wide range of clinical and research data using controlled vocabularies mapped to the Unified Medical Language System (UMLS) as a bridge between the two subject areas. The data sources include data from Medicaid, hospital billing systems, CTSAShareCenter, and other CTSA resource data, eagle-i and VIVO. It allows institutions to leverage existing tools and data sources by making the information they contain more discoverable and easier to integrate. For instance, with the ISF, researchers can be characterized by organizational affiliations, grant and project participation, research resources that they have generated, and publications that they have (co)-authored. Clinicians can be characterized by training and credentials, by clinical research topic, and by the kinds of procedures and specialization that can be inferred from encounter data. LOD refers to data that has been given a specific Uniform Resource Identifier (URI), for the purpose of sharing and linking data and information on the Semantic Web. While a large amount of data is published as LOD, there remains a significant gap in the representation of research resources and clinical expertise. Researchers can be characterized by the organization to which they belong, the grants and research in which they have participated, the research topics and research resources (reagents, biospecimens, animal models) they have generated, as well as the publications they have (co)-authored. Clinician profiles on the other hand, can be defined by their credentials, clinical research topics, and the kinds of procedures and specialization that can be inferred from clinical encounter data. They believe that integrating and relating this diversity of information sources and platforms requires addressing the overlap between research resources and the attributes and activities of researchers and clinicians. CTSAconnect aims to promote integration and discovery of research activities, resources, and clinical expertise. To this end, they will publish their ontologies and LOD via their website, which will also illustrate repeatable methods and examples of how to extract, consume, and utilize this valuable new LOD using freely available tools like VIVO, eagle-i, and Google APIs. CTSAconnect is a collaboration between Oregon Health & Science University, Stony Brook University, Cornell University, Harvard University, University at Buffalo, and the University of Florida, and leverages the work of eagle-i (eagle-i.net), VIVO (vivoweb.org), and ShareCenter (ctsasharecenter.org).

Proper citation: CTSAconnect (RRID:SCR_005225) Copy   

•   Source: SciCrunch Registry

http://llama.mshri.on.ca/gofish/GoFishWelcome.html

Software program, available as a Java applet online or to download, allows the user to select a subset of Gene Ontology (GO) attributes, and ranks genes according to the probability of having all those attributes.

Proper citation: GoFish (RRID:SCR_005682) Copy   

•   Source: SciCrunch Registry

http://microbesonline.org/

MicrobesOnline is designed specifically to facilitate comparative studies on prokaryotic genomes. It is an entry point for operon, regulons, cis-regulatory and network predictions based on comparative analysis of genomes. The portal includes over 1000 complete genomes of bacteria, archaea and fungi and thousands of expression microarrays from diverse organisms ranging from model organisms such as Escherichia coli and Saccharomyces cerevisiae to environmental microbes such as Desulfovibrio vulgaris and Shewanella oneidensis. To assist in annotating genes and in reconstructing their evolutionary history, MicrobesOnline includes a comparative genome browser based on phylogenetic trees for every gene family as well as a species tree. To identify co-regulated genes, MicrobesOnline can search for genes based on their expression profile, and provides tools for identifying regulatory motifs and seeing if they are conserved. MicrobesOnline also includes fast phylogenetic profile searches, comparative views of metabolic pathways, operon predictions, a workbench for sequence analysis and integration with RegTransBase and other microbial genome resources. The next update of MicrobesOnline will contain significant new functionality, including comparative analysis of metagenomic sequence data. Programmatic access to the database, along with source code and documentation, is available at http://microbesonline.org/programmers.html.

Proper citation: MicrobesOnline (RRID:SCR_005507) Copy   

•   Source: SciCrunch Registry

http://www.garban.org/garban/home.php

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 12, 2012. GARBAN is a tool for analysis and rapid functional annotation of data arising from cDNA microarrays and proteomics techniques. GARBAN has been implemented with bioinformatic tools to rapidly compare, classify, and graphically represent multiple sets of data (genes/ESTs, or proteins), with the specific aim of facilitating the identification of molecular markers in pathological and pharmacological studies. GARBAN has links to the major genomic and proteomic databases (Ensembl, GeneBank, UniProt Knowledgebase, InterPro, etc.), and follows the criteria of the Gene Ontology Consortium (GO) for ontological classifications. Source may be shared: e-mail garban (at) ceit.es. Platform: Online tool

Proper citation: GARBAN (RRID:SCR_005778) Copy   

•   Source: SciCrunch Registry

http://great.stanford.edu/public/html/splash.php

Data analysis service that predicts functions of cis-regulatory regions identified by localized measurements of DNA binding events across an entire genome. Whereas previous methods took into account only binding proximal to genes, GREAT is able to properly incorporate distal binding sites and control for false positives using a binomial test over the input genomic regions. GREAT incorporates annotations from 20 ontologies and is available as a web application. The utility of GREAT extends to data generated for transcription-associated factors, open chromatin, localized epigenomic markers and similar functional data sets, and comparative genomics sets. Platform: Online tool

Proper citation: GREAT: Genomic Regions Enrichment of Annotations Tool (RRID:SCR_005807) Copy   

•   Source: SciCrunch Registry

http://brainnetworks.sourceforge.net

Brain Networks: Code to perform network analysis on brain imaging data.

Proper citation: Brain Networks (RRID:SCR_005841) Copy   

•   Source: SciCrunch Registry

http://www.brain-map.org/api/index.html

API and demo application for accessing the Allen Brain Atlas Mouse Brain data. Data available via the API includes download high resolution images, expression data from a 3D volume, 3D coordinates of the Allen Reference Atlas, and searching genes with similar gene expression profiles using NeuroBlast. Data made available includes: * High resolution images for gene expression, connectivity, and histology experiments, as well as annotated atlas images * 3-D expression summaries registered to a reference space for the Mouse Brain and Developing Mouse Brain * Primary microarray results for the Human Brain and Non-Human Primate * RNA sequencing results for the Developing Human Brain * MRI and DTI files for Human Brain The API consists of the following resources: * RESTful model access * Image download service * 3-D expression summary download service * Differential expression search services * NeuroBlast correlative searches * Image-to-image synchronization service * Structure graph download service

Proper citation: Allen Brain Atlas API (RRID:SCR_005984) Copy   

•   Source: SciCrunch Registry

http://www.stanford.edu/~nigam/cgi-bin/dokuwiki/doku.php?id=clench

Cluster Enrichment (CLENCH) allows A. thaliana researchers to perform automated retrieval of GO annotations from TAIR and calculate enrichment of GO terms in gene group with respect to a reference set. Before calculating enrichment, CLENCH allows mapping of the returned annotations to arbitrary coarse levels using GO slim term lists (which can be edited by the user) and a local installation of GO. Platform: Windows compatible, Linux compatible,

Proper citation: CLENCH (RRID:SCR_005735) Copy   

•   Source: SciCrunch Registry

http://evolution.genetics.washington.edu/phylip.html

A free package of software programs for inferring phylogenies (evolutionary trees). The source code is distributed (in C), and executables are also distributed. In particular, already-compiled executables are available for Windows (95/98/NT/2000/me/xp/Vista), Mac OS X, and Linux systems. Older executables are also available for Mac OS 8 or 9 systems.

Proper citation: PHYLIP (RRID:SCR_006244) Copy   

•   Source: SciCrunch Registry

http://www.birncommunity.org/tools-catalog/human-imaging-database-hid/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented October 5, 2017.

Database management system developed to handle the increasingly large and diverse datasets collected as part of the MBIRN and FBIRN collaboratories and throughout clinical imaging communities at large. The HID can be extended to contain relevant information concerning experimental subjects, assessments of subjects, the experimental data collected, the experimental protocols, and other metadata normally included with experiments.

Proper citation: Human Imaging Database (RRID:SCR_006126) Copy   

•   Source: SciCrunch Registry

https://compbio.dfci.harvard.edu/predictivenetworks//

A flexible, open-source, web-based application and data services framework that enables the integration, navigation, visualization and analysis of gene interaction networks. The primary goal of PN is to allow biomedical researchers to evaluate experimentally derived gene lists in the context of large-scale gene interaction networks. The PN analytical pipeline involves two key steps. The first is the collection of a comprehensive set of known gene interactions derived from a variety of publicly available sources. The second is to use these ''known'' interactions together with gene expression data to infer robust gene networks. The regression-based network inference algorithm creates a graph of gene interactions in which cycles may be present (but no self-loops). Based on information-theoretic techniques, a causal gene interaction network is inferred from both prior knowledge (interactions extracted from biomedical literature and structured biological databases) and gene expression data. A prediction model is fitted for each gene, given its parents, enabling assessment of the predictive ability of the network model.

Proper citation: Predictive Networks (RRID:SCR_006110) Copy   

•   Source: SciCrunch Registry

http://insitu.fruitfly.org/cgi-bin/ex/insitu.pl

Database of embryonic expression patterns using a high throughput RNA in situ hybridization of the protein-coding genes identified in the Drosophila melanogaster genome with images and controlled vocabulary annotations. At the end of production pipeline gene expression patterns are documented by taking a large number of digital images of individual embryos. The quality and identity of the captured image data are verified by independently derived microarray time-course analysis of gene expression using Affymetrix GeneChip technology. Gene expression patterns are annotated with controlled vocabulary for developmental anatomy of Drosophila embryogenesis. Image, microarray and annotation data are stored in a modified version of Gene Ontology database and the entire dataset is available on the web in browsable and searchable form or MySQL dump can be downloaded. So far, they have examined expression of 7507 genes and documented them with 111184 digital photographs.

Proper citation: Patterns of Gene Expression in Drosophila Embryogenesis (RRID:SCR_002868) Copy   

•   Source: SciCrunch Registry

http://function.princeton.edu/GOLEM/index.html

THIS RESOURCE IS NO LONGER IN SERVICE, documented July 7, 2017. Welcome to the home of GOLEM: An interactive, graphical gene-ontology visualization, navigation,and analysis tool on the web. GOLEM is a useful tool which allows the viewer to navigate and explore a local portion of the Gene Ontology (GO) hierarchy. Users can also load annotations for various organisms into the ontology in order to search for particular genes, or to limit the display to show only GO terms relevant to a particular organism, or to quickly search for GO terms enriched in a set of query genes. GOLEM is implemented in Java, and is available both for use on the web as an applet, and for download as a JAR package. A brief tutorial on how to use GOLEM is available both online and in the instructions included in the program. We also have a list of links to libraries used to make GOLEM, as well as the various organizations that curate organism annotations to the ontology. GOLEM is available as a .jar package and a macintosh .app for use on- or off- line as a stand-alone package. You will need to have Java (v.1.5 or greater) installed on your system to run GOLEM. Source code (including Eclipse project files) are also available. GOLEM (Gene Ontology Local Exploration Map)is a visualization and analysis tool for focused exploration of the gene ontology graph. GOLEM allows the user to dynamically expand and focus the local graph structure of the gene ontology hierarchy in the neighborhood of any chosen term. It also supports rapid analysis of an input list of genes to find enriched gene ontology terms. The GOLEM application permits the user either to utilize local gene ontology and annotations files in the absence of an Internet connection, or to access the most recent ontology and annotation information from the gene ontology webpage. GOLEM supports global and organism-specific searches by gene ontology term name, gene ontology id and gene name. CONCLUSION: GOLEM is a useful software tool for biologists interested in visualizing the local directed acyclic graph structure of the gene ontology hierarchy and searching for gene ontology terms enriched in genes of interest. It is freely available both as an application and as an applet.

Proper citation: GOLEM An interactive, graphical gene-ontology visualization, navigation, and analysis tool (RRID:SCR_003191) Copy   

•   Source: SciCrunch Registry