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https://www.eagle-i.net/

Web application to discover resources available at participating networked universities. This distributed platform for creating and sharing semantically rich data is built around semantic web technologies and follows linked open data principles.

Proper citation: Eagle I (RRID:SCR_013153) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/validate29/

Atlas was created from MRI scans of squirrel monkey brains. The atlas is currently comprised of multiple anatomical templates, diffusion MRI templates, and ex vivo templates. In addition, the templates are combined with histologically defined cortical labels, and diffusion tractography defined white matter labels.

Proper citation: VALiDATe29 Squirrel Monkey Brain Atlas (RRID:SCR_015542) Copy   

•   Source: SciCrunch Registry

https://github.com/fmaguire/Bridger_Assembler

Software package as de novo trascriptome assembler for RNA-Seq data. Framework for de novo transcriptome assembly using RNA-seq data. Can assemble all transcripts from short reads without using reference. Input RNA-Seq reads in fasta or fastq format, and ouput all assembled candidate transcripts in fasta format. Operating system Unix/Linux.

Proper citation: Bridger (RRID:SCR_017039) Copy   

•   Source: SciCrunch Registry

https://modbase.compbio.ucsf.edu/foxs/

Web server for computing theoretical scattering profile of structure and fitting of experimental profile. Computes SAXS profile of given atomistic model and fits it to experimental profile. Used for structural modeling applications with small angle X-ray scattering data.

Proper citation: FoXS (RRID:SCR_017269) Copy   

•   Source: SciCrunch Registry

http://www.bioinformatics.ucla.edu/ASAP2

THIS RESOURCE IS NO LONGER IN SERVICE, documented on 8/12/13. An expanded version of the Alternative Splicing Annotation Project (ASAP) database with a new interface and integration of comparative features using UCSC BLASTZ multiple alignments. It supports 9 vertebrate species, 4 insects, and nematodes, and provides with extensive alternative splicing analysis and their splicing variants. As for human alternative splicing data, newly added EST libraries were classified and included into previous tissue and cancer classification, and lists of tissue and cancer (normal) specific alternatively spliced genes are re-calculated and updated. They have created a novel orthologous exon and intron databases and their splice variants based on multiple alignment among several species. These orthologous exon and intron database can give more comprehensive homologous gene information than protein similarity based method. Furthermore, splice junction and exon identity among species can be valuable resources to elucidate species-specific genes. ASAP II database can be easily integrated with pygr (unpublished, the Python Graph Database Framework for Bioinformatics) and its powerful features such as graph query, multi-genome alignment query and etc. ASAP II can be searched by several different criteria such as gene symbol, gene name and ID (UniGene, GenBank etc.). The web interface provides 7 different kinds of views: (I) user query, UniGene annotation, orthologous genes and genome browsers; (II) genome alignment; (III) exons and orthologous exons; (IV) introns and orthologous introns; (V) alternative splicing; (IV) isoform and protein sequences; (VII) tissue and cancer vs. normal specificity. ASAP II shows genome alignments of isoforms, exons, and introns in UCSC-like genome browser. All alternative splicing relationships with supporting evidence information, types of alternative splicing patterns, and inclusion rate for skipped exons are listed in separate tables. Users can also search human data for tissue- and cancer-specific splice forms at the bottom of the gene summary page. The p-values for tissue-specificity as log-odds (LOD) scores, and highlight the results for LOD >= 3 and at least 3 EST sequences are all also reported.

Proper citation: Alternative Splicing Annotation Project II Database (RRID:SCR_000322) Copy   

•   Source: SciCrunch Registry

http://mus.well.ox.ac.uk/gscandb/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. Database / display tool of genome scans, with a web interface that lets the user view the data. It does not perform any analyses - these must be done by other software, and the results uploaded into it. The basic features of GSCANDB are: * Parallel viewing of scans for multiple phenotypes. * Parallel analyses of the same scan data. * Genome-wide views of genome scans * Chromosomal region views, with zooming * Gene and SNP Annotation is shown at high zoom levels * Haplotype block structure viewing * The positions of known Trait Loci can be overlayed and queried. * Links to Ensembl, MGI, NCBI, UCSC and other genome data browsers. In GSCANDB, a genome scan has a wide definition, including not only the usual statistical genetic measures of association between genetic variation at a series of loci and variation in a phenotype, but any quantitative measure that varies along the genome. This includes for example competitive genome hybridization data and some kinds of gene expression measurements.

Proper citation: WTCHG Genome Scan Viewer (RRID:SCR_001635) Copy   

•   Source: SciCrunch Registry

http://www.isi.edu/projects/bioscholar/overview

Knowledge management and engineering system software for experimental biomedical scientists permitting a single scientific worker (at the level of a graduate student or postdoctoral worker) to design, construct and manage a shared knowledge repository for a research group derived on a local store of PDF files. Usability is especially emphasized within a laboratory so that this software could provide support to experimental scientists attempting to construct a personalized representation of their own knowledge on a medium scale. The BioScholar system uses a graphical interface to create experimental designs based on the experimental variables in the system. The design is then analyzed to construct a tabular input form based on the data flow. They call this methodology "Knowledge Engineering from Experimental Design" or "KEfED". The approach is domain-independent but domain-specific modules reasoning can be constructed to generate interpretations from the observational data represented in the KEfED model. The application is available for download as platform-specific installers including Linux, Unix, Mac OS, and Windows. The installer will install an application that will run the BioScholar server. This server uses Jetty as its integrated web server.

Proper citation: Bioscholar (RRID:SCR_001380) Copy   

•   Source: SciCrunch Registry

http://ecocyc.org/

Database for the bacterium Escherichia coli K-12 MG1655, the EcoCyc project performs literature-based curation of the entire genome, and of transcriptional regulation, transporters, and metabolic pathways. The long-term goal of the project is to describe the molecular catalog of the E. coli cell, as well as the functions of each of its molecular parts, to facilitate a system-level understanding of E. coli. EcoCyc is an electronic reference source for E. coli biologists, and for biologists who work with related microorganisms.

Proper citation: EcoCyc (RRID:SCR_002433) Copy   

•   Source: SciCrunch Registry

http://biositemaps.ncbcs.org/rds/search.html

Resource Discovery System is a web-accessible and searchable inventory of biomedical research resources. Powered by the Resource Discovery System (RDS) that includes a standards-based informatics infrastructure * Biositemaps Information Model * Biomedical Resource Ontology Extensions * Web Services distributed web-accessible inventory framework * Biositemap Resource Editor * Resource Discovery System Source code and project documentation to be made available on an open-source basis. Contributing institutions: University of Pittsburgh, University of Michigan, Stanford University, Oregon Health & Science University, University of Texas Houston. Duke University, Emory University, University of California Davis, University of California San Diego, National Institutes of Health, Inventory Resources Working Group Members

Proper citation: Resource Discovery System (RRID:SCR_005554) Copy   

•   Source: SciCrunch Registry

http://www.cbioportal.org/

A portal that provides visualization, analysis and download of large-scale cancer genomics data sets.

Proper citation: cBioPortal (RRID:SCR_014555) Copy   

•   Source: SciCrunch Registry

http://www.proteometools.org/index.php?id=home

Project for building molecular and digital tools from human proteome to facilitate biomedical research, drug discovery, personalized medicine and life science research.

Proper citation: ProteomeTools (RRID:SCR_018535) Copy   

•   Source: SciCrunch Registry

http://www.informatics.jax.org/home/recombinase

Curated data about all recombinase-containing transgenes and knock-ins developed in mice providing a comprehensive resource delineating known activity patterns and allows users to find relevant mouse resources for their studies.

Proper citation: Recombinase (cre) Activity (RRID:SCR_006585) Copy   

•   Source: SciCrunch Registry

https://open.med.harvard.edu/display/SHRINE/Community

Software providing a scalable query and aggregation mechanism that enables federated queries across many independently operated patient databases. This platform enables clinical researchers to solve the problem of identifying sufficient numbers of patients to include in their studies by querying across distributed hospital electronic medical record systems. Through the use of a federated network protocol, SHRINE allows investigators to see limited data about patients meeting their study criteria without compromising patient privacy. This software should greatly enable population-based research, assessment of potential clinical trials cohorts, and hypothesis formation for followup study by combining the EHR assets across the hospital system. In order to obtain the maximum number of cases representing the study population, it is useful to aggregate patient facts across as many sites as possible. Cutting across institutional boundaries necessitates that each hospital IRB remain in control, and that their local authority is recognized for each and every request for patient data. The independence, ownership, and legal responsibilities of hospitals predetermines a decentralized technical approach, such as a federated query over locally controlled databases. The application comes with the SHRINE Core Ontology but it can be used with any ontology, even one that is disease specific. The Core Ontology is designed to enable the widest range of studies possible using facts gathered in the EMR during routine patient care. SHRINE allows multiple ontologies to be used for different research purposes on the same installed systems.

Proper citation: SHRINE (RRID:SCR_006293) Copy   

•   Source: SciCrunch Registry

http://www.wakehealth.edu/Research/WFUPC/Cynomolgus-Breeding-Colony-Request-Form-Instructions.htm

The Wake Forest Cynomolgus Breeding Colony (CBC) is a colony of cynomolgus macaques (crab-eating macaques, Macaca fascicularis). The cynomolgus colony is designed to produce specific pathogen free (SPF) cynomolgus monkeys for use in biomedical research. The colony, supported by a grant from the NCRR, addresses the growing need for investigators to use in their protocols animals defined for the absence of specific diseases including CHV-1 (Herpes B), simian immunodeficiency virus, and simian retroviruses. An additional important characteristic of this colony is that, unlike many breeding colonies, the NHPs will be fed two defined diets. The first diet is a soy-free diet, not commercial monkey chow. The second diet has the same macronutrients but the protein source is from soy; similar in isoflavone content. A drawback of chow diets is that the exact nutritional product composition is unknown from lot to lot. However, they are always rich in soy bean meal, isoflavones and other constituents of soy bean meal that are known confounders of several types of research projects. All research using the cynomolgus colony must be reviewed and approved by the colony''s scientific board and the Wake Forest Animal Care and Use Committee (ACUC) before any work can be initiated. The scientific board meets regularly to assess the scientific value of each request and to determine whether or not animals/samples/data can be made available. This includes all requests for: # The purchase of animals for use outside the colony # The use of animals within the colony for the collection of blood/tissue samples, behavioral observations or other kinds of testing # The use of the CBC sample/tissue repository # The use of the CBC data repository

Proper citation: Wake Forest Cynomolgus Breeding Colony (RRID:SCR_006605) Copy   

•   Source: SciCrunch Registry

http://code.google.com/p/lapdftext/

Software that facilitates accurate extraction of text from PDF files of research articles for use in text mining applications. It is intended for both scientists and natural language processing (NLP) engineers interested in getting access to text within specific sections of research articles. The system extracts text blocks from PDF-formatted full-text research articles and classifies them into logical units based on rules that characterize specific sections. The LA-PDFText system focuses only on the textual content of the research articles. The current version of LA-PDFText is a baseline system that extracts text using a three-stage process: * identification of blocks of contiguous text * classification of these blocks into rhetorical categories * extraction of the text from blocks grouped section-wise.

Proper citation: lapdftext (RRID:SCR_006167) Copy   

•   Source: SciCrunch Registry

http://ccb.loni.usc.edu/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on August 31, 2022. Center focused on the development of computational biological atlases of different populations, subjects, modalities, and spatio-temporal scales with 3 types of resources: (1) Stand-alone computational software tools (image and volume processing, analysis, visualization, graphical workflow environments). (2) Infrastructure Resources (Databases, computational Grid, services). (3) Web-services (web-accessible resources for processing, validation and exploration of multimodal/multichannel data including clinical data, imaging data, genetics data and phenotypic data). The CCB develops novel mathematical, computational, and engineering approaches to map biological form and function in health and disease. CCB computational tools integrate neuroimaging, genetic, clinical, and other relevant data to enable the detailed exploration of distinct spatial and temporal biological characteristics. Generalizable mathematical approaches are developed and deployed using Grid computing to create practical biological atlases that describe spatiotemporal change in biological systems. The efforts of CCB make possible discovery-oriented science and the accumulation of new biological knowledge. The Center has been divided into cores organized as follows: - Core 1 is focused on mathematical and computational research. Core 2 is involved in the development of tools to be used by Core 3. Core 3 is composed of the driving biological projects; Mapping Genomic Function, Mapping Biological Structure, and Mapping Brain Phenotype. - Cores 4 - 7 provide the infrastructure for joint structure within the Center as well as the development of new approaches and procedures to augment the research and development of Cores 1-3. These cores are: (4)Infrastructure and Resources, (5) Education and Training, (6) Dissemination, and (7) Administration and Management. The main focus of the CCB is on the brain, and specifically on neuroimaging. This area has a long tradition of sophisticated mathematical and computational techniques. Nevertheless, new developments in related areas of mathematics and computational science have emerged in recent years, some from related application areas such as Computer Graphics, Computer Vision, and Image Processing, as well as from Computational Mathematics and the Computational Sciences. We are confident that many of these ideas can be applied beneficially to neuroimaging.

Proper citation: Center for Computational Biology at UCLA (RRID:SCR_000334) Copy   

•   Source: SciCrunch Registry

http://www.kojak-ms.org/

Software tool for identification of cross-linked peptides from mass spectra. Used for analysis of chemically cross-linked protein complexes. Used to analyze both novel and existing data sets.

Proper citation: Kojak (RRID:SCR_021028) Copy   

•   Source: SciCrunch Registry

https://www.researchmatch.org/

Free and secure registry to bring together two groups of people who are looking for one another: (1) people who are trying to find research studies, and (2) researchers who are looking for people to participate in their studies. It has been developed by major academic institutions across the country who want to involve you in the mission of helping today''''s studies make a real difference for everyone''''s health in the future. Anyone can join ResearchMatch. Many studies are looking for healthy people of all ages, while some are looking for people with specific health conditions. ResearchMatch can help ''''match'''' you with any type of research study, ranging from surveys to clinical trials, always giving you the choice to decide what studies may interest you.

Proper citation: ResearchMatch (RRID:SCR_006387) Copy   

•   Source: SciCrunch Registry

http://www.ctsaip.org/

Database that aggregates and markets technologies from CTSA institutions as well as those of the National Institutes of Health, with the goal of enhancing research activity and private partnerships across the CTSA consortium. Regular, automatic updating with a standardized template facilitates broad participation by CTSA consortium members. Currently, there are over a dozen CTSAs contributing information on their technologies to the site. CTSA-IP Mission * Intellectual Property information exchange * Links publicly available licensing opportunities from CTSI Institutions in an easily searchable format that connects providers & users. * Aim of creating a consortium view of IP, licensing & sponsored research opportunities. * Stimulus to collaboration and partnering with and between CTSA member institutions.

Proper citation: CTSA-IP (RRID:SCR_006380) Copy   

•   Source: SciCrunch Registry

http://ctdbase.org/

A public database that enhances understanding of the effects of environmental chemicals on human health. Integrated GO data and a GO browser add functionality to CTD by allowing users to understand biological functions, processes and cellular locations that are the targets of chemical exposures. CTD includes curated data describing cross-species chemical–gene/protein interactions, chemical–disease and gene–disease associations to illuminate molecular mechanisms underlying variable susceptibility and environmentally influenced diseases. These data will also provide insights into complex chemical–gene and protein interaction networks.

Proper citation: Comparative Toxicogenomics Database (CTD) (RRID:SCR_006530) Copy   

•   Source: SciCrunch Registry