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http://www.hormone.org/

A portal for hormone-related health information for the public, physicians, allied health professionals and the media. It serves as a resource for the public by promoting the prevention, treatment and cure of hormone-related conditions through outreach and education. It provides free educational materials, public forums, physician referral service, and media education campaigns. It offers a library of educational materials and programs covering a wide range of endocrine topics, including adrenal disorders, breast cancer, diabetes, osteoporosis, stress, thyroid disease and cancer.

Proper citation: Hormone Health Network (RRID:SCR_005765) Copy   

•   Source: SciCrunch Registry

https://www.google.com/docs/about/

Authoring tool to create, share, and collaborate on the web with documents, spreadsheets, presentations, and more in real time. All your changes are saved automatically in Drive.

Proper citation: Google Docs (RRID:SCR_005886) Copy   

•   Source: SciCrunch Registry

http://manuals.bioinformatics.ucr.edu/home/R_BioCondManual#GOHyperGAll

To test a sample population of genes for overrepresentation of GO terms, the R/BioC function GOHyperGAll computes for all GO nodes a hypergeometric distribution test and returns the corresponding p-values. A subsequent filter function performs a GO Slim analysis using default or custom GO Slim categories. Basic knowledge about R and BioConductor is required for using this tool. Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible, THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: GOHyperGAll (RRID:SCR_005766) Copy   

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https://edwardslab.bmcb.georgetown.edu/trac/GlycoPeptideSearch/#WelcometoGlycoPeptideSearch

GlycoPeptideSearch (GPS) simplifies data interpretation of N-glycopeptide CID MS/MS datasets by searching for glycopeptide results consistent with MS/MS spectra. Results are tabulated in Excel format. Accelerate and simplify interpretation of N-glycopeptide CID MS/MS spectra using GlycoPeptideSearch (GPS). This tool is designed for tandem mass-spectra acquired from proteolytic digests of purified glycoproteins modified with N-glycans and analyzed by LC-MS/MS and CID. The search yields an Excel spreadsheet of N-glycopeptide matches consistent with the spectra. GPS requires two files as input - an mzXML (or other open spectral format) file of glycopeptide CID tandem mass-spectra and a text file (.txt) of peptide sequences containing the N-linked glycosylation motif NXS/T. Spectral datafiles must be converted from raw vendor formats, such as .RAW or .wiff, to an open peak list format (mzXML preferred). In addition to these two input files, the user must specify one or more glycan databases (provided in the software package). The database(s) selected by the user will be used to match glycan structures in the glycopeptide spectra. The output is an Excel spreadsheet with one or more rows for spectra within the dataset that contain evidence of glycoprotein fragmentation, paired with one or more proposed glycopeptide matches for each spectrum. Glycopeptide matches consist of a peptide-glycan pair, with the peptide drawn from the user-supplied peptide file, and the glycan selected from a glycan database(s). The human subset of the GlycomeDB glycan database is provided, and N-linked glycans are automatically selected from it. GPS interprets glycopeptide CID MS/MS spectra by first requiring MS/MS spectra contain evidence of glycopeptide fragmentation - the oxonium ion peaks (m/z 204 - Hex, m/z 366 - HexNAc), and N-glycopeptide core specific peaks (peptide, peptide + HexNAc, peptide + HexNAc-HexNAc, peptide + HexNAc-HexNAc-Hex). For spectra that meet these initial criteria, for a particular peptide, a mass-based search of one or more glycan databases looks for glycans which capture the remaining mass of the spectral precursor. Additional spectral information may be used to narrow the number of matches, and equivalent glycan topologies may be collapsed to a single peptide-glycan pair. GPS also provides N-glycan compositions with the necessary additional mass, even if no glycan with the composition is present in the glycan database(s). GPS can either be run from the command-line or by using its graphical user interface. We recommend the msconvert (or MSConvertGUI) software from the ProteoWizard project to convert spectral datafiles from vendor formats such as .wiff and .RAW into mzXML.

Proper citation: GlycoPeptideSearch (RRID:SCR_005767) Copy   

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https://gene-atlas.brainminds.jp/

Database of gene expression in the marmoset brain.Comparative anatomy of marmoset and mouse cortex from genomic expression. Atlas comparing brain of neonatal marmoset with mouse using in situ hybridization.

Proper citation: Expression Atlas of the Marmoset (RRID:SCR_005760) Copy   

•   Source: SciCrunch Registry

http://www.cancer.fi/syoparekisteri/en/

The Finnish Cancer Registry maintains a nation-wide database on all cancer cases in Finland going back to 1953. It is also an internationally active institute for statistical and epidemiological cancer research. The Mass Screening Registry is a department of the Finnish Cancer Registry, and is responsible of planning and evaluating national cancer screening programs in Finland. The site contains information on cancer research and up to date statistics on the prevalence of different types of cancer in Finland, the Nordic countries and on a global level. The web pages include information for participants in cancer screening and for professionals involved in organizing such screening.

Proper citation: Finnish Cancer Registry (RRID:SCR_005881) Copy   

•   Source: SciCrunch Registry

http://snp-magma.sourceforge.net

Software that utilizes a multiobjective evolutionary algorithm for genetic mapping. It is based on a the ECJ evolutionary software package written by Sean Luke and includes the Strength Pareto Evoluationary Algorithm Version 2 changes for multiobjective analysis. The code runs on any platform with Java Version 2. A genetic mapping project, typically implemented during a search for genes responsible for a disease, requires the acquisition of a set of data from each of a large number of individuals. This data set includes the values of multiple genetic markers. These genetic markers occur at discrete positions along the genome, which is a collection of one or more linear chromosomes. Typing the value of a marker in an individual carries a cost; one seeks to minimize the number of markers typed without excessively jeopardizing the probability of detecting an association between a marker and a disease phenotype. MAGMA is a project which employ''s a multiobjective evolutionary algorithm to solve this problem.

Proper citation: MAGMA (RRID:SCR_005757) Copy   

•   Source: SciCrunch Registry

http://www.utsouthwestern.edu/education/medical-school/departments/neurology/programs/traumatic-brain-injury/index.html

The 16 affiliated Model System centers throughout the United States are responsible for gathering and submitting the core data set to the national database as well as conducting research studies on traumatic brain injury (TBI) both in collaboration with the other centers and within our own site. Through our research we hope to learn more about TBI and about the issues and concerns of people with TBI. Our goals are to improve the outcome and quality of life for people who have had brain injuries and for those who are caring for the person with a TBI. The North Texas Traumatic Brain Injury Model System (NT-TBIMS) pools the efforts and talents of individuals from the Departments of Neurosurgery, Neurology, Physical Medicine and Rehabilitation, Psychiatry (Neuropsychiatry), and Neuroradiology of the two leading medical institutions in the North Texas region. To be a patient involved in the research being conducted by the North Texas Traumatic Brain Injury Model System you must have suffered a TBI, be at least 16 years of age, have received initial treatment for the TBI at either Parkland Health and Hospital System or Baylor University Medical Center and then have received rehabilitative care at either Parkland, University Hospital Zale-Lipshy, or Baylor Institute for Rehabilitation. The patient must also be able to understand and sign an informed consent to participate or, if unable, have a family member or a legal guardian who understands the form sign the informed consent for the patient.

Proper citation: North Texas Traumatic Brain Injury Model System (RRID:SCR_005879) Copy   

•   Source: SciCrunch Registry

http://thedata.org/citation/standard

Citation standard that offers proper recognition to authors as well as permanent identification through the use of global, persistent identifiers in place of URLs, which can change frequently. Use of universal numerical fingerprints (UNFs) guarantees to the scholarly community that future researchers will be able to verify that data retrieved is identical to that used in a publication decades earlier, even if it has changed storage media, operating systems, hardware, and statistical program format.

Proper citation: Universal Numerical Fingerprint (RRID:SCR_005912) Copy   

•   Source: SciCrunch Registry

https://adaptivedisclosure.wordpress.com/aida/

A generic set of components that can perform a variety of tasks, such as learn new pattern recognition models, perform specialized search on resource collections, and store knowledge in a repository. W3C standards are used to make data accessible and manageable with semantic web technologies such as OWL, RDF(S), and SKOS. The AIDA Toolkit is directed at groups of knowledge workers that cooperatively search, annotate, interpret, and enrich large collections of heterogeneous documents from diverse locations. The server offers services for: text indexing and statistics, metadata storage and querying, thesaurus reasoning, annotation, text retrieval, spelling correction, synonym detection, and model learning.

Proper citation: AIDA Toolkit (RRID:SCR_005914) Copy   

•   Source: SciCrunch Registry

http://icahn.mssm.edu/

Icahn School of Medicine at Mount Sinai, formerly Mount Sinai School of Medicine, is graduate medical school in Manhattan, New York City. Leader in medical and scientific training and education, biomedical research and patient care.

Proper citation: Icahn School of Medicine at Mount Sinai; New York; USA (RRID:SCR_005793) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/Tools/pfa/iprscan/

Software package for functional analysis of sequences by classifying them into families and predicting presence of domains and sites. Scans sequences against InterPro's signatures. Characterizes nucleotide or protein function by matching it with models from several different databases. Used in large scale analysis of whole proteomes, genomes and metagenomes. Available as Web based version and standalone Perl version and SOAP Web Service.

Proper citation: InterProScan (RRID:SCR_005829) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/webservices/whatizit/info.jsf

A text processing system that allows you to do textmining tasks on text. It is great at identifying molecular biology terms and linking them to publicly available databases. Whatizit is also a Medline abstracts retrieval/search engine. Instead of providing the text by Copy&Paste, you can launch a Medline search. The abstracts that match your search criteria are retrieved and processed by a pipeline of your choice. Whatizit is also available as 1) a webservice and as 2) a streamed servlet. The webservice allows you to enrich content within your website in a similar way as in the wikipedia. The streamed servlet allows you to process large amounts of text.

Proper citation: Whatizit (RRID:SCR_005824) Copy   

•   Source: SciCrunch Registry

http://www.ibridgenetwork.org/wustl/splinter

Software that detects and quantifies short IN/DELs as well as single nucleotide substitutions in pooled-DNA samples.

Proper citation: SPLINTER (RRID:SCR_005826) Copy   

•   Source: SciCrunch Registry

http://umbbd.msi.umn.edu/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on August 27, 2014. Database containing information on microbial biocatalytic reactions and biodegradation pathways for primarily xenobiotic, chemical compounds. Its goal is to provide information on microbial enzyme-catalyzed reactions that are important for biotechnology. The reactions covered are studied for basic understanding of nature, biocatalysis leading to specialty chemical manufacture, and biodegradation of environmental pollutants. Individual reactions and metabolic pathways are presented with information on the starting and intermediate chemical compounds, the organisms that transform the compounds, the enzymes, and the genes. The present database has been successfully used to teach enzymology and use of biochemical Internet information resources to advanced undergraduate and graduate students, and is being expanded primarily with the help of such students. In addition to reactions and pathways, this database also contains Biochemical Periodic Tables and a Pathway Prediction System. * Search the UM-BBD for compound, enzyme, microorganism, pathway, or BT rule name; chemical formula; chemical structure; CAS Registry Number; or EC code. * Go to Pathways and Metapathways in the UM-BBD * Lists of 203 pathways; 1400 reactions; 1296 compounds; 916 enzymes; 510 microorganism entries; 245 biotransformation rules; 50 organic functional groups; 76 reactions of naphthalene 1,2-dioxygenase; 109 reactions of toluene dioxygenase; Graphical UM-BBD Overview; and Other Graphics (Metapathway and Pathway Maps and Reaction Mechanisms).

Proper citation: UM-BBD (RRID:SCR_005787) Copy   

•   Source: SciCrunch Registry

Ratings or validation data are available for this resource

http://genome.ucsc.edu/

Portal to interactively visualize genomic data. Provides reference sequences and working draft assemblies for collection of genomes and access to ENCODE and Neanderthal projects. Includes collection of vertebrate and model organism assemblies and annotations, along with suite of tools for viewing, analyzing and downloading data.

Proper citation: UCSC Genome Browser (RRID:SCR_005780) Copy   

•   Source: SciCrunch Registry

http://pmgn.vbi.vt.edu

The Oomycete Molecular Genetics Research Collaboration Network (OMGN) is a network for research collaboration for investigators interested in oomycete molecular genetics and genomics. The goals of the OMGN is to facilitate the integration of these investigators into the community and to further strengthen the cooperative culture of this community. A particular emphasis is placed on training and integrating junior faculty and faculty from institutions under-represented in the U.S. research infrastructure. Because of their economic impact as plant pathogens, molecular, genetic and genomics studies are well advanced in many oomycete species. These organisms have served as lead species for the entire Stramenopiles lineage, a major radiation of crown eukaryotes, distinct from plants, animals and fungi. The oomycete molecular genetics community has a strong culture of collaboration and communication, and sharing of techniques and resources. With the recent blossoming of genetic and genomic tools for oomycetes, many new investigators, from a variety of backgrounds, have become interested in oomycete molecular genetics and genomics. The proposed network is open to all researchers with an interest in oomycete molecular genetics and genomics, either at an experimental or a computational level. Investigators new to the field are always welcome, especially those interested in saprophytes and animal pathogens. Goals of OMGN # Provide training to o��mycete molecular genetics researchers, especially those from smaller institutions, in the use of bioinformatics and genomics resources. # Promote the entry, participation and training of new investigators into the field of o��mycete genomics, particularly junior faculty and faculty from institutions under-represented in the U.S. research infrastructure. # Promote communication and collaboration, and minimize duplication of effort, within the worldwide o��mycete genomics community. # Support an O��mycete Genomics Resources Center to maintain and distribute training and research materials produced by community genomics projects. The network''s activities have been supported by two grants from the NSF Research Collaboration Networks in Biology program.

Proper citation: OMGN (RRID:SCR_005781) Copy   

•   Source: SciCrunch Registry

http://www.patternlabforproteomics.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented July 5, 2018. Gene Ontology Explorer (GOEx) combines data from protein fold changes with GO over-representation statistics to help draw conclusions in proteomic experiments. It is tightly integrated within the PatternLab for Proteomics project and, thus, lies within a complete computational environment that provides parsers and pattern recognition tools designed for spectral counting. GOEx offers three independent methods to query data: an interactive directed acyclic graph, a specialist mode where key words can be searched, and an automatic search. A recent hack included in GOEx is to load the sparse matrix index file directly into GOEx, instead of going through the report generation using the AC/T-fold methods. This makes it easy for GOEx to analyze any list of proteins as long as the list follows the index file format (described in manuscript) . Please note that if using this alternative strategy, there will be no protein fold information. Platform: Windows compatible

Proper citation: GOEx - Gene Ontology Explorer (RRID:SCR_005779) Copy   

•   Source: SciCrunch Registry

https://www.applichem.com/home/

An Antibody supplier

Proper citation: AppliChem (RRID:SCR_005814) Copy   

•   Source: SciCrunch Registry

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

OWLTools (aka OWL2LS - OWL2 Life Sciences) is a java API for accessing ontologies in either OBO or OWL. OWLTools provides a bio-ontologies friendly wrapper on top of the Manchester OWL API. It provides many features, including: * convenience methods for OBO-like properties such as synonyms, textual definitions, obsoletion, replaced_by * simple graph-like operations over ontologies * visualization using the QuickGO graphs libraries Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: OWLTools (RRID:SCR_005732) Copy   

•   Source: SciCrunch Registry