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http://www.uwyo.edu/

Public land grant research university in Laramie, Wyoming.

Proper citation: University of Wyoming; Wyoming; USA (RRID:SCR_007020) Copy   

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http://prodom.prabi.fr/

Comprehensive set of protein domain families automatically generated from UniProt Knowledge Database. Automated clustering of homologous domains generated from global comparison of all available protein sequences., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: ProDom (RRID:SCR_006969) Copy   

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http://www.physionet.org/physiobank/database/gaitdb/

A mini-collection of human gait data that was constructed as a teaching resource for an intensive course (The Modern Science of Human Aging, conducted at MIT) that includes walking stride interval time series from 15 subjects: 5 healthy young adults (23 - 29 years old), 5 healthy old adults (71 - 77 years old), and 5 older adults (60 - 77 years old) with Parkinson's disease. For each subject, two columns of data are included. The first column is time (in seconds) and the second is the stride interval (variously known as stride time, gait cycle duration, and time between successive heel strikes of the same foot). The same data are also available as standard PhysioBank-format annotation (.str) and header (.hea) files, for viewing or analysis using PhysioToolkit software from this site. Subjects walked continuously on level ground around an obstacle-free path. The stride interval was measured using ultra-thin, force sensitive resistors placed inside the shoe. The analog force signal was sampled at 300 Hz with a 12 bit A/D converter, using an ambulatory, ankle-worn microcomputer that also recorded the data. Subsequently, the time between foot-strikes was automatically computed. The method for determining the stride interval is a modification of a previously validated method that has been shown to agree with force-platform measures, a gold standard. Data were collected from the healthy subjects as they walked in a roughly circular path for 15 minutes, and from the subjects with Parkinson's disease as they walked for 6 minutes up and down a long hallway.

Proper citation: Gait in Aging and Disease Database (RRID:SCR_006886) Copy   

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http://www.nichd.nih.gov/research/supported/seccyd/Pages/overview.aspx

It is the most comprehensive child care study conducted to date to determine how variations in child care are related to children''s development. The NICHD SECCYD is a longitudinal study initiated by The National Institute of Child Health and Human Development (NICHD) in 1989 to answer the many questions about the relationship between child care experiences and characteristics and children''s developmental outcomes. After a thorough scientific review, the NICHD selected a research team located at universities across the U.S., and at the NICHD, together providing multiple perspectives on and interests in child care research. The network was led and managed by a Steering Committee which included an independent chairperson, one representative from each of the grantee sites, one representative from the data center and one representative from NICHD. The Steering Committee established policies and procedures that governed the operations of the network, including its publication procedures. The progress of the study was monitored by NICHD and by the Steering Committee with guidance from an Advisory Board which was nominated by the Director of NICHD. This team of researchers worked cooperatively to design and implement the study, and in 1991, enrolled a very diverse sample of children and their families at 10 locations across the U.S. The NICHD SECCYD is characterized by a complex and detailed study design which takes into account many variables, including characteristics of the child care and the family environment. Researchers assessed children''s development using multiple methods (trained observers, interviewers, questionnaires, and testing) and measuring many facets of children''s development (social, emotional, intellectual, language development, behavioral problems and adjustment, and physical health). The 1,364 children and their families enrolled in the study were followed from birth to age 3 years during Phase I of the study from 1991-1994. Phase II of the study was conducted between 1995-2000 to follow the 1226 children and families continuing to participate from age 54 months through their second year in school. Phase III of the study was conducted between 2000 - 2005 to follow over 1100 of the children through their seventh year in school. Phase IV was conducted between 2006 2007 to follow over 1000 of the original families through age 15. The NICHD SECCYD was conducted by a network of investigators, the NICHD Early Child Care Research Network. You may view information regarding data assessments, study publications, as well as a listing of the study researchers and committee members on the study website located at http://secc.rti.org. Qualified researchers are able to become affiliates with the study to utilize data from all phases of the study. As of January 2009, the Inter-University Consortium for Political and Social Research (ICPSR) at the University of Michigan assumed responsibility for the administration of data use agreements for the Phase I IV data. The ICPSR Data Use Agreement can be found at the following location: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/21940/documentation. If you have questions regarding the ICPSR process, please contact Russel Hathaway at rhataway (at) umich.edu.

Proper citation: NICHD SECCYD (RRID:SCR_006920) Copy   

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http://bioconductor.org/packages/2.9/bioc/html/RamiGO.html

Software package with an R interface sending requests to AmiGO visualize, retrieving DAG GO trees, parsing GraphViz DOT format files and exporting GML files for Cytoscape. Also uses RCytoscape to interactively display AmiGO trees in Cytoscape.

Proper citation: RamiGO (RRID:SCR_006922) Copy   

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http://www.seqan.de/projects/razers/

A read mapping software program with adjustable sensitivity based on counting q-grams. RazerS 3 supports shared-memory parallelism, an additional seed-based filter with adjustable sensitivity, a much faster, banded version of the Myers? bit-vector algorithm for verification, memory saving measures and support for the SAM output format. This leads to a much improved performance for mapping reads, in particular long reads with many errors.

Proper citation: RazerS (RRID:SCR_006889) Copy   

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http://scicrunch.org

THIS RESOURCE IS NO LONGER IN SERVICE, documented on August 27, 2019.

Database for those interested in the consequences of Factor VIII genetic variation at the DNA and protein level, it provides access to data on the molecular pathology of haemophilia A. The database presents a review of the structure and function of factor VIII and the molecular genetics of haemophilia A, a real time update of the biostatistics of each parameter in the database, a molecular model of the A1, A2 and A3 domains of the factor VIII protein (based on the crystal structure of caeruloplasmin) and a bulletin board for discussion of issues in the molecular biology of factor VIII. The database is completely updated with easy submission of point mutations, deletions and insertions via e-mail of custom-designed forms. A methods section devoted to mutation detection is available, highlighting issues such as choice of technique and PCR primer sequences. The FVIII structure section now includes a download of a FVIII A domain homology model in Protein Data Bank format and a multiple alignment of the FVIII amino-acid sequences from four species (human, murine, porcine and canine) in addition to the virtual reality simulations, secondary structural data and FVIII animation already available. Finally, to aid navigation across this site, a clickable roadmap of the main features provides easy access to the page desired. Their intention is that continued development and updating of the site shall provide workers in the fields of molecular and structural biology with a one-stop resource site to facilitate FVIII research and education. To submit your mutants to the Haemophilia A Mutation Database email the details. (Refer to Submission Guidelines)

Proper citation: HAMSTeRS - The Haemophilia A Mutation Structure Test and Resource Site (RRID:SCR_006883) Copy   

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

SAS is a tool for applying structural information to a given protein sequence. It uses FASTA to scan a given protein sequence against all the proteins of known 3D structure in the Protein Data Bank and provides functional residue annotation based on data from the Catalytic Site Atlas and PDBsum. The web service is aimed to facilitate the use of the SAS tool when having a huge number of queries. Currently, the web service provides annotation for binding sites (to ligand, metal or nucleic acid), catalytic residues and amino acids related to protein-protein interactions.

Proper citation: WSsas - Web Service for the SAS tool (RRID:SCR_007051) Copy   

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http://code.google.com/p/crop-tingchenlab/

A clustering tool designed mainly for Metagenomics studies, which clusters 16S rRNA sequences into Operational Taxonomic Units (OTU). By using a Gaussian Mixture model, CROP can automatically determine the best clustering result for 16S rRNA sequences at different phylogenetic levels without setting a hard cutoff threshold as hierarchical clustering does. Yet, at the same time, it is able to manage large datasets and to overcome sequencing errors.

Proper citation: CROP (RRID:SCR_006916) Copy   

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http://sourceforge.net/p/fastsemsim/home/Home/

A package that implements several semantic similarity measures. It is both a library and an end-user application, featuring an intuitive graphical user interface (GUI). It has been implemented with the aim of being fast, expandable, and easy to use. It allows the user to work with the most updated version of GO database and customizable annotation corpora. It provides a set of logically-organized classes that can be easily exploited to both integrate semantic similarity into different analysis pipelines and extend the library with new measures. Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: FastSemSim (RRID:SCR_006919) Copy   

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http://www.cjdats.org

A cooperative research program to explore the issues related to the complex system of offender treatment services. Nine research centers and a Coordinating Center were created in partnership with researchers, criminal justice professionals, and drug abuse treatment practitioners to form a national research infrastructure. The establishment of CJ-DATS is an outstanding example of cooperation among Federal agencies with the research community... We need to understand how to provide better drug treatment services for criminal justice offenders to alter their drug use and criminal behavior. - Dr. Nora Volkow, Director of NIDA. CJ-DATS PHASE I In 2002, NIDA launched the National Criminal Justice����������Drug Abuse Treatment Studies (CJ-DATS). CJ-DATS is a multisite research program aimed at improving the treatment of offenders with drug use disorders and integrating criminal justice and public health responses to drug involved offenders. From 2002 through 2008, CJ-DATS researchers from 9 research centers, a coordinating center, and NIDA worked together with federal, state, and local criminal justice partners to develop and test integrated approaches to the treatment of offenders with drug use disorders. The areas that were studied included: * Assessing Offender Problems * Measuring Progress in Treatment and Recovery * Linking Criminal Justice and Drug Abuse Treatment * Adolescent Interventions * HIV and Hepatitis Risk Reduction * Understanding Systems CJ-DATS PHASE II In 2008, CJ-DATS began to focus on the problems of implementing research-based practices drug treatment practices. This research concerns the organizational and systems processes involved in implementing valid, evidence-based practices to reduce drug use and drug-related recidivism for individuals in the criminal justice system. 12 CJ-DATS Research Centers are conducting implementation research in three primary domains: * Research to improve the implementation of evidence-based assessment processes for offenders with drug problems * Implementing effective treatment for drug-involved offenders * Implementing evidence-based interventions to improve an HIV continuum-of-care for offenders

Proper citation: Criminal Justice Drug Abuse Treatment Studies (RRID:SCR_006996) Copy   

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http://www.stanleyresearch.org/dnn/Default.aspx?tabid=203

The Stanley Medical Research Institute (SMRI) is a nonprofit organization supporting research on the causes of, and treatments for, schizophrenia and bipolar disorder. Since it began in 1989, SMRI has supported more than $300 million in research in over 30 countries around the world. It is the largest nongovernmental source of funds for research on these diseases in the United States. Schizophrenia and bipolar disorder are the most important psychiatric disorders in the United States, affecting more than 4 million people at any given time. Until recent years, little research had been done on these diseases, and the treatment of them was unsatisfactory. The neuroscience revolution has brought with it great opportunities for increased understanding of brain diseases such as schizophrenia and bipolar disorder. SMRI is on the leading edge of this exciting research. Approximately 75 percent of SMRI expenditures goes towards the development of new treatments for schizophrenia and bipolar disorder. The remaining funds are used for research on the causes of these diseases. SMRI has a close relationship with and is the supporting organization for the Treatment Advocacy Center (TAC). The Treatment Advocacy Center is a nonprofit organization dedicated to eliminating barriers to the timely and effective treatment of severe psychiatric disorders. TAC promotes laws, policies, and practices for the delivery of psychiatric care and supports the development of innovative treatments for and research into the causes of severe and persistent psychiatric disorders, such as schizophrenia and bipolar disorder.

Proper citation: Stanley Medical Research Institute (RRID:SCR_007047) Copy   

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http://ardb.cbcb.umd.edu

The goals of Antibiotic Resistance Genes Database (ARGB) are to provide a centralized compendium of information on antibiotic resistance, to facilitate the consistent annotation of resistance information in newly sequenced organisms, and also to facilitate the identification and characterization of new genes. ARGB contains six types of database groups: - Resistance Type: This database contains information, such as resistance profile, mechanism, requirement, epidemiology for each type. - Resistance Gene: This database contains information, such as resistance profile, resistance type, requirement, protein and DNA sequence for each gene.This database only includes NON-REDUNDANT, NON-VECTOR, COMPLETE genes. - Antibiotic: This database contains information, such as producer, action mechanism, resistance type, for each gene. - Resistance Gene(NonRD): This database contains the same information as Resistance Gene. It does NOT include NON-REDUNDANT, NON-VECTOR genes, but includes INCOMPLETE genes. - Resistance Gene(ALL): This database contains the same information as Resistance Gene. It includes all REDUNDANT, VECTOR AND INCOMPLETE genes. - Resistance Species: This database contains resistance profile and corresponding resistance genes for each species. Furthermore, ARDB also contians three types BLAST database: - Resistance Genes Complete: Contains only NON-REDUNDANT, NON-VECTOR, COMPLETE genes sequences. - Resistance Genes Non-redundant: Contains NON-REDUNDANT, NON-VECTOR, COMPLETE, INCOMPLETE genes sequences. - Resistance Genes All: Contains all REDUNDANT, VECTOR, COMPLETE, INCOMPLETE genes sequences. Lastly, ARDB provides four types of Analytical tools: - Normal BLAST: This function allows an user to input a DNA or protein sequence, and find similar DNA (Nucleotide BLAST) or protein (Protein BLAST) sequences using blastn, blastp, blastx, tblastn, tblastx - RPS BLAST: A web RPSBLAST (RPS BLAST) interface is provided to align a query sequence against the Position Specific Scoring Matrix (PSSM) for each type. Normally, this will give the same annotation information as using regular BLAST mentioned above. - Multiple Sequences BLAST (Genome Annotation): This function allows an user to annotate multiple (less than 5000) query sequences in FASTA format. - Mutation Resistance Identification: This function allows an user to identify mutations that will cause potential antibiotic resistance, for 12 genes (16S rRNA, 23S rRNA, gyrA, gyrB, parC, parE, rpoB, katG, pncA, embB, folP, dfr). ������ :Sponsors: ARDB is funded by Uniformed Services University of the Health Sciences, administered by the Henry Jackson Foundation. :

Proper citation: Antibiotic Resistance Genes Database (RRID:SCR_007040) Copy   

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http://xin.cz3.nus.edu.sg/group/drt/dart.asp

Database that provides comprehensive information about adverse effect targets of drugs described in the literature, including information about known drug adverse reaction targets, functions and properties. Moreover, proteins involved in adverse effect targets of chemicals not yet confirmed as adverse drug reaction (ADR) targets are also included as potential targets. Associated references are also included. This database gives physiological function of each target, binding drugs / agonists / antagonists / activators / inhibitors, IC(50) values of the inhibitors, corresponding adverse effects, and type of ADR induced by drug binding to a target. Cross-links to other databases are also introduced to facilitate the access of information about the sequence, 3-dimensional structure, function, and nomenclature of each target along with drug/ligand binding properties, and related literature. Each entry can be retrieved through multiple search methods including target name, target physiological function, adverse effect, ligand name, and biological pathways. A special page is provided for contribution of new or additional information. Function for ADR-target prediction by SVMDART: Submit protein primary sequence for ADR-related protein prediction.

Proper citation: DART - Drug Adverse Reaction Targets (RRID:SCR_007041) Copy   

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http://seer.cancer.gov/

SEER collects cancer incidence data from population-based cancer registries covering approximately 47.9 percent of the U.S. population. The SEER registries collect data on patient demographics, primary tumor site, tumor morphology, stage at diagnosis, and first course of treatment, and they follow up with patients for vital status.There are two data products available: SEER Research and SEER Research Plus. This was motivated because of concerns about the increasing risk of re-identifiability of individuals. The Research Plus databases require more rigorous process for access that includes user authentication through Institutional Account or multiple-step request process for Non-Institutional users.

Proper citation: Surveillance Epidemiology and End Results (RRID:SCR_006902) Copy   

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http://geneontology.org/docs/tools-overview/

Collection of tools developed by GO Consortium and by third parties. Tools are listed by category or alphabetically and continue to be improved and expanded.

Proper citation: Gene Ontology Tools (RRID:SCR_006941) Copy   

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https://www.siumed.edu/pharm

Pharmacology research teams and faculty study mechanisms of disease, develop and test novel drug therapies and seek to understand and prevent drug interactions. They also educate and train medical students, graduate students, residents and fellows in field of pharmacology.

Proper citation: Southern Illinois University School of Medicine Department of Pharmacology (RRID:SCR_006940) Copy   

•   Source: SciCrunch Registry

http://gtrnadb.ucsc.edu

This genomic tRNA database contains tRNA gene predictions made by the program tRNAscan-SE (Lowe & Eddy, Nucl Acids Res 25: 955-964, 1997) on complete or nearly complete genomes. Unless otherwise noted, all annotation is automated, and has not been inspected for agreement with published literature. Transfer RNAs (tRNAs) represent the single largest, best-understood class of non-protein coding RNA genes found in all living organisms. By far, the major source of new tRNAs is computational identification of genes within newly sequenced genomes. To organize the rapidly growing collection and enable systematic analyses, we created the Genomic tRNA Database (GtRNAdb). The web resource provides overview statistics of tRNA genes within each analyzed genome, including information by isotype and genetic locus, easily downloadable primary sequences, graphical secondary structures and multiple sequence alignments. Direct links for each gene to UCSC eukaryotic and microbial genome browsers provide graphical display of tRNA genes in the context of all other local genetic information. The database can be searched by primary sequence similarity, tRNA characteristics or phylogenetic group. Inevitably with automated sequence analysis, we find exceptions to general identification rules, isoacceptor type predictions (esp. due to variable post-transcriptional anticodon modification), and questionable tRNA identifications (due to pseudogenes, SINES, or other tRNA-derived elements). We attempt to document all cases we come across, and welcome feedback on new or unrecognized discrepancies.

Proper citation: GtRNAdb - Genomic tRNA Database (RRID:SCR_006939) Copy   

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http://www.europhenome.org

Open source software system for capturing, storing and analyzing raw phenotyping data from SOPs contained in EMPReSS, it provides access to raw and annotated mouse phenotyping data generated from primary pipelines such as EMPReSSlim and secondary procedures from specialist centers. Mutants of interest can be identified by searching the gene or the predicted phenotype. You can also access phenotype data from the EMPReSSlim Pipeline for inbred mouse strains. Initially EuroPhenome was developed within the EUMORPHIA programme to capture and store pilot phenotyping data obtained on four background strains (C57BL/6J, C3H/HeBFeJ, BALB/cByJ and 129/SvPas). EUMORPHIA (European Union Mouse Research for Public Health and Industrial Applications) was a large project comprising of 18 research centers in 8 European countries, with the main focus of the project being the development of novel approaches in phenotyping, mutagenesis and informatics to improve the characterization of mouse models for understanding human molecular physiology and pathology. The current version of EuroPhenome is capturing data from the EUMODIC project as well as the WTSI MGP, HMGU GMC pipeline and the CMHD. EUMODIC is undertaking a primary phenotype assessment of up to 500 mouse mutant lines derived from ES cells developed in the EUCOMM project as well as other lines. Lines showing an interesting phenotype will be subject to a more in depth assessment. EUMODIC is building upon the comprehensive database of standardized phenotyping protocols, called EMPReSS, developed by the EUMORPHIA project. EUMODIC has developed a selection of these screens, called EMPReSSslim, to enable comprehensive, high throughput, primary phenotyping of large numbers of mice. Phenovariants are annotated using a automated pipeline, which assigns a MP term if the mutant data is statistically different to the baseline data. This data is shown in the Phenomap and the mine for a mutant tool. Please note that a statistically significant result and the subsequent MP annotation does not necessarily mean a true phenovariant. There are other factors that could cause this result that have not been accounted for in the analysis. It is the responsibility of the user to download the data and use their expert knowledge or further analysis to decide whether they agree or not. EuroPhenome is primarily based in the bioinformatics group at MRC Harwell. The development of EuroPhenome is in collaboration with the Helmholtz Zentrum Munchen, Germany, the Wellcome Trust Sanger Institute, UK and the Institut Clinique de la Souris, France.

Proper citation: Europhenome Mouse Phenotyping Resource (RRID:SCR_006935) Copy   

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http://scalablebrainatlas.incf.org/

A web-based, interactive brain atlas viewer, containing a growing number of atlas templates for various species, including mouse, macaque and human. Standard features include fast brain region lookup, point and click to select a region and view its full 3D extent, mark a stereotaxic coordinate and view all regions in a hierarchy. Built-in extensions are the CoCoMac plugin, which provides a spatial display of Macaque connectivity, and a service to transform stereotaxic coordinates to and from the INCF Waxholm space for the mouse. Three dimensional renderings of brain regions are available through a Matlab interface (local installation of Matlab required). The SBA is designed to be customizable. External users can create plugins, hosted on their own servers, to interactively attach images or data to spatial atlas locations. This fully web-based display engine for brain atlases and topologies allows client websites to show brain region related data in a 3D interactive context. Currently available atlases are: * Macaque: The Paxinos Rhesus Monkey atlas (2000) * Macaque: Various templates available through Caret, registered to F99 space: Felleman and Van Essen (1991), Lewis and Van Essen (2000), Regional Map from K��tter and Wanke (2005), Paxinos Rhesus Monkey (2000) * Macaque: The NeuroMaps Macaque atlas (2008) * Mouse: The INCF Waxholm Space for the mouse (2011). Previous versions available. * Mouse: The Allen Mouse Brain volumetric atlas (ABA07) * Human: The LPBA40 parcellation, registered to SRI24 space A variety of services are being developed around the templates contained in the Scalable Brain Atlas. For example, you can include thumbnails of brain regions in your own webpage. Other applications include: * Analyze atlas templates in Matlab * List all regions belonging to the given template * List of supported atlas templates * Find region by coordinate * Color-coded PNG (bitmap) or SVG (vector) image of a brain atlas slice * Region thumbnail in 2D (slice) or 3D (stack of slices) The Scalable Brain Atlas is created by Rembrandt Bakker and Gleb Bezgin, under supervision of Rolf K��tter in the NeuroPhysiology and -Informatics group of the Donders Institute, Radboud UMC Nijmegen.

Proper citation: Scalable Brain Atlas (RRID:SCR_006934) Copy   

•   Source: SciCrunch Registry