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http://tdrtargets.org/

This database functions both as a website where researchers can look for information on their targets of interest; and as a tool for prioritization of targets in whole genomes. Using the database as a tool, researchers can quickly prioritize a genome of interest by performing any number of individual queries on a species of interest, then assigning numerical weights to each query (in the history page) to finally obtain a ranked list of genes by combining the weighted queries. This site is part of a WHO/TDR project seeking to exploit the availability of diverse datasets to facilitate the identification and prioritization of drug targets in pathogens causing neglected diseases.

Proper citation: TDR Targets Database (RRID:SCR_007963) Copy   

•   Source: SciCrunch Registry

http://ppdb.tc.cornell.edu/

A Plant Proteome DataBase for Arabidopsis thaliana and maize (Zea mays). The PPDB stores experimental data from in-house proteome and mass spectrometry analysis, curated information about protein function, protein properties and subcellular localization. Importantly, proteins are particularly curated for possible (intra) plastid location and their plastid function. Protein accessions identified in published Arabidopsis (and other Brassicacea) proteomics papers are cross-referenced to rapidly determine previous experimental identification by mass spectrometry. All protein-encoding gene models in the Arabidopsis nuclear and organellar genomes, as assembled by TAIR, as well as all maize EST assemblies (ZmGI) as assembled by DFCI Maize Gene Index project. These are all uploaded in PPDB and are linked to each other via a BLAST alignment. Thus every predicted protein in both species can be searched for experimental and other information (even if not experimentally identified).

Proper citation: PPDB: Plant Proteomics Database (RRID:SCR_007872) Copy   

•   Source: SciCrunch Registry

http://www.poxvirus.org

A database of information on pox viruses. Goals of this project are to acquire and annotate data on poxviruses, and to develop and utilize new tools to facilitate the study of this group of organisms. This basic research is being undertaken with an eye toward the development of novel antiviral therapies, vaccines against human orthopoxvirus infections, new approaches for the environmental detection of virions, and methods to accomplish more rapid diagnosis of disease.

Proper citation: Poxvirus Bioinformatics Resource Center (RRID:SCR_007870) Copy   

•   Source: SciCrunch Registry

http://carta.anthropogeny.org/moca/about

The Museum of Comparative Anthropogeny (MOCA) is a collection of comparative information regarding humans and our closest evolutionary cousins (chimpanzees, bonobos, gorillas and orangutans i.e, great apes), with an emphasis on uniquely human features. MOCA is organized by Domains, each grouping Topics by areas of interest and scientific discipline. Each topic entry will eventually cover existing information about a particular difference (alleged or documented) between humans and non-human hominids. Comparisons of these non-human hominids with humans are difficult, as so little is known about their phenotypic features (phenomes), in contrast to humans. Ethical, fiscal and practical issues also limit collection of further information about great apes. MOCA attempts to collect existing information about human-specific differences from great apes, currently scattered in the literature. Having such information in one location could lead to new insights and multi-disciplinary interactions, and to ethically-sound studies to explain differences, and uniquely human specializations. MOCA is not targeted at experts in specific disciplines, but rather aims to communicate basic information to a broad audience of scientists from many backgrounds, and to the interested lay public. MOCA includes not only aspects wherein there are known or apparent differences between humans and great apes, but additionally, topics for which popular wisdom about claimed or assumed differences is not entirely correct. It is for all these reasons that MOCA is called a Museum, and not an Encyclopedia or Database.

Proper citation: MOCA (RRID:SCR_010638) Copy   

•   Source: SciCrunch Registry

http://www.cgl.ucsf.edu/

Biomedical technology resource center that develops software and web-based resources for the visualization and analysis of molecular structure, and related data, at scales ranging from the atomic to the supramolecular. They create tools for handling and integrating diverse types of biomolecular data, including atomic-resolution coordinates, density maps, sequences, annotations, and networks. Their primary efforts are in the visualization and analysis of structures of molecules and molecular assemblies, enzyme sequence-structure-function relationships, and network representations of protein similarity, binding interactions, and biological pathways. They provide technologies to enable identifying the molecular bases of disease and phenotypic variation, annotating proteins of unknown function, identifying targets for drug development, designing drugs, and engineering proteins with new functions. RBVI distributes software tools, including the popular UCSF Chimera visualization and analysis package, develops and hosts the Structure-Function Linkage Database, and provides access to state-of-the-art computational resources in support of research projects in these areas.

Proper citation: Resource for Biocomputing Visualization and Informatics (RRID:SCR_001374) Copy   

•   Source: SciCrunch Registry

http://www.novusbio.com/

Commercial antibody vendor which supplies antibodies and other products to life science researchers.

Proper citation: Novus Biologicals (RRID:SCR_004286) Copy   

•   Source: SciCrunch Registry

http://www.biopieces.org

A collection of bioinformatics tools that can be pieced together in a very easy and flexible manner to perform both simple and complex tasks. The Biopieces work on a data stream in such a way that the data stream can be passed through several different Biopieces, each performing one specific task: modifying or adding records to the data stream, creating plots, or uploading data to databases and web services. The Biopieces are executed in a command line environment where the data stream is initialized by specific Biopieces which read data from files, databases, or web services, and output records to the data stream that is passed to downstream Biopieces until the data stream is terminated at the end of the analysis. The advantage of the Biopieces is that a user can easily solve simple and complex tasks without having any programming experience. Moreover, since the data format used to pass data between Biopieces is text based, different developers can quickly create new Biopieces in their favorite programming language - and all the Biopieces will maintain compatibility. Finally, templates exist for creating new Biopieces in Perl and Ruby. There are currently ~190 Biopieces (March 2014).

Proper citation: Biopieces (RRID:SCR_005783) Copy   

•   Source: SciCrunch Registry

http://www.biochem.ucl.ac.uk/bsm/virus_database/VIDA3/VIDA.html

VIDA contains a collection of homologous protein families derived from open reading frames from complete and partial virus genomes. For each family, users can get an alignment of the conserved regions, functional and taxonomy information, and links to DNA sequences and structures. * Search homologous protein families from particular virus families * Links to complete genome sequence: Arteriviridae, Coronaviridae, Herpesviridae, Poxviridae The Virus Database at University College London has been developed as a system to organize animal virus open reading frame sequences. All known and predicted protein sequences from complete and partial genomes of particular virus families are extracted from GenBank and filtered to remove 100% redundancy. On the basis of sequence similarity the sequences are then clustered into homologous protein families (HPFs). The families are enriched with annotations including function and functional classification, related protein structures, taxonomy, length of the proteins, boundaries of the conserved region/s, virus-specific gene name and links to EMBL entries and SWISSPROT., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: VIDA (RRID:SCR_007111) Copy   

•   Source: SciCrunch Registry

http://www.nrcam.uchc.edu/

Biomedical technology research center that develops new technologies for modeling cell biological processes. The technologies are integrated through Virtual Cell, a problem-solving environment built on a central database and disseminated as a Web application for the analysis, modeling and simulation of cell biological processes. NRCAM resides at the Center for Cell Analysis and Modeling, CCAM, and provides a vast array of laboratory equipment that can be used for obtaining experimental data needed to create and enhance Virtual Cell models. Microscopy instrumentation includes three confocal laser scanning microscopes including UV excitation, nonlinear optical microscopy utilizing a titanium sapphire pulsed laser, confocal-based fluorescence correlation spectroscopy, wide-field imaging workstation with cooled CCD and rapid excitation filter wheel, and dual-wavelength spectrofluorometer. Access to the facilities and technical staff is open to all researchers., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: NRCAM (RRID:SCR_006134) Copy   

•   Source: SciCrunch Registry

http://www.census.gov/did/www/nlms/

A database based on a random sample of the noninstitutionalized population of the United States, developed for the purpose of studying the effects of demographic and socio-economic characteristics on differentials in mortality rates. It consists of data from 26 U.S. Current Population Surveys (CPS) cohorts, annual Social and Economic Supplements, and the 1980 Census cohort, combined with death certificate information to identify mortality status and cause of death covering the time interval, 1979 to 1998. The Current Population Surveys are March Supplements selected from the time period from March 1973 to March 1998. The NLMS routinely links geographical and demographic information from Census Bureau surveys and censuses to the NLMS database, and other available sources upon request. The Census Bureau and CMS have approved the linkage protocol and data acquisition is currently underway. The plan for the NLMS is to link information on mortality to the NLMS every two years from 1998 through 2006 with research on the resulting database to continue, at least, through 2009. The NLMS will continue to incorporate data from the yearly Annual Social and Economic Supplement into the study as the data become available. Based on the expected size of the Annual Social and Economic Supplements to be conducted, the expected number of deaths to be added to the NLMS through the updating process will increase the mortality content of the study to nearly 500,000 cases out of a total number of approximately 3.3 million records. This effort would also include expanding the NLMS population base by incorporating new March Supplement Current Population Survey data into the study as they become available. Linkages to the SEER and CMS datasets are also available. Data Availability: Due to the confidential nature of the data used in the NLMS, the public use dataset consists of a reduced number of CPS cohorts with a fixed follow-up period of five years. NIA does not make the data available directly. Research access to the entire NLMS database can be obtained through the NIA program contact listed. Interested investigators should email the NIA contact and send in a one page prospectus of the proposed project. NIA will approve projects based on their relevance to NIA/BSR''s areas of emphasis. Approved projects are then assigned to NLMS statisticians at the Census Bureau who work directly with the researcher to interface with the database. A modified version of the public use data files is available also through the Census restricted Data Centers. However, since the database is quite complex, many investigators have found that the most efficient way to access it is through the Census programmers. * Dates of Study: 1973-2009 * Study Features: Longitudinal * Sample Size: ~3.3 Million Link: *ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/00134

Proper citation: National Longitudinal Mortality Study (RRID:SCR_008946) Copy   

•   Source: SciCrunch Registry

http://blanco.biomol.uci.edu/membrane_proteins_xtal.html

Table providing information about integral membrane proteins whose crystallographic, or sometimes NMR, structures have been determined to a resolution sufficient to identify TM helices of helix-bundle membrane proteins (typically 4 - 4.5 angstroms). It is based upon Preusch et al. (1998) as revised by White & Wimley (1999). Reference is made to all of the protein types whose structures have been determined. They have attempted to make the database as inclusive as possible.

Proper citation: Mpstruct (RRID:SCR_013284) Copy   

•   Source: SciCrunch Registry