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https://github.com/Cai-Lab-at-University-of-Michigan/nTracer

Software tool as plug-in for ImageJ software. Used for tracing microscopic images.

Proper citation: nTracer (RRID:SCR_023032) Copy   

•   Source: SciCrunch Registry

http://www.genes2cognition.org/db/Search

Database of protein complexes, protocols, mouse lines, and other research products generated from the Genes to Cognition project, a project focused on understanding molecular complexes involved in synaptic transmission in the brain.

Proper citation: Genes to Cognition Database (RRID:SCR_002735) Copy   

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http://sonorus.princeton.edu/hefalmp/

HEFalMp (Human Experimental/FunctionAL MaPper) is a tool developed by Curtis Huttenhower in Olga Troyanskaya's lab at Princeton University. It was created to allow interactive exploration of functional maps. Functional mapping analyzes portions of these networks related to user-specified groups of genes and biological processes and displays the results as probabilities (for individual genes), functional association p-values (for groups of genes), or graphically (as an interaction network). HEFalMp contains information from roughly 15,000 microarray conditions, over 15,000 publications on genetic and physical protein interactions, and several types of DNA and protein sequence analyses and allows the exploration of over 200 H. sapiens process-specific functional relationship networks, including a global, process-independent network capturing the most general functional relationships. Looking to download functional maps? Keep an eye on the bottom of each page of results: every functional map of any kind is generated with a Download link at the bottom right. Most functional maps are provided as tab-delimited text to simplify downstream processing; graphical interaction networks are provided as Support Vector Graphics files, which can be viewed using the Adobe Viewer, any recent version of Firefox, or the excellent open source Inkscape tool.

Proper citation: Human Experimental/FunctionAL MaPper: Providing Functional Maps of the Human Genome (RRID:SCR_003506) Copy   

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http://biosearch.berkeley.edu/

Developed as part of the BioText project at the University of California, Berkeley, the BioText Search Engine is a freely available Web-based application that provides biologists with new ways to access the scientific literature. The system indexes all open access articles available at PubMed Central. New articles are indexed daily. The current collection consists of more than 300 journals, 40,000 articles, 100,000 figures, and 60,000 tables. The Full Text & Abstract view searches the full text of articles (in addition to title, author, and abstract information) and returns full-text excerpts that match users' queries. Three selection boxes at the top (ABSTRACTS, FULL-TEXT EXCERPTS and FIGURES allow users to choose what the view displays. The BioText Search Engine allows users to search in tables. When the table view is selected, BioText searches in article titles, table captions, and table contents. The Grid View allows users to search over captions. It returns figures and truncated captions in a grid arrangement.

Proper citation: BioText Search Engine (RRID:SCR_003600) Copy   

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http://cmr.jcvi.org/cgi-bin/CMR/shared/GenomePropertiesHomePage.cgi

The Genome Properties system consists of a suite of Properties which are carefully defined attributes of prokaryotic organisms whose status can be described by numerical values or controlled vocabulary terms for individual completely sequenced genomes. The system has been designed to capture the widest possible range of attributes and currently encompasses taxonomic terms, genometric calculations, metabolic pathways, systems of interacting macromolecular components and quantitative and descriptive experimental observations (phenotypes) from the literature. You may search the Genome Properties Database in 1 of 3 ways: * Search For Predicted Properties in the CMR: The Genome Property Search allows you to search the Genome Property database for state information for selected genomes and properties. * Perform a Keyword Search for a Specific Property: Lists all Genome Properties that match a specific text string. You can choose to search All Fields within a genome property or the Property Name. * Browse Top Level Genome Properties: Click on the properties to see the specific genome property report page. The Genome Properties system presents key aspects of prokaryotic biology using standardized computational methods and controlled vocabularies. Properties reflect gene content, phenotype, phylogeny and computational analyses. The results of searches using hidden Markov models allow many properties to be deduced automatically, especially for families of proteins (equivalogs) conserved in function since their last common ancestor. Additional properties are derived from curation, published reports and other forms of evidence. Genome Properties system was applied to 156 complete prokaryotic genomes, and is easily mined to find differences between species, correlations between metabolic features and families of uncharacterized proteins, or relationships among properties.

Proper citation: JCVI GenProp (RRID:SCR_004592) Copy   

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http://pilgrm.princeton.edu

PILGRM (the platform for interactive learning by genomics results mining) puts advanced supervised analysis techniques applied to enormous gene expression compendia into the hands of bench biologists. This flexible system empowers its users to answer diverse biological questions that are often outside of the scope of common databases in a data-driven manner. This capability allows domain experts to quickly and easily generate hypotheses about biological processes, tissues or diseases of interest. Specifically PILGRM helps biologists generate these hypotheses by analyzing the expression levels of known relevant genes in large compendia of microarray data. PILGRM is for the biologist with a set of proteins relevant to a disease, biological function or tissue of interest who wants to find additional players in that process. It uses a data driven method that provides added value for literature search results by mining compendia of publicly available gene expression datasets using lists of relevant and irrelevant genes (standards). PILGRM produces publication quality PDFs usable as supplementary material to describe the computational approach, standards and datasets. Each PILGRM analysis starts with an important biological question (e.g. What genes are relevant for breast cancer but not mammary tissue in general?). For PILGRM to discover relevant genes, it needs examples of both genes that you would (positive) and would not (negative) find interesting. Lists of these genes are what we call standards and in PILGRM you can build your own standards or you can use standards from common sources that we pre-load for your convenience. PILGRM lets you build your own literature-documented standards so that processes, disease, and tissues that are not well covered in databases of tissue expression, disease, or function can still be used for an analysis.

Proper citation: PILGRM (RRID:SCR_004749) Copy   

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http://nbc.ece.drexel.edu/

Webserver for taxonomic classification of metagenomic reads.

Proper citation: NBC (RRID:SCR_004772) Copy   

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http://gila.bioengr.uic.edu/snp/toposnp

A topographic database for analyzing non-synonymous SNPs (nsSNPs) that can be mapped onto known 3D structures of proteins. These include disease- associated nsSNPs derived from the Online Mendelian Inheritance in Man (OMIM) database and other nsSNPs derived from dbSNP, a resource at the National Center for Biotechnology Information that catalogs SNPs. TopoSNP further classifies each nsSNP site into three categories based on their geometric location: those located in a surface pocket or an interior void of the protein, those on a convex region or a shallow depressed region, and those that are completely buried in the interior of the protein structure. These unique geometric descriptions provide more detailed mapping of nsSNPs to protein structures. It also includes relative entropy of SNPs calculated from multiple sequence alignment as obtained from the Pfam database (a database of protein families and conserved protein motifs) as well as manually adjusted multiple alignments obtained from ClustalW. These structural and conservational data can be useful for studying whether nsSNPs in coding regions are likely to lead to phenotypic changes. TopoSNP includes an interactive structural visualization web interface, as well as downloadable batch data.

Proper citation: TopoSNP (RRID:SCR_005572) Copy   

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http://bioinformatics.clemson.edu/G-SESAME/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 2,2025. G-SESAME contains a set of tools. They include: tools for measuring the semantic similarity of GO terms; tools for measuring the functional similarity of genes; and tools for clustering genes based on their GO term annotation information. Platform: Online tool, THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: G-SESAME - Gene Semantic Similarity Analysis and Measurement Tools (RRID:SCR_005816) Copy   

•   Source: SciCrunch Registry

http://dommino.org

DOMMINO is a comprehensive structural database on macromolecular interactions. As of June, 2011, it contains more than 407,000 binary interactions. The distinctive features of DOMMINO are: # Automated updates: DOMMINO is fully automated and is designed to update itself on a weekly basis, one day after a PDB weekly update. Thus, the community will be able to study macromolecular interactions almost immediately after they are released by PDB. # Coverage of non-domain mediated interactions: In addition to domain-domain and domain-peptide interactions the database characterizes the interaction between domains and unstructured protein regions that are not parts of a domain, such as inter-domain linkers and N- and C-termini. The interactions that involve the latter unstructured parts of proteins have been included to the database for the first time providing additional ~186,000 interactions (~45% of the total number of interactions, as of June, 2011). # Coverage of new structural domains: DOMMINO employs one of the most accurate structural classifications of proteins, SCOP. In addition to the existing SCOP-annotated domains, we employ a state-of-the-art machine learning approach to classify newer protein structures into existing SCOP families. With the progress of structural genomics, we do not expect a significant growth of the number of structurally novel folds or protein families and therefore our method allows covering almost all new protein structures. In total, using this predictive approach has allowed us to add more than 261,000 new interactions, almost twice as many as existing SCOP-annotated interactions. # The web-interface is designed to give the user a possibility of a flexible search as well as the capability to study macromolecular interactions in a PDB structure at the interaction network level and at the individual interface level. The web interface of the DOMMINO database includes a comprehensive list of help topics linked to the specific actions. In addition, we have designed a step-by-step tutorial that covers all aspects of working with the data from DOMMINO using the web interface.

Proper citation: DOMMINO - Database Of MacroMolecular INteractiOns (RRID:SCR_005958) Copy   

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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://www.icn.ucl.ac.uk/motorcontrol/imaging/propatlas.htm

A probabilistic atlas of the cerebellar lobules in the space defined by the MNI152 template. The anatomical definitions are based on the fMRI atlas of an individual cerebellum by Schmahmann et al. (2000). To obtain a representative anatomical atlas, we separately masked the lobules on T1-weighted MRI scans (1mm isotropic resolution) of 20 healthy young participants (10 male, 10 female, average age 23.7 yrs). Using a different set of 23 participants, we also masked the deep cerebellar nucelei. These cerebella were then aligned using different commonly used normalization algorithms. The resultant probabilistic maps allow for the valid assignment of functional activations to specific cerebellar lobules and the nuclei, while providing a quantitative measure of the certainty of such assignments. Furthermore, maximum probability maps derived from these atlases can be used to define regions of interest (ROIs) in functional neuroimaging and neuroanatomical research. The atlas is included in the newer releases of FSL and the Anatomy toolbox. More version of the atlases for use with MRICroN are also available.

Proper citation: Probabilistic atlas of the human cerebellum (RRID:SCR_008797) Copy   

•   Source: SciCrunch Registry

http://www.plantgdb.org/

Software tools and databases for plant genomics.

Proper citation: PlantGDB (RRID:SCR_013166) Copy   

•   Source: SciCrunch Registry

http://planteome.org

An international collaborative effort to develop and enrich new and existing reference ontologies for plants, improve ontology use and cross-references, and to develop data annotation standards. Users can search for ontology terms and bioentities and submit the ontology-related term requests by visiting the following GitHub request trackers.

Proper citation: Planteome (RRID:SCR_014411) Copy   

•   Source: SciCrunch Registry

http://research.bioinformatics.udel.edu/iptmnet/

A protein database which connects multiple disparate bioinformatics tools and systems text mining, data mining, analysis and visualization tools, and databases and ontologies.

Proper citation: iPTMnet (RRID:SCR_014416) Copy   

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http://www.scienceexchange.com/facilities/vcu-nanomaterials-characterization-center-vcu

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on April 15,2024. Nanomaterials Characterization Center at Virginia Commonwealth University is a state of the art 4000 sq. ft. facility located within the new Health and Life Science Engineering Facility. The Center provides an academic structure for students in natural sciences, mathematics, engineering, and medicine to participate in nanoscience and nanotechnology research to acquire the skills necessary to pursue such careers. In the past year, VCU received two National Science Foundation major research instrumentation grants totaling more than $1.6 million to expand its capabilities for research in materials science. Combining these federal awards with state instrumentation grants and private donations, the facility has been able to build a state of the art facility with over $5 million in new equipment. This new equipment will allow faculty and student researchers from both VCU campuses, as well as other universities.

Proper citation: VCU Nanomaterials Characterization Center (RRID:SCR_012162) Copy   

•   Source: SciCrunch Registry

http://www.marine-geo.org/portals/antarctic/

Accepts and provides access to geoscience data, primarily marine, collected from oceanographic expeditions in the Antarctic region. The synthesis began in 2003 as the Antarctic Multibeam Bathymetry and Geophysical Data Synthesis (AMBS) with a focus on multibeam bathymetry field data and other geophysical data from the Southern Ocean collected with the R/V N. B. Palmer. In 2005, the effort was expanded to include all routine underway geophysical and oceanographic data collected with both the R/V N. B. Palmer and R/V L. Gould, the two primary research vessels serving the US Antarctic Program. Data available include seafloor bathymetry, subbottom profiling, trackline gravity and magnetics, meteorological, and water column data as well as basic cruise information for all Palmer and Gould expeditions. Seafloor bathymetry data are provided both as raw swath data as well as in gridded form through the Global Multi-Resolution Topography (GMRT) synthesis. This gridded compilation of seafloor bathymetry data can be accessed through GeoMapApp, Create Maps and Grids and through an OGC-compliant Web Map Service. GeoMapApp is an integrated mapping application that provides access to many additional regional bathymetric grids, seismic, radar, gravity and magnetics profiles as well as other map and grid compilations for the Antarctic continent including LIMA.

Proper citation: Antarctic and Southern Ocean Data Portal (RRID:SCR_002193) Copy   

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http://psidonline.isr.umich.edu/

Long-term longitudinal dataset with information on generational links and socioeconomic and health conditions of individuals over time. The central foci of the data are economic and demographic, with substantial detail on income sources and amounts, wealth, savings, employment, pensions, family composition changes, childbirth and marriage histories, and residential location. Over the life of the PSID, the NIA has funded supplements on wealth, health, parental health and long term care, housing, and the financial impact of illness, thus also making it possible to model retirement and residential mobility. Starting in 1999, much greater detail on specific health conditions and health care expenses is included for respondent and spouse. Other enhancements have included a question series about emotional distress (2001); the two stem questions from the Composite International Diagnostic Interview to assess symptoms of major depression (2003); a supplement on philanthropic giving and volunteering (2001-03); a question series on Internet and computer use (2003); linkage to the National Death Index with cause of death information for more than 4,000 individuals through the 1997 wave, updated for each subsequent wave; social and family history variables and GIS-linked environmental data; basic data on pension plans; event history calendar methodology to facilitate recall of employment spells (2001). The reporting unit is the family: single person living alone or sharing a household with other non-relatives; group of people related by blood, marriage, or adoption; unmarried couple living together in what appears to be a fairly permanent arrangement. Interviews were conducted annually from 1968 through 1997; biennial interviewing began in 1999. There is an oversample of Blacks (30%). Waves 1990 through 1995 included a 20% Hispanic oversample; within the Hispanic oversample, Cubans and Puerto Ricans were oversampled relative to Mexicans. All data from 1994 through 2001 are available as public release files; prior waves can be obtained in archive versions. The special files with weights for families are also available. Restricted files include the Geocode Match File with information for 1968 through 2001, the 1968-2001 Death File, and the 1991 Medicare Claims File. * Dates of Study: 1968-2003 * Study Features: Longitudinal, Minority Oversampling * Sample Size: 65,000+ Links * ICPSR Series: http://www.icpsr.umich.edu/icpsrweb/ICPSR/series/00131 * ICPSR 1968-1999: Annual Core Data: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/07439 * ICPSR 1968-1999: Supplemental Files: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/03202 * ICPSR 1989-1990: Latino Sample: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/03203

Proper citation: Panel Study of Income Dynamics (RRID:SCR_008976) Copy   

•   Source: SciCrunch Registry

http://nis.princeton.edu/

Public use data set on new legal immigrants to the U.S. that can address scientific and policy questions about migration behavior and the impacts of migration. A survey pilot project, the NIS-P, was carried out in 1996 to inform the fielding and design of the full NIS. Baseline interviews were ultimately conducted with 1,127 adult immigrants. Sample members were interviewed at baseline, 6 months, and 12 months, with half of the sample also interviewed at three months. The first full cohort, NIS-2003, is based on a nationally representative sample of the electronic administrative records compiled for new immigrants by the US government. NIS-2003 sampled immigrants in the period May-November 2003. The geographic sampling design takes advantage of the natural clustering of immigrants. It includes all top 85 Metropolitan Statistical Areas (MSAs) and all top 38 counties, plus a random sample of other MSAs and counties. Interviews were conducted in respondents'' preferred languages. The baseline was multi-modal: 60% of adult interviews were administered by telephone; 40% were in-person. The baseline round was in the field from June 2003 to June 2004, and includes in the Adult Sample 8,573 respondents, 4,336 spouses, and 1,072 children aged 8-12. A follow-up was planned for 2007. Several modules of the NIS were designed to replicate sections of the continuing surveys of the US population that provide a natural comparison group. Questionnaire topics include Health (self-reports of conditions, symptoms, functional status, smoking and drinking history) and use/source/costs of health care services, depression, pain; background; (2) Background: Childhood history and living conditions, education, migration history, marital history, military history, fertility history, language skills, employment history in the US and foreign countries, social networks, religion; Family: Rosters of all children; for each, demographic attributes, education, current work status, migration, marital status and children; for some, summary indicators of childhood and current health, language ability; Economic: Sources and amounts of income, including wages, pensions, and government subsidies; type, value of assets and debts, financial assistance given/received to/from respondent from/to relatives, friends, employer, type of housing and ownership of consumable durables. * Dates of Study: 2003-2007 * Study Features: Longitudinal * Sample Size: 13,981

Proper citation: New Immigrant Survey (RRID:SCR_008973) Copy   

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https://nationalmaglab.org/user-facilities/icr

Facility provides service operations for sample analysis that requires ultrahigh resolution and high mass accuracy of Fourier Transform Ion Cyclotron Resonance. Used for research in biomolecular analysis, hydrogen-deuterium exchange and environmental and petrochemical analysis. Four FT-ICR mass spectrometers feature high magnetic fields including the world-record 21 tesla and are compatible with multiple ionization and fragmentation techniques.

Proper citation: National High Magnetic Field Laboratory Ion Cyclotron Resonance Core Facility (RRID:SCR_017361) Copy   

•   Source: SciCrunch Registry