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http://www.mouse-genome.bcm.tmc.edu/ENU/MutagenesisProj.asp

THIS RESOURCE IS NO LONGER IN SERVICE. For updated mutant information, please visit MMRRC or The Jackson Laboratory. Produces, characterizes, and distributes mutant mouse strains with defects in embryonic and postembryonic development. The goal of the ENU Mutagenesis project III is to determine the function of genes on mouse Chromosome 11 by saturating the chromosome with recessive mutations. The distal 40 cM of mouse Chr 11 exhibits linkage conservation with human Chromosome 17. We are using the chemical N-ethyl-N-nitrosourea (ENU) to saturate wild type chromosomes with point mutations. By determining the function of genes on a mouse chromosome, we can extrapolate to predict function on a human chromosome. We expect many of the new mutants to represent models of human diseases such as birth defects, patterning defects, growth and endocrine defects, neurological anomalies, and blood defects. Because many of the mutations we expect to isolate may be lethal or detrimental to the mice, we are using a unique approach to isolate mutations. This approach uses a balancer chromosome that is homozygous lethal and carries a dominant coat color marker to suppress recombination over a reasonable interval.

Proper citation: Mouse Mutagenesis Center for Developmental Defects (RRID:SCR_007321) Copy   

•   Source: SciCrunch Registry

http://cellprofiler.org

Software tool to enable biologists without training in computer vision or programming to quantitatively measure phenotypes from thousands of images automatically. It counts cells and also measures the size, shape, intensity and texture of every cell (and every labeled subcellular compartment) in every image. It was designed for high throughput screening but can perform automated image analysis for images from time-lapse movies and low-throughput experiments. CellProfiler has an increasing number of algorithms to identify and measure properties of neuronal cell types.

Proper citation: CellProfiler Image Analysis Software (RRID:SCR_007358) Copy   

•   Source: SciCrunch Registry

https://mibig.secondarymetabolites.org/

MIBiG is genomic standards consortium project and biosynthetic gene cluster database used as reference dataset. Provides community standard for annotations and metadata on biosynthetic gene clusters and their molecular products. Standardised data format that describes minimally required information to uniquely characterise biosynthetic gene clusters. MIBiG 2.0 is expended repository for biosynthetic gene clusters of known function. MIBiG 3.0 is database update comprising large scale validation and re-annotation of existing entries and new entries. Community driven effort to annotate experimentally validated biosynthetic gene clusters.

Proper citation: Minimum Information about Biosynthetic Gene cluster (RRID:SCR_023660) Copy   

•   Source: SciCrunch Registry

http://www.bindingdb.org

Web accessible database of data extracted from scientific literature, focusing on proteins that are drug-targets or candidate drug-targets and for which structural data are present in Protein Data Bank . Website supports query types including searches by chemical structure, substructure and similarity, protein sequence, ligand and protein names, affinity ranges and molecular weight . Data sets generated by BindingDB queries can be downloaded in form of annotated SDfiles for further analysis, or used as basis for virtual screening of compound database uploaded by user. Data are linked to structural data in PDB via PDB IDs and chemical and sequence searches, and to literature in PubMed via PubMed IDs .

Proper citation: BindingDB (RRID:SCR_000390) Copy   

•   Source: SciCrunch Registry

http://hanalyzer.sourceforge.net/

An open-source data integration system designed to assist biologists in explaining the results observed in genome-scale experiments as well as generating new hypotheses. It combines information extraction techniques, semantic data integration, and reasoning and facilitates network visualization. The Hanalyzer source code and binaries are available for download.

Proper citation: Hanalyzer (RRID:SCR_000923) 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

https://github.com/SciKnowEngine/kefed.io

Knowledge engineering software for reasoning with scientific observations and interpretations. The software has three parts: (a) the KEfED model editor - a design editor for creating KEfED models by drawing a flow diagram of an experimental protocol; (b) the KEfED data interface - a spreadsheet-like tool that permits users to enter experimental data pertaining to a specific model; (c) a "neural connection matrix" interface that presents neural connectivity as a table of ordinal connection strengths representing the interpretations of tract-tracing data. This tool also allows the user to view experimental evidence pertaining to a specific connection. The KEfED model is designed to provide a lightweight representation for scientific knowledge that is (a) generalizable, (b) a suitable target for text-mining approaches, (c) relatively semantically simple, and (d) is based on the way that scientist plan experiments and should therefore be intuitively understandable to non-computational bench scientists. The basic idea of the KEfED model is that scientific observations tend to have a common design: there is a significant difference between measurements of some dependent variable under conditions specified by two (or more) values of some independent variable.

Proper citation: Knowledge Engineering from Experimental Design (RRID:SCR_001238) Copy   

•   Source: SciCrunch Registry

http://scicrunch.org/resources

Portal providing identifiers for Antibodies, Model Organisms, and Tools (software, databases, services) created in support of the Resource Identification Initiative, which aims to promote research resource identification, discovery, and reuse. The portal offers a central location for obtaining and exploring Research Resource Identifiers (RRIDs) - persistent and unique identifiers for referencing a research resource. A critical goal of the RII is the widespread adoption of RRIDs to cite resources in the biomedical literature and other places that reference their generation or use. RRIDs use established community identifiers where they exist, and are cross-referenced in their system where more than one identifier exists for a single resource.

Proper citation: Resource Identification Portal (RRID:SCR_004098) Copy   

•   Source: SciCrunch Registry

http://publications.nigms.nih.gov/insidelifescience/

The NIGMS Inside Life Science series brings you inside the science of health. Each story shows how basic biomedical researchfrom the history of a field to the people doing cutting-edge work todaylays the foundation for advances in disease diagnosis, treatment and prevention. Through explorations of how the body works and highlights from recent studies, you''ll discover even more on what scientists have found and are finding about fundamental life processes. NIGMS supported all of the featured research.

Proper citation: NIGMS Inside Life Science (RRID:SCR_005852) Copy   

•   Source: SciCrunch Registry

http://mind.loni.usc.edu

The MiND: Metadata in NIfTI for DWI framework enables data sharing and software interoperability for diffusion-weighted MRI. This site provides specification details, tools, and examples of the MiND mechanism for representing important metadata for DWI data sets at various stages of post-processing. MiND framework provides a practical solution to the problem of interoperability between DWI analysis tools, and it effectively expands the analysis options available to end users. To assist both users and developers in working with MiND-formatted files, we provide a number of software tools for download. * MiNDHeader A utility for inspecting MiND-extended files. * I/O Libraries Programming libraries to simplify writing and parsing MiND-formatted data. * Sample Files Example files for each MiND schema. * DIRAC LONI''s Diffusion Imaging Reconstruction and Analysis Collection is a DWI processing suite which utilizes the MiND framework.

Proper citation: LONI MiND (RRID:SCR_004820) Copy   

•   Source: SciCrunch Registry

http://probalign.njit.edu/probalign/login

Data analysis service that computes maximal expected accuracy multiple sequence alignments from partition function posterior probabilities.

Proper citation: eProbalign (RRID:SCR_013247) Copy   

•   Source: SciCrunch Registry

http://web.mit.edu/glycomics/gt/gtdb.shtml

A pathway-based graphical interface for navigating the glycoenzyme database. The goal of the project is to define the paradigms by which carbohydrate binding proteins function in cellular communication. These pages are divided into six categories: -Glycosphingolipid: Sub-categories are Isogloboseries, Globoseries, Neo-lactoseries, Lactoseries and Ganglioseries - N-linked: Sub-categories are High-mannose, Hybrid and Complex -Mucin -Terminal Core 1 -Other O-linked -Terminal All: Includes all potential terminal structures for each glycan category

Proper citation: Glycosylation Pathways Database (RRID:SCR_013486) Copy   

•   Source: SciCrunch Registry

http://web.stanford.edu/group/barres_lab/brain_rnaseq.html

Database containing RNA-Seq transcriptome and splicing data from glia, neurons, and vascular cells of cerebral cortex. Collection of RNA-Seq transcriptome and splicing data from glia, neurons, and vascular cells of mouse cerebral cortex. RNA-Seq of cell types isolated from mouse and human brain.

Proper citation: Brain RNA-Seq (RRID:SCR_013736) Copy   

•   Source: SciCrunch Registry

http://hb.flatironinstitute.org/

Formerly known as GIANT (Genome-scale Integrated Analysis of gene Networks in Tissues), HumanBase applies machine learning algorithms to learn biological associations from massive genomic data collections. These integrative analyses reach beyond existing "biological knowledge" represented in the literature to identify novel, data-driven associations.

Proper citation: HumanBase (RRID:SCR_016145) Copy   

•   Source: SciCrunch Registry

http://www.broadinstitute.org/pubs/MitoCarta/

Collection of genes encoding proteins with strong support of mitochondrial localization. Inventory of genes encoding mitochondrial-localized proteins and their expression across 14 mouse tissues. Database is based on human and mouse RefSeq proteins that are mapped to NCBI Gene loci. MitoCarta 2.0 inventory provides molecular framework for system-level analysis of mammalian mitochondria.

Proper citation: MitoCarta (RRID:SCR_018165) Copy   

•   Source: SciCrunch Registry

http://software.broadinstitute.org/gsea/msigdb/index.jsp

Collection of annotated gene sets for use with Gene Set Enrichment Analysis (GSEA) software.

Proper citation: Molecular Signatures Database (RRID:SCR_016863) Copy   

•   Source: SciCrunch Registry

https://hdpm.biomedinfolab.com/netmage/

Web tool for automated generation of interactive disease-disease network visualizations given input PheWAS summary data. Given genetic associations from Phenome-Wide Association Study, disease-disease network can be constructed where nodes represent phenotypes and edges represent shared genetic associations between phenotypes.

Proper citation: NETMAGE (RRID:SCR_021843) Copy   

•   Source: SciCrunch Registry

http://oufti.org/

Software designed for analysis of microscopy data. It performs sub-pixel precision detection, quantification of cells and fluorescence signals, as well as other image analysis functions.

Proper citation: Oufti (RRID:SCR_016244) Copy   

•   Source: SciCrunch Registry

https://biochem.missouri.edu/chapman/software.htm

Software for fitting of atomic models into density maps derived from x-ray crystallography or electron microscopy.

Proper citation: RSRef (RRID:SCR_017211) Copy   

•   Source: SciCrunch Registry