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http://fairbrother.biomed.brown.edu/spliceman/index.cgi

An online tool that takes a set of DNA sequences with point mutations and returns a ranked list to predict the effects of point mutations on pre-mRNA splicing. The current implementation includes 11 genomes: human, chimp, rhesus, mouse, rat, dog, cat, chicken, guinea pig, frog and zebrafish.

Proper citation: Spliceman (RRID:SCR_005354) Copy   

•   Source: SciCrunch Registry

http://genetrail.bioinf.uni-sb.de/

A web-based application that analyzes gene sets for statistically significant accumulations of genes that belong to some functional category. Considered category types are: KEGG Pathways, TRANSPATH Pathways, TRANSFAC Transcription Factor, GeneOntology Categories, Genomic Localization, Protein-Protein Interactions, Coiled-coil domains, Granzyme-B clevage sites, and ELR/RGD motifs. The web server provides two statistical approaches, "Over-Representation Analysis" (ORA) comparing a reference set of genes to a test set, and "Gene Set Enrichment Analysis" (GSEA) scoring sorted lists of genes., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: GeneTrail (RRID:SCR_006250) Copy   

•   Source: SciCrunch Registry

http://neomorph.salk.edu/brain_methylomes/

THIS RESOURCE IS NO LONGER IN SERVICE. Datasets described in the manuscript: "Global Epigenomic Reconfiguration During Mammalian Brain Development" (Science, 2013 - DOI: 10.1126/science.1237905. This study provides genome-wide composition, patterning, cell specificity, and dynamics of DNA methylation at single-base resolution in human and mouse frontal cortex throughout their lifespan. Widespread methylome reconfiguration occurs during fetal to young adult development, coincident with synaptogenesis.

Proper citation: Mammalian Brain Methylomes (RRID:SCR_001648) Copy   

•   Source: SciCrunch Registry

http://www.neuro.mpg.de/connectomics

Data set of the dense reconstruction of 950 neurons and their mutual contacts for the mouse inner plexiform layer--the main computational neuropil region in the mammalian retina. This was achieved by applying a combination of crowd-sourced manual annotation and machine-learning-based volume segmentation to serial block-face electron microscopy data. They characterize a new type of retinal bipolar interneuron and show that they can subdivide a known type based on connectivity. Circuit motifs that emerge from their data indicate a functional mechanism for a known cellular response in a ganglion cell that detects localized motion, and predict that another ganglion cell is motion sensitive. A Data browser is also available for download

Proper citation: Connectomic reconstruction of the inner plexiform layer in the mouse retina (RRID:SCR_002246) Copy   

•   Source: SciCrunch Registry

http://bpg.utoledo.edu/~afedorov/lab/eid.html

Data sets of protein-coding intron-containing genes that contain gene information from humans, mice, rats, and other eukaryotes, as well as genes from species whose genomes have not been completely sequenced. This is a comprehensive and convenient dataset of sequences for computational biologists who study exon-intron gene structures and pre-mRNA splicing. The database is derived from GenBank release 112, and it contains protein-coding genes that harbor introns, along with extensive descriptions of each gene and its DNA and protein sequences, as well as splice motif information. They have created subdatabases of genes whose intron positions have been experimentally determined. The collection also contains data on untranslated regions of gene sequences and intron-less genes. For species with entirely sequenced genomes, species-specific databases have been generated. A novel Mammalian Orthologous Intron Database (MOID) has been introduced which includes the full set of introns that come from orthologous genes that have the same positions relative to the reading frames.

Proper citation: EID: Exon-Intron Database (RRID:SCR_002469) Copy   

•   Source: SciCrunch Registry

http://ftp://ftp.informatics.jax.org/pub/reports/MGI_PhenotypicAllele.rpt

Data set of collected and annotated expression and activity data for recombinase-containing transgenes and knock-in alleles. As the authoritative source of official names for mouse genes, alleles, and strains, MGI makes this list of transgenes available as a service and includes all known transgenes and synonyms. NIF provides a database interface so that researchers may have a better idea whether the trangene or transgenic animal that they are searching for is available.
Nomenclature follows the rules and guidelines established by the International Committee on Standardized Genetic Nomenclature for Mice.

Proper citation: Mouse Genome Informatics Transgenes (RRID:SCR_003468) Copy   

•   Source: SciCrunch Registry

http://degradome.uniovi.es/domains.html

Domains found in human and mouse proteases colour-coded according to the catalytic class in which they appear. Some of them appear in more than one catalytic group, and two-colours are used. Yellow, aspartyl proteases; blue, cysteine proteases; green, metalloproteases; and red, serine proteases.

Proper citation: Ancillary Domains Associated With Human and Mouse Proteases (RRID:SCR_008363) Copy   

•   Source: SciCrunch Registry

https://confluence.crbs.ucsd.edu/display/NIF/StemCellInfo

Data tables providing an overview of information about stem cells that have been derived from mice and humans. The tables summarize published research that characterizes cells that are capable of developing into cells of multiple germ layers (i.e., multipotent or pluripotent) or that can generate the differentiated cell types of another tissue (i.e., plasticity) such as a bone marrow cell becoming a neuronal cell. The tables do not include information about cells considered progenitor or precursor cells or those that can proliferate without the demonstrated ability to generate cell types of other tissues. The tables list the tissue from which the cells were derived, the types of cells that developed, the conditions under which differentiation occurred, the methods by which the cells were characterized, and the primary references for the information.

Proper citation: National Institutes of Health Stem Cell Tables (RRID:SCR_008359) Copy   

•   Source: SciCrunch Registry

http://krasnow1.gmu.edu/cn3/L-Neuron/database/

A database of virtually generated anatomically plausible neurons for several morphological classes, including cerebellar Purkinje cells, hippocampal pyramidal and granule cells, and spinal cord motoneurons. It presently contains 542 cells. In the trade neurons collection the database contains an amaral cell archive, neuron morpho reconstructions, and mouse alpha motoneurons. Their collection of generated neurons include motoneurons, Purkinje cells, and hippocampal pyramidal cells.

Proper citation: Virtual NeuroMorphology Electronic Database (RRID:SCR_007118) Copy   

•   Source: SciCrunch Registry

http://purl.bioontology.org/ontology/EMAP

A structured controlled vocabulary of stage-specific anatomical structures of the mouse (Mus).

Proper citation: Mouse Gross Anatomy and Development Ontology (RRID:SCR_003891) Copy   

•   Source: SciCrunch Registry

http://purl.bioontology.org/ontology/MPATH

A structured controlled vocabulary of mutant and transgenic mouse pathology phenotypes

Proper citation: Mouse Pathology Ontology (RRID:SCR_003950) Copy   

•   Source: SciCrunch Registry

http://openconnectomeproject.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 9, 2023. Connectomes repository to facilitate the analysis of connectome data by providing a unified front for connectomics research. With a focus on Electron Microscopy (EM) data and various forms of Magnetic Resonance (MR) data, the project aims to make state-of-the-art neuroscience open to anybody with computer access, regardless of knowledge, training, background, etc. Open science means open to view, play, analyze, contribute, anything. Access to high resolution neuroanatomical images that can be used to explore connectomes and programmatic access to this data for human and machine annotation are provided, with a long-term goal of reconstructing the neural circuits comprising an entire brain. This project aims to bring the most state-of-the-art scientific data in the world to the hands of anybody with internet access, so collectively, we can begin to unravel connectomes. Services: * Data Hosting - Their Bruster (brain-cluster) is large enough to store nearly any modern connectome data set. Contact them to make your data available to others for any purpose, including gaining access to state-of-the-art analysis and machine vision pipelines. * Web Viewing - Collaborative Annotation Toolkit for Massive Amounts of Image Data (CATMAID) is designed to navigate, share and collaboratively annotate massive image data sets of biological specimens. The interface is inspired by Google Maps, enhanced to allow the exploration of 3D image data. View the fork of the code or go directly to view the data. * Volume Cutout Service - RESTful API that enables you to select any arbitrary volume of the 3d database (3ddb), and receive a link to download an HDF5 file (for matlab, C, C++, or C#) or a NumPy pickle (for python). Use some other programming language? Just let them know. * Annotation Database - Spatially co-registered volumetric annotations are compactly stored for efficient queries such as: find all synapses, or which neurons synapse onto this one. Create your own annotations or browse others. *Sample Downloads - In addition to being able to select arbitrary downloads from the datasets, they have also collected a few choice volumes of interest. * Volume Viewer - A web and GPU enabled stand-alone app for viewing volumes at arbitrary cutting planes and zoom levels. The code and program can be downloaded. * Machine Vision Pipeline - They are building a machine vision pipeline that pulls volumes from the 3ddb and outputs neural circuits. - a work in progress. As soon as we have a stable version, it will be released. * Mr. Cap - The Magnetic Resonance Connectome Automated Pipeline (Mr. Cap) is built on JIST/MIPAV for high-throughput estimation of connectomes from diffusion and structural imaging data. * Graph Invariant Computation - Upload your graphs or streamlines, and download some invariants. * iPad App - WholeSlide is an iPad app that accesses utilizes our open data and API to serve images on the go.

Proper citation: Open Connectome Project (RRID:SCR_004232) Copy   

•   Source: SciCrunch Registry

http://brainarchitecture.org/

Evolving portal that will provide interactive tools and resources to allow researchers, clinicians, and students to discover, analyze, and visualize what is known about the brain's organization, and what the evidence is for that knowledge. This project has a current experimental focus: creating the first brainwide mesoscopic connectivity diagram in the mouse. Related efforts for the human brain currently focus on literature mining and an Online Brain Atlas Reconciliation Tool. The primary goal of the Brain Architecture Project is to assemble available knowledge about the structure of the nervous system, with an ultimate emphasis on the human CNS. Such information is currently scattered in research articles, textbooks, electronic databases and datasets, and even as samples on laboratory shelves. Pooling the knowledge across these heterogeneous materials - even simply getting to know what we know - is a complex challenge that requires an interdisciplinary approach and the contributions and support of the greater community. Their approach can be divided into 4 major thrusts: * Literature Curation and Text Mining * Computational Analysis * Resource Development * Experimental Efforts

Proper citation: Brain Architecture Project (RRID:SCR_004283) Copy   

•   Source: SciCrunch Registry

http://dkcoin.org/

THIS RESOURCE IS NO LONGER IN SERVICE, documented October 13, 2014. The resource has moved to the NIDDKInformation Network (dkNET) project. Contact them at info_at_dknet.org with any questions. Database of large pools of data relevant to the mission of NIDDKwith the goal of developing a community-based network for integration across disciplines to include the larger DKuniverse of diseases, investigators, and potential users. The focus is on greater use of this data with the objective of adding value by breaking down barriers between sites to facilitate linking of different datasets. To date (2013/06/10), a total of 1,195 resources have been associated with one or more genes. Of 11,580 total genes associated with resources, the ten most represented are associated with 359 distinct resources. The main method by which they currently interconnect resources between the providers is via EntrezGene identifiers. A total of 780 unique genes provide the connectivity between 3,159 resource pairs across consortia. To further increase interconnectivity, the groups have been further annotating their data with additional gene identifiers, publications, and ontology terms from selected Open Biological and Biomedical Ontologies (OBO).

Proper citation: dkCOIN (RRID:SCR_004438) Copy   

•   Source: SciCrunch Registry

http://stemcellcommons.org/

Open source environment for sharing, processing and analyzing stem cell data bringing together stem cell data sets with tools for curation, dissemination and analysis. Standardization of the analytical approaches will enable researchers to directly compare and integrate their results with experiments and disease models in the Commons. Key features of the Stem Cell Commons * Contains stem cell related experiments * Includes microarray and Next-Generation Sequencing (NGS) data from human, mouse, rat and zebrafish * Data from multiple cell types and disease models * Carefully curated experimental metadata using controlled vocabularies * Export in the Investigation-Study-Assay tabular format (ISA-Tab) that is used by over 30 organizations worldwide * A community oriented resource with public data sets and freely available code in public code repositories such as GitHub Currently in development * Development of Refinery, a novel analysis platform that links Commons data to the Galaxy analytical engine * ChIP-seq analysis pipeline (additional pipelines in development) * Integration of experimental metadata and data files with Galaxy to guide users to choose workflows, parameters, and data sources Stem Cell Commons is based on open source software and is available for download and development.

Proper citation: Stem Cell Commons (RRID:SCR_004415) Copy   

•   Source: SciCrunch Registry

http://www.brainarchitecture.org/mouse-home

An atlas project whose goal is to enerate brainwide maps of inter-regional neural connectivity that specify the inputs and outputs of every brain region, at a "mesoscopic" level of analysis. A 3D injection viewer is used to view the mouse brain. To determine the outputs of a brain region, anterograde tracers are used which are taken up by neurons locally ("the input"), then transported actively down the axons to the "output regions." The whole brain is then sliced thinly, and each slice is digitally imaged. These 2-D images are reconstructed in 3D. The majority of the resulting 3-D brain image is unlabeled. Only the injected region and its output regions have tracer in them, allowing for identification of this small fraction of the connectivity map. This procedure is repeated identically, to account for individual variability. To determine the inputs to the same brain region as above, a retrograde tracer is injected in the same stereotaxic location ("the input"), and the process is repeated. In order to accumulate data from different mice (each of whom has a slightly different brain shape and size), 3-D spatial normalization is performed using registration algorithms. These gigapixel images of whole-brain sections can be zoomed to show individual neurons and their processes, providing a "virtual microscope." Each sampled brain is represented in about 500 images, each image showing an optical section through a 20 micron-thick slice of brain tissue. A multi-resolution viewer permits users to journey through each brain, following the pathways taken through three-dimensional brain space by tracer-labeled neuronal pathways. A key point is that at the mid-range "mesoscopic" scale, the team expects to assemble a picture of connections that are stereotypical and probably genetically determined in a species-specific manner. By dividing the volume of a hemisphere of the mouse brain into 250 equidistant, predefined grid-points, and administering four different kinds of tracer injections at each grid point -- in different animals of the same sex and age a complete wiring diagram that will be stitched together in "shotgun" fashion from the full dataset.

Proper citation: Mouse Brain Architecture Project (RRID:SCR_004683) Copy   

•   Source: SciCrunch Registry

http://variant.bioinfo.cipf.es/

Analysis tool that can report the functional properties of any variant in all the human, mouse or rat genes (and soon new model organisms will be added) and the corresponding neighborhoods. Also other non-coding extra-genic regions, such as miRNAs are included in the analysis. It not only reports the obvious functional effects in the coding regions but also analyzes noncoding SNVs situated both within the gene and in the neighborhood that could affect different regulatory motifs, splicing signals, and other structural elements. These include: Jaspar regulatory motifs, miRNA targets, splice sites, exonic splicing silencers, calculations of selective pressures on the particular polymorphic positions, etc. Software analysis pipelines used in the analysis of NGS data are highly modular, heterogeneous, and rapidly evolving. VARIANT can easily be incorporated into a NGS resequencing pipeline either as a CLI or invoked a webservice. It inputs data directly from the most widely used programs for SNV detection., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: VARIANT (RRID:SCR_005194) Copy   

•   Source: SciCrunch Registry

http://seqant.genetics.emory.edu/

A free web service and open source software package that performs rapid, automated annotation of DNA sequence variants (single base mutations, insertions, deletions) discovered with any sequencing platform. Variant sites are characterized with respect to their functional type (Silent, Replacement, 5' UTR, 3' UTR, Intronic, Intergenic), whether they have been previously submitted to dbSNP, and their evolutionary conservation. Annotated variants can be viewed directly on the web browser, downloaded in a tab delimited text file, or directly uploaded in a Browser Extended Data (BED) format to the UCSC genome browser. SeqAnt further identifies all loci harboring two or more coding sequence variants that help investigators identify potential compound heterozygous loci within exome sequencing experiments. In total, SeqAnt resolves a significant bottleneck by allowing an investigator to rapidly prioritize the functional analysis of those variants of interest.

Proper citation: SeqAnt (RRID:SCR_005186) Copy   

•   Source: SciCrunch Registry

http://jilab.biostat.jhsph.edu/database/cgi-bin/hmChIP.pl

A database of genome-wide chromatin immunoprecipitation (ChIP) data in human and mouse. Currently, the database contains >2000 samples from >500 ChIP-seq and ChIP-chip experiments, representing a total of >170 proteins and >10,000,000 protein-DNA interactions (March 2014). A web server provides an interface for database query. Protein-DNA binding intensities can be retrieved from individual samples for user-provided genomic regions. The retrieved intensities can be used to cluster samples and genomic regions to facilitate exploration of combinatorial patterns, cell type dependencies, and cross-sample variability of protein-DNA interactions.

Proper citation: hmChIP (RRID:SCR_005407) Copy   

•   Source: SciCrunch Registry

http://amp.pharm.mssm.edu/lib/chea.jsp

Data analysis service for gene-list enrichment analysis against a manual database. It allows users to input lists of mammalian gene symbols for which the program computes over-representation of transcription factor targets from the ChIP-X database. The database integrates interaction data from ChIP-chip, ChIP-seq, ChIP-PET and DamID studies and contains 189,933 interactions, manually extracted from 87 publications, describing the binding of 92 transcription factors to 31,932 target genes.

Proper citation: ChEA (RRID:SCR_005403) Copy   

•   Source: SciCrunch Registry