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https://neuinfo.org/mynif/search.php?list=cover&q=*

Service that partners with the community to expose and simultaneously drill down into individual databases and data sets and return relevant content. This type of content, part of the so called hidden Web, is typically not indexed by existing web search engines. Every record links back to the originating site. In order for NIF to directly query these independently maintained databases and datasets, database providers must register their database or dataset with the NIF Data Federation and specify permissions. Databases are concept mapped for ease of sharing and to allow better understanding of the results. Learn more about registering your resource, http://neuinfo.org/nif_components/disco/interoperation.shtm Search results are displayed under the Data Federation tab and are categorized by data type and nervous system level. In this way, users can easily step through the content of multiple resources, all from the same interface. Each federated resource individually displays their query results with links back to the relevant datasets within the host resource. This allows users to take advantage of additional views on the data and tools that are available through the host database. The NIF site provides tutorials for each resource, indicated by the Professor Icon professor icon showing users how to navigate the results page once directed there through the NIF. Additionally, query results may be exported as an Excel document. Note: NIF is not responsible for the availability or content of these external sites, nor does NIF endorse, warrant or guarantee the products, services or information described or offered at these external sites. Integrated Databases: Theses virtual databases created by NIF and other partners combine related data indexed from multiple databases and combine them into one view for easier browsing. * Integrated Animal View * Integrated Brain Gene Expression View * Integrated Disease View * Integrated Nervous System Connectivity View * Integrated Podcasts View * Integrated Software View * Integrated Video View * Integrated Jobs * Integrated Blogs For a listing of the Federated Databases see, http://neuinfo.org/mynif/databaseList.php or refer to the Resources Listed by NIF Data Federation table below.

Proper citation: NIF Data Federation (RRID:SCR_004834) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/QuickGO/

A web-based browser for Gene Ontology terms and annotations, which is provided by the UniProtKB-GOA group at the EBI. It is able to offer a range of facilities including bulk downloads of GO annotation data which can be extensively filtered by a range of different parameters and GO slim set generation. The software for QuickGO is freely available under the Apache 2 license. QuickGO can supply GO term information and GO annotation data via REST web services.

Proper citation: QuickGO (RRID:SCR_004608) Copy   

•   Source: SciCrunch Registry

http://openflydata.org/

A cross-database search service for Drosophila gene expression data, including microarray data from FlyAtlas and in situ images from BDGP and Fly-TED. The applications provide different ways to search for and compare gene expression data for the fruit fly Drosophila melanogaster. You may Search Gene Expression Data by Gene, Gene Batch, and by Tissue Expression Profile. A number of Web services (SPARQL endpoints) are provided from this site which may be queried programmatically for data.

Proper citation: OpenFlyData.org (RRID:SCR_004807) Copy   

•   Source: SciCrunch Registry

http://glioblastoma.alleninstitute.org/

Platform for exploring the anatomic and genetic basis of glioblastoma at the cellular and molecular levels that includes two interactive databases linked together by de-identified tumor specimen numbers to facilitate comparisons across data modalities: * The open public image database, here, providing in situ hybridization data mapping gene expression across the anatomic structures inherent in glioblastoma, as well as associated histological data suitable for neuropathological examination * A companion database (Ivy GAP Clinical and Genomic Database) offering detailed clinical, genomic, and expression array data sets that are designed to elucidate the pathways involved in glioblastoma development and progression. This database requires registration for access. The hope is that researchers all over the world will mine these data and identify trends, correlations, and interesting leads for further studies with significant translational and clinical outcomes. The Ivy Glioblastoma Atlas Project is a collaborative partnership between the Ben and Catherine Ivy Foundation, the Allen Institute for Brain Science and the Ben and Catherine Ivy Center for Advanced Brain Tumor Treatment.

Proper citation: Ivy Glioblastoma Atlas Project (RRID:SCR_005044) Copy   

•   Source: SciCrunch Registry

http://www.genmapp.org/

GenMAPP is a free computer application designed to visualize gene expression and other genomic data on maps representing biological pathways and groupings of genes. Integrated with GenMAPP are programs to perform a global analysis of gene expression or genomic data in the context of hundreds of pathway MAPPs and thousands of Gene Ontology Terms (MAPPFinder), import lists of genes/proteins to build new MAPPs (MAPPBuilder), and export archives of MAPPs and expression/genomic data to the web. The main features underlying GenMAPP are: *Draw pathways with easy to use graphics tools *Color genes on MAPP files based on user-imported genomic data *Query data against MAPPs and the GeneOntology Enhanced features include the simultaneous view of multiple color sets, expanded species-specific gene databases and custom database options.

Proper citation: Gene Map Annotator and Pathway Profiler (RRID:SCR_005094) Copy   

•   Source: SciCrunch Registry

http://www.omicsoft.com/fusionmap/

An efficient fusion aligner which aligns reads spanning fusion junctions directly to the genome without prior knowledge of potential fusion regions. It detects and characterizes fusion junctions at base-pair resolution. FusionMap can be applied to detect fusion junctions in both single- and paired-end dataset from either gDNA-Seq or RNA-Seq studies. FusionMap runs under both Windows and Linux (requiring MONO) environments. Although it can run on 32 bit machine, it is recommended to run on 64-bit machine with 8GB RAM or more. If you have an ArrayStudio License, you can run the fusion detection easily through its GUI.

Proper citation: FusionMap (RRID:SCR_005242) Copy   

•   Source: SciCrunch Registry

http://sourceforge.net/projects/cova/

A variant annotation and comparison tool for next-generation sequencing. It annotates the effects of variants on genes and compares those among multiple samples, which helps to pinpoint causal variation(s) relating to phenotype.

Proper citation: COVA (RRID:SCR_005175) 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://stothard.afns.ualberta.ca/downloads/NGS-SNP/

A collection of command-line scripts for providing rich annotations for SNPs identified by the sequencing of transcripts or whole genomes from organisms with reference sequences in Ensembl. Included among the annotations, several of which are not available from any existing SNP annotation tools, are the results of detailed comparisons with orthologous sequences. These comparisons allow, for example, SNPs to be sorted or filtered based on how drastically the SNP changes the score of a protein alignment. Other fields indicate the names of overlapping protein domains or features, and the conservation of both the SNP site and flanking regions. NCBI, Ensembl, and Uniprot IDs are provided for genes, transcripts, and proteins when applicable, along with Gene Ontology terms, a gene description, phenotypes linked to the gene, and an indication of whether the SNP is novel or known. A ?Model_Annotations? field provides several annotations obtained by transferring in silico the SNP to an orthologous gene, typically in a well-characterized species.

Proper citation: NGS-SNP (RRID:SCR_005182) Copy   

•   Source: SciCrunch Registry

http://www.pazar.info/

Database that unites independently created and maintained data collections of transcription factor and regulatory sequence annotation. The flexible PAZAR schema permits the representation of diverse information derived from experiments ranging from biochemical protein-DNA binding to cellular reporter gene assays. Data collections can be made available to the public, or restricted to specific system users. The data ''boutiques'' within the shopping-mall-inspired system facilitate the analysis of genomics data and the creation of predictive models of gene regulation., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: PAZAR (RRID:SCR_005410) Copy   

•   Source: SciCrunch Registry

https://www.har.mrc.ac.uk/about/mammalian-genetics-unit

It is now widely known that animals share many genes with humans and can suffer from the same diseases, for example diabetes or deafness. Investigating these diseases in animals can provide vital leads to understanding both their causes and ways to treat them in humans. This approach to medical research lies at the heart of work at the MRC Mammalian Genetics Unit (MGU) at Harwell in Oxfordshire. In 1995 the MRC Radiobiology Unit was reconstituted to form two new units, the Radiation and Genome Stability Unit and the MGU. These opened in January 1996, together with the UK Mouse Genome Centre which is now part of MGU, making MRC Harwell a unique campus for multi-disciplinary genetics research. Since MGU's Director Steve Brown took the reins in 1996, the unit has dramatically expanded its scientific scope and increased its personnel from 40 to over 100. It now has 13 research programs encompassing molecular genetics, genomics, genetic manipulation and data analysis at all levels, from single genes to the whole genome. With a combination of cutting-edge facilities and expertise unrivaled in Europe, MGU Harwell has become firmly established as one of the world's leading academic centres for mouse genetics.

Proper citation: MRC Mammalian Genetics Unit (RRID:SCR_005378) Copy   

•   Source: SciCrunch Registry

http://statgenpro.psychiatry.hku.hk/limx/kggseq/

A biological Knowledge-based mining platform for Genomic and Genetic studies using Sequence data. The software platform, constituted of bioinformatics and statistical genetics functions, makes use of valuable biologic resources and knowledge for sequencing-based genetic mapping of variants / genes responsible for human diseases / traits. It facilitates geneticists to fish for the genetic determinants of human diseases / traits in the big sea of DNA sequences. KGGSeq has paid attention to downstream analysis of genetic mapping. The framework was implemented to filter and prioritize genetic variants from whole exome sequencing data.

Proper citation: KGGSeq (RRID:SCR_005311) Copy   

•   Source: SciCrunch Registry

http://bioapps.sabanciuniv.edu/mugex/v02/

Service that automatically extracts mutation-gene pairs from MEDLINE abstracts for a given disease.

Proper citation: MuGeX (RRID:SCR_005306) Copy   

•   Source: SciCrunch Registry

http://llama.mshri.on.ca/synergizer/translate/

The Synergizer database is a growing repository of gene and protein identifier synonym relationships. This tool facilitates the conversion of identifiers from one naming scheme (a.k.a namespace) to another. The Synergizer is a service for translating between sets of biological identifiers. It can, for example, translate Ensembl Gene IDs to Entrez Gene IDs, or IPI IDs to HGNC gene symbols, and much more. Unlike some other tools for this purpose, The Synergizer is simple and easy to learn. The Synergizer works via a web interface (for users who are not programmers) or through a web service (for programmatic access).

Proper citation: Synergizer (RRID:SCR_005308) Copy   

•   Source: SciCrunch Registry

http://services.nbic.nl/copub/portal/

Text mining tool that detects co-occuring biomedical concepts in abstracts from the MedLine literature database. It allows batch input of multiple human, mouse or rat genes and produces lists of keywords from several biomedical thesauri that are significantly correlated with the set of input genes. These lists link to Medline abstracts in which the co-occurring input genes and correlated keywords are highlighted. Furthermore, CoPub can graphically visualize differentially expressed genes and over-represented keywords in a network, providing detailed insight in the relationships between genes and keywords, and revealing the most influential genes as highly connected hubs.

Proper citation: CoPub (RRID:SCR_005327) Copy   

•   Source: SciCrunch Registry

http://www.ncbi.nlm.nih.gov/gtr/

Central location for voluntary submission of genetic test information by providers including the test''s purpose, methodology, validity, evidence of the test''s usefulness, and laboratory contacts and credentials. GTR aims to advance the public health and research into the genetic basis of health and disease. GTR is accepting registration of clinical tests for Mendelian disorders, complex tests and arrays, and pharmacogenetic tests. These tests may include multiple methods and may include multiple major method categories such as biochemical, cytogenetic, and molecular tests. GTR is not currently accepting registration of tests for somatic disorders, research tests or direct-to-consumer tests.

Proper citation: Genetic Testing Registry (RRID:SCR_005565) Copy   

•   Source: SciCrunch Registry

http://www.gene-regulation.com/pub/databases.html#transpath

Database on eukaryotic transcription factors, their experimentally-proven binding sites, consensus binding sequences (positional weight matrices) and regulated genes. Its broad compilation of binding sites allows the derivation of positional weight matrices. It can either be used as an encyclopedia, for both specific and general information on signal transduction, or can serve as a network analyzer. Cross-references to important sequence and signature databases such as EMBL/GenBank UniProt/Swiss-Prot InterPro or Ensembl EntrezGene RefSeq are provided. The database is equipped with the tools for data visualization and analysis. It has three modules: the first one is the data, which have been manually extracted, mostly from the primary literature; the second is PathwayBuilder, which provides several different types of network visualization and hence facilitates understanding; the third is ArrayAnalyzer, which is particularly suited to gene expression array interpretation, and is able to identify key molecules within signalling networks (potential drug targets). These key molecules could be responsible for the coordinated regulation of downstream events. Manual data extraction focuses on direct reactions between signalling molecules and the experimental evidence for them, including species of genes/proteins used in individual experiments, experimental systems, materials and methods. This combination of materials and methods is used in TRANSPATH to assign a quality value to each experimentally proven reaction, which reflects the probability that this reaction would happen under physiological conditions. Another important feature in TRANSPATH is the inclusion of transcription factor-gene relations, which are transferred from TRANSFAC, a database focused on transcription regulation and transcription factors. Since interactions between molecules are mainly direct, this allows a complete and stepwise pathway reconstruction from ligands to regulated genes.

Proper citation: TRANSPATH (RRID:SCR_005640) Copy   

•   Source: SciCrunch Registry

http://llama.mshri.on.ca/

The Roth Laboratory is designing and interpreting large-scale experiments to understand pathway structure and its relationship to phenotype and human disease. Software for research focused on a specific research goal is available. Current experimental interests: * Exploiting parallel sequencing technology to phenotype all pairwise gene deletion combinations in S. cerevisiae, with initial application to genes involved in transcription. * Generation of S. cerevisiae strains carrying dozens of chosen targeted deletions, with initial application to delete all ABC transporters imparting multidrug resistance. * Targeted insertion of gene sets encoding entire human pathways into S. cerevisiae, with initial application to genes involved in drug metabolism. Current computational interests: * Systematic analysis of genetic interaction to reveal redundant systems and order of action in genetic pathways * Integrating large-scale studies - including phenotype, genetic epistasis, protein-protein and transcription-regulatory interactions and sequence patterns - to quantitatively assign function to genes and guide experimentation and disease association studies. * Alternative splicing and its relationship to protein interaction networks.

Proper citation: Roth Laboratory (RRID:SCR_005711) Copy   

•   Source: SciCrunch Registry

http://sourceforge.net/projects/netclassr/

An R package for network-based feature (gene) selection for biomarkers discovery via integrating biological information. The package adapts the following 5 algorithms for classifying and predicting gene expression data using prior knowledge: # average gene expression of pathway (aep); # pathway activities classification (PAC); # Hub network classification (hubc); # filter via top ranked genes (FrSVM); # network smoothed t-statistic (stSVM).

Proper citation: netClass (RRID:SCR_005672) Copy   

•   Source: SciCrunch Registry

http://dbbb.georgetown.edu/research/bioinformatics/

THIS RESOURCE IS NO LONGER IN SERVCE, documented September 2, 2016.

Proper citation: GUMC Department of Biostatistics Bioinformatics and Biomathematics - Liu Lab (RRID:SCR_005708) Copy   

•   Source: SciCrunch Registry