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http://nectarmutation.org/main

A database and web application to annotate disease-related and functionally important amino acids in human proteins., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: NECTAR (RRID:SCR_007757) Copy   

•   Source: SciCrunch Registry

http://www.geneatlas.org/gene/main.jsp

This website allows visitors to search for genes of interest based on their spatial expression patterns in the Postnatal Day 7 mouse brain. Geneatlas provides two searching tools: A graphical interface for customized spatial queries; A textual interface for querying annotated structures. Geneatlas is the product of a collaboration between researchers at Baylor College of Medicine, Rice University, and University of Houston.

Proper citation: Gene Atlas (RRID:SCR_008089) Copy   

•   Source: SciCrunch Registry

http://bioportal.kobic.re.kr/SNPatETHNIC/

SNP at Ethnos is a catalog of human SNPs and genes that contain human ethnic variation. The database contains the following results for detecting natural selection and population difference: * Neareast Shrunken Centroid Method Score for detecting ethnic difference * Fst * Hudson, Kreitman and Aguade (HKA) test (1987) * Tajima''''s D test (1989) * Fu and Li D test (1993) It also contains copious links to dbSNP, Entrez Gene, GeneCards, OMIM, HGMD, International HapMap SNP at domain, and Haplotter (EHH). You can search by entering a gene symbol or an rs number in the text box at Data Search page. Search results provide above selection analysis data, rs lists corresponding a searched gene, and a genome viewer which contains functional annotation. The data underlying these analyses are from the Phase I HapMap Project.

Proper citation: SNP at Ethnos (RRID:SCR_007940) Copy   

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http://natural.salk.edu/CREB/

CREB target gene database that uses a multi-layered approach to predict, validate and characterize CREB target genes. For each gene, the database tries to provide the following information: 1. CREB binding sites on the promoters 2. Promoter occupancy by CREB 3. Gene activation by cAMP in tissues CREB seems to occupy a large number of promoters in the genome (up to ~5000 in human), and the profiles for CREB promoter occupancy are very similar in different human tissues. However, only a small proportion of CREB occupied genes are induced by cAMP in any cell type, possibly reflecting the requirement of additional regulatory partners that assist in recruitment of the transcriptional apparatus. To use the database, choose the species, select the table you want to search, leave field (''All'') and type in the gene you want to search. A table listing the search results will be returned, followed by the description of the table. If no search result is returned, try the official gene symbol or gene ID (locuslink number) from NCBI Entrez Gene to search. Sponsors: This work was supported by National Institutes of Health Grants GM RO1-037828 (to M.M.) and DK068655 (to R.A.Y.).

Proper citation: CRE Binding-protein Target Gene Database (RRID:SCR_008027) Copy   

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http://vox.pharmacology.ucla.edu/home.html

Two-dimensional images of gene expression for 20,000 genes in a coronal slice of the mouse brain at the level of the striatum by using microarrays in combination with voxelation at a resolution of 1 cubic mm gene expression patterns in the brain obtained through voxelation. Voxelation employs high-throughput analysis of spatially registered voxels (cubes) to produce multiple volumetric maps of gene expression analogous to the images reconstructed in biomedical imaging systems.

Proper citation: Voxelation Map of Gene Expression in a Coronal Section of the Mouse Brain (RRID:SCR_008065) Copy   

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http://statgen.ncsu.edu/asg/

Alternative splicing essentially increases the diversity of the transcriptome and has important implications for physiology, development and the genesis of diseases. This resource uses a different approach to investigate alternative splicing (instead of the conventional case-by case fashion) and integrates all transcripts derived from a gene into a single splicing graph. ASG is a database of splicing graphs for human genes, using transcript information from various major sources (Ensembl, RefSeq, STACK, TIGR and UniGene). Each transcript corresponds to a path in the graph, and alternative splicing is displayed by bifurcations. This representation preserves the relationships between different splicing variants and allows us to investigate systematically all possible putative transcripts. Web interface allows users to display the splicing graphs, to interactively assemble transcripts and to access their sequences as well as neighboring genomic regions. ASG also provide for each gene, an exhaustive pre-computed catalog of putative transcriptsin total more than 1.2 million sequences. It has found that ~65 of the investigated genes show evidence for alternative splicing, and in 5 of the cases, a single gene might produce over 100 transcripts.

Proper citation: Alternate splicing gallery (RRID:SCR_008129) Copy   

•   Source: SciCrunch Registry

http://www.webcitation.org/getfile?fileid=c2cde11f2c71cfb474c58964754c6aecc7586986

NASCArrays is the Nottingham Arabidopsis Stock Centre''s microarray database. Currently most of the data is for Arabidopsis thaliana experiments run by the NASC Affymetrix Facility. There are also experiments from other species, and experiments run by other centres too. NASCArrays is an Affymetrix microarray database. It contains free Affymetrix microarray data, and also features a series of tools allowing you to query that data in powerful ways. Most of the data currently comes from NASC''s Affymetrix Service. It also includes data from other sources, notably the AtGenExpress project. They currently distribute over 30,000 tubes of seed a year. There are currently the following data mining tools available. All of these tools allow you to type in a gene(s) of interest, and identify experiments or slides that you might be interested in: -Spot History: This tool allows you to see the pattern of gene expression over all slides in the database. Easily identify slides (and therefore experimental treatments) where genes are highly, lowly, or unusually expressed -Two gene scatter plot: This tool allows you to see the pattern of gene expression over all slides for two genes as a scatter plot. If you are interested in two genes, you can find out if they act in tandem, and highlight slides (and therefore experimental conditions) where these two genes behave in an unusual manner. -Gene Swinger: If you have a gene of interest, this tool will show you which experiment the gene expression varied most -Bulk Gene Download: This tool allows you to download the expression of a list of genes over all experiments. You can get all genes over all experiments (the entire database!) from the Super Bulk Gene Download Sponsors: This is a BBSRC funded consortium to provide services to the Arabidopsis community.

Proper citation: NASCArrays: The Nottingham Arabidopsis Stock Centre Arrays (RRID:SCR_008126) Copy   

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http://www.peroxisomedb.org/

The aim of the PEROXISOME database (PeroxisomeDB) is to gather, organize and integrate curated information on peroxisomal genes, their encoded proteins, their molecular function and metabolic pathway they belong to, and their related disorders. PeroxisomeDB contains the complete peroxisomal proteome of Homo sapiens (encoded by 85 genes) and Saccharomyces cerevisiae (encoded by 61 genes). Now, we have included 34 new organism genomes with the acquisition of 2426 new peroxisomal homolog proteins. PeroxisomeDB 2.0 integrates the peroxisomal metabolome of whole microbody family by the new incorporation of the glycosome proteomes of trypanosomatids and the glyoxysome proteome of Arabidopsis thaliana. The site also provides a Peroxisome Metabolome of peroxisomal genes and proteins, their molecular interactions and metabolic pathways, tools for comparative genomics, predictive tools. Sponsors: Preoxisome Database is funded by Institut de Gntique et deBiologie Molculaire et Cellulaire.

Proper citation: Peroxisome Database (RRID:SCR_008352) Copy   

•   Source: SciCrunch Registry

http://cssb.biology.gatech.edu/skolnick/files/gpcr/gpcr.html

THIS RESOURCE IS NO LONGER IN SERVICE, documented on August 19,2019.Database of tertiary structural modeling results of threading assembly refinement (TASSER) method for all 907 G protein-coupled receptors (GPCRs) in human genome. All sequences were collected from GPCR database http://www.gpcr.org/7tm/ and http://www.expasy.org/cgi-bin/lists?7tmrlist.txt. Unlike traditional homology modeling approaches, TASSER modeling does not require solved homologous template structures; moreover, it often refines the structures closer to native. G protein-coupled receptors (GPCRs), encoded by about 5% of human genes, comprise the largest family of integral membrane proteins and act as cell surface receptors responsible for the transduction of endogenous signal into a cellular response. Although tertiary structural information is crucial for function annotation and drug design, there are few experimentally determined GPCR structures. To address this issue, we employ the recently developed threading assembly refinement (TASSER) method to generate structure predictions for all 907 putative GPCRs in the human genome. Unlike traditional homology modeling approaches, TASSER modeling does not require solved homologous template structures; moreover, it often refines the structures closer to native. These features are essential for the comprehensive modeling of all human GPCRs when close homologous templates are absent. Based on a benchmarked confidence score, approximately 820 predicted models should have the correct folds. The majority of GPCR models share the characteristic seven-transmembrane helix topology, but 45 ORFs are predicted to have different structures. This is due to GPCR fragments that are predominantly from extracellular or intracellular domains as well as database annotation errors. Our preliminary validation includes the automated modeling of bovine rhodopsin, the only solved GPCR in the Protein Data Bank. With homologous templates excluded, the final model built by TASSER has a global C(alpha) root-mean-squared deviation from native of 4.6 angstroms, with a root-mean-squared deviation in the transmembrane helix region of 2.1 angstroms. Models of several representative GPCRs are compared with mutagenesis and affinity labeling data, and consistent agreement is demonstrated. Structure clustering of the predicted models shows that GPCRs with similar structures tend to belong to a similar functional class even when their sequences are diverse. These results demonstrate the usefulness and robustness of the in silico models for GPCR functional analysis. Sponsors: GPCR is funded by the University at Buffalo, Buffalo, New York.

Proper citation: Structure modeling of 907 G protein coupled receptors in the human genome (RRID:SCR_008351) Copy   

•   Source: SciCrunch Registry

https://wiki.cgb.indiana.edu/display/DGC/Home

The Daphnia Genomics Consortium (DGC) is an international network of investigators committed to mounting the freshwater crustacean Daphnia as a model system for ecology, evolution and the environmental sciences. Along with research activities, the DGC is: (1) coordinating efforts towards developing the Daphnia genomic toolbox, which will then be available for use by the general community; (2) facilitating collaborative cross-disciplinary investigations; (3) developing bioinformatic strategies for organizing the rapidly growing genome database; and (4) exploring emerging technologies to improve high throughput analyses of molecular and ecological samples. If we are to succeed in creating a new model system for modern life-sciences research, it will need to be a community-wide effort. Research activities of the DGC are primarily focused on creating genomic tools and information. When completed, the current projects will offer a first view of the Daphnia genome''s topography, including regions of high and low recombination, the distribution of transposable, repetitive and regulatory elements, the size and structure of genes and of their neighborhoods. This information is crucial in formulating testable hypotheses relating genetics and demographics to the evolutionary potential or constraints of natural populations. Projects aiming to compile identifiable genes with their function are also underway, together with robust methods to verify these findings. Finally, these tools are being tested, by exploring their uses in key ecological and toxicological investigations. Each project benefits from the leadership and expertise of many individuals. For further details, begin by contacting the project directors. The DGC consists of biologists from a broad spectrum of subdisciplines, including limnology, ecotoxicology, quantitative and population genetics, systematics, molecular biology and evolution, developmental biology, genomics and bioinformatics. In many regards, the rapid early success of the consortium results from its grass-roots origin promoting an international composition, under a cooperative model, with significant scientific breadth. We hold to this approach in building this network and encourage more people to participate. All the while, the DGC is structured to effectively reach specific goals. The consortium includes an advisory board (composed of experts of the various subdisciplines), whose responsibility is to act as the research community''s agent in guiding the development of Daphnia genomic resources. The advisors communicate directly to DGC members, who are either contributing genomic tools or actively seeking funds for this function. The consortium''s main body (given the widespread interest in applying genomic tools in environmental studies) are the affiliates, who make use of these tools for their research and who are soliciting support.

Proper citation: Daphnia genomics consortium (RRID:SCR_008148) Copy   

•   Source: SciCrunch Registry

http://www.nih.gov/science/models/mouse/deltagenlexicon/list.html

This resource contains an alphabetical listing of the various mice knockout genes according to name. Each of the listing contains the phenotypical information observed when a particular gene is knocked out. Other information, such as papers associated to a particular gene and methodology used to conduct the knockout may also be present.
This resource contains 314 different knockout genes and provides various navigation tools for easy access.
Sponsors: This resource portal is supported by FBS.

Proper citation: Alphabetical List of Knockout Genes (RRID:SCR_008133) Copy   

•   Source: SciCrunch Registry

http://cmbi.bjmu.edu.cn/cmbidata/cgf/CGF_Database/cytokine.medic.kumamoto-u.ac.jp/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on August 26, 2016. A collection of cDNA, gene and protein records of cytokines deposited in public databases provides various information about the cytokine members of vertebrates in other databases including NCBI GenBank, Swiss-Prot, UniGene, TIGR (The Institute for Genomic Research) Gene Indices, Ensembl, Entrez Gene, Mouse Genome Informatics (MGI) and Rat Genome Database (RGD). It also provides orthologous relationship of cytokine members and includes novel members identified in the databases.

Proper citation: Cytokine Family Database (RRID:SCR_008134) Copy   

•   Source: SciCrunch Registry

http://pbil.univ-lyon1.fr/acuts/ACUTS.html

THIS RESOURCE IS NO LONGER IN SERVICE, Documented on August 12, 2014. Database that identifies new regulatory elements in untranslated regions of protein-coding genes (5 prime flanks, 5 prime UTRs, introns, 3 prime UTRs and 3 prime flanks). The analyses is focused on genes from metazoan species (essentially vertebrates, insects and nematodes). Information on highly conserved regions (sequences, alignments, annotations, bibliographic references) are compiled. Currently 176 out of 326 detected highly conserved regions (HCRs) have been analyzed and incorporated in the database. You can also access the list of annotated conserved elements and the list of conserved elements that remain to be processed. Their approach is based on comparative sequence analysis, for the identification of phylogenetic footprints.

Proper citation: Ancient conserved untranslated sequences (RRID:SCR_008130) Copy   

•   Source: SciCrunch Registry

http://gene64.dna.affrc.go.jp/RPD/main_en.html

THIS RESOURCE IS NO LONGER IN SERVICE, documented July 22, 2016.

A database on the proteome of rice that contains reference maps based on two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) of proteins from rice tissues and subcellular compartments.

Proper citation: Rice Proteome Database (RRID:SCR_000743) Copy   

•   Source: SciCrunch Registry

http://www-personal.umich.edu/~jianghui/rseq/

A software toolkit for RNA sequence data analysis. It contains programs that cover several aspects of RNA-Seq data analysis such as read quality assessment, reference sequence generation, sequence mapping, and gene and isoform expressions estimations.

Proper citation: rSeq (RRID:SCR_000562) Copy   

•   Source: SciCrunch Registry

http://psychiatry.igm.jhmi.edu/SynaptomeDB/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on July 31,2025. Ontology-based knowledgebase for synaptic genes. These genes encode components of the synapse including neurotransmitters and their receptors, adhesion / cytoskeletal proteins, scaffold proteins, transporters, and others. It integrates various and complex data sources for synaptic genes and proteins., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: SynaptomeDB (RRID:SCR_000157) Copy   

•   Source: SciCrunch Registry

http://gemma-doc.chibi.ubc.ca/neurocarta/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. Neurocarta is a knowledgebase that consolidates information on genes and phenotypes across multiple resources and allows tracking and exploring of the associations. The system enables automatic and manual curation of evidence supporting each association, as well as user-enabled entry of their own annotations. Phenotypes are recorded using controlled vocabularies such as the Disease Ontology to facilitate computational inference and linking to external data sources. The gene-to-phenotype associations are filtered by stringent criteria to focus on the annotations most likely to be relevant. Neurocarta is constantly growing and currently holds more than 30,000 lines of evidence linking over 6,800 genes to 1,800 different phenotypes. Neurocarta is a one-stop shop for researchers looking for candidate genes for any disorder of interest. In Neurocarta, they can review the evidence linking genes to phenotypes and filter out the evidence they're not interested in. In addition, researchers can enter their own annotations from their experiments and analyze them in the context of existing public annotations. Neurocarta's in-depth annotation of neurodevelopmental disorders makes it a unique resource for neuroscientists working on brain development.

Proper citation: Neurocarta (RRID:SCR_000617) Copy   

•   Source: SciCrunch Registry

http://text0.mib.man.ac.uk/software/mldic/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 9, 2022. System that retrieves relevant UniProt IDs from BioThesaurus entries using a soft string matching algorithm.

Proper citation: Smart Dictionary Lookup (RRID:SCR_000568) Copy   

•   Source: SciCrunch Registry

http://genome.igib.res.in/tbvar/

Database of the variome of Mycobacterium tuberculosis (Mtb) comprising of over 29,000 single nucleotide variations created from re-analyzed data sets corresponding to over 400 isolates of Mtb. Using a systematic computational pipeline, potential functional variants and drug-resistance associated variants have been annotated. The database has an option to annotate variants from clinical re-sequencing of Mtb.

Proper citation: tbvar (RRID:SCR_001178) Copy   

•   Source: SciCrunch Registry

http://gbrowse.csbio.unc.edu/cgi-bin/gb2/gbrowse/slep/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. Database of genetic and gene expression data from the published literature on psychiatric disorders. Users can search the accumulated data to find the evidence in support of the involvement of a particular genomic region with a set of important psychiatric disorders, ADHD, autism, bipolar disorder, eating disorder, major depressive disorder, schizophrenia, and smoking behavior. It contains findings from manual reviews of 144 papers in psychiatric genetics, 136 primary reports and 8 meta-analyses. Disorders covered include schizophrenia (44 papers), autism (24 papers), bipolar disorder (24 papers), smoking behavior (24 papers), major depressive disorder and neuroticism (14 papers), ADHD (8 papers), eating disorders (3 papers), and a combined schizophrenia-bipolar phenotype (3 papers). The unbiased searches integrated into SLEP include genomewide linkage (117 papers), genomewide association (15 papers), copy number variation (9 papers), and gene expression studies of post-mortem brain tissue (3 meta-analyses courtesy of the Stanley Foundation). In total, SLEP captures 3,741 findings from these 144 papers. SLEP also contains over 70,000 SignPosts. These annotations derive from many different sources and are designed to try to capture current state of knowledge about disease associations in the human genome. SignPosts can be searched simultaneously with the psychiatric genetics literature in order to integrate these two bodies of knowledge. The SignPosts include: accumulated GWAS findings from the human genetics literature, the OMIM database, candidate gene association study literature, CNV location and frequency data, SNPs that influence gene expression in brain, genes expressed in brain, genes with evidence of imprinting and random monoalleleic expression, genes mutated in breast or colorectal cancer, and pathway data from BioCyc.

Proper citation: Sullivan Lab Evidence Project (RRID:SCR_000753) Copy   

•   Source: SciCrunch Registry