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https://fungi.ensembl.org/Neurospora_crassa/Info/Index

It's strategy involves Whole Genome Shotgun (WGS) sequencing, in which sequence from the entire genome is generated and reassembled. This method is standard for microbial genome sequencing, and has been successfully applied to Drosophila. Neurospora is an ideal candidate for this approach because of the low repeat content of the genome. Neurospora crassa Database has expanded the scope of its database by including a mitochondrial annotation, incorporating information from the Neurospora compendium, and assigning NCU numbers to tRNA and rRNAs. They have improved the annotation process to predict untranslated regions and to reduce the number of spurious predictions. As a result, version 3 contains 9,826 genes, 794 fewer than version 2. During the initial phase of a WGS project they sequence both ends of the 4 kb inserts from a plasmid library prepared using randomly sheared and sized-selected DNA. The shotgun reads are assembled by recognizing overlapping regions of sequence and making use of the knowledge of the orientation and distance of the paired reads from each plasmid. Obtaining deep sequence coverage though high levels of sequence redundancy assures that the majority of the genome is represented in the initial assembly and that the consensus sequence is of high quality. Their approach toward the initial assembly was conservative, meaning they would rather fail to join sequence contigs that might overlap each other than risk making false joins between two closely related but non-overlapping genomic regions. Hence, the initial assembly contains many sequence contigs and over time these contigs will increase in size and decrease in number as they are joined together. After shotgun sequencing and assembly there was a second phase of sequencing in which additional sequence was obtained from specific regions that were missing from the original assembly or are recognized to be of low quality in the consensus. The Neurospora crassa sequencing project reflects a close collaboration between the Broad Institute and the Neurospora research community. Principal investigators include Bruce Birren and Chad Nusbaum from the Broad Institute, Matt Sachs at the Oregon Graduate Institute of Science and Technology, Chuck Staben at the University of Kentucky and Jak Kinsey at the Fungal Genetics Stock Center at the University of Kansas Medical Center. In addition, we have a larger Advisory Board made up of a number of Neurospora researchers. Sponsors: They have been funded by the National Science Foundation to sequence the N. crassa genome and make the information publicly available.

Proper citation: Neurospora crassa Database (RRID:SCR_001372) Copy   

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http://umcecaruca01.extern.umcn.nl:8080/ecaruca/ecaruca.jsp

A database of cytogenetic and clinical information on rare chromosomal disorders, including microdeletions and microduplications. The database is meant to be easily accessible for all participants, to improve patient care and collaboration between genetic centers, and collect the results of research and clinical features. The acronym ECARUCA stands for "European Cytogeneticists Association Register of Unbalanced Chromosome Aberrations".

Proper citation: ECARUCA Project (RRID:SCR_000797) Copy   

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http://igenbio.com/

A web-based genome analysis platform that integrates proprietary functional genomic data, metabolic reconstructions, expression profiling, and biochemical and microbiological data with publicly available information. Focused on microbial genomics, it provides better and faster identification of gene function across all organisms. Building upon a comprehensive genomic database integrated with a collection of microbial metabolic and non-metabolic pathways and using proprietary algorithms, it assigns functions to genes, integrates genes into pathways, and identifies previously unknown or mischaracterized genes, cryptic pathways and gene products. . * Automated and manual annotation of genes and genomes * Analysis of metabolic and non-metabolic pathways to understand organism physiology * Comparison of multiple genomes to identify shared and unique features and SNPs * Functional analysis of gene expression microarray data * Data-mining for target gene discovery * In silico metabolic engineering and strain improvement

Proper citation: ERGO (RRID:SCR_001243) Copy   

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http://hscl.cimr.cam.ac.uk/bloodexpress/

A database of gene expression in mouse haematopoiesis, integrating 271 individual microarray experiments derived from 15 distinct studies done on most characterized mouse blood cell types. Gene expression information has been discretized to absent/present/unknown calls. It supports gene-centric searches to find out where a gene of interest is expressed, and what other genes follow the same (or a similar) pattern of expression. It also supports cell-centric searches to find out what genes are expressed in specific cell types/studies and not others.

Proper citation: BloodExpress (RRID:SCR_001142) Copy   

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http://django.nubic.northwestern.edu/fundo/

Tool that takes a list of genes and finds relevant diseases based on statistical analysis of the Disease Ontology annotation database. It accepts Entrez gene ids or gene symbols, separated by tabs, newlines, or commas. This list of genes can be obtained by microarray, proteomics, sequencing or other high-throughput screening methods.

Proper citation: FunDO (RRID:SCR_001725) Copy   

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http://caps.ncbs.res.in/stifdb2/

Database of biotic and abiotic stress responsive genes in Arabidopsis thaliana and Oryza sativa L. with options to identify probable Transcription Factor Binding Sites in their promoters. In the response to biotic stress like Bacteria and abiotic stresses like ABA, drought, cold, salinity, dehydration, UV-B, high light, heat,heavy metals etc, ten specific families of transcription factors in Arabidopsis thaliana and six in Oryza sativa L. are known to be involved. HMM-based models are used to identify binding sites of transcription factors belonging to these families. They have also consulted literature reports to cross-validate the Transcription Factor Binding Sites predicted by the method.

Proper citation: STIFDB (RRID:SCR_002131) Copy   

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http://pathway.gramene.org/gramene/ricecyc.shtml

THIS RESOURCE IS NO LONGER IN SERVICE, documented on August 26, 2016. RiceCyc is a catalog of known and/or predicted biochemical pathways from rice (Oryza sativa). Pathways and genes presented in this catalog are primarily based on the annotations carried out by Gramene database project on the release 5 of the TIGR-assembly of Oryza sativa japonica cv. Nipponbare genome sequenced by IRGSP.

Proper citation: Rice Metabolic Pathway Database (RRID:SCR_002128) Copy   

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http://www.homozygositymapper.org/

A web-based approach of homozygosity mapping that can handle tens of thousands markers. User can upload their own SNP genotype files to the database. Intuitive graphic interface is provided to view the homozygous stretches, with the ability of zooming into single chromosomes or user-defined chromosome regions. The underlying genotypes in all samples are displayed. The software is also integrated with our candidate gene search engine, GeneDistiller, so that users can interactively determine the most promising gene. (entry from Genetic Analysis Software)

Proper citation: HOMOZYGOSITYMAPPER (RRID:SCR_001714) Copy   

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http://pstiing.icr.ac.uk/

A publicly accessible knowledgebase about protein-protein, protein-lipid, protein-small molecules, ligand-receptor interactions, receptor-cell type information, transcriptional regulatory and signal transduction modules relevant to inflammation, cell migration and tumourigenesis. It integrates in-house curated information from the literature, biochemical experiments, functional assays and in vivo studies, with publicly available information from multiple and diverse sources across human, rat, mouse, fly, worm and yeast. The knowledgebase allowing users to search and to dynamically generate visual representations of protein-protein interactions and transcriptional regulatory networks. Signalling and transcriptional modules can also be displayed singly or in combination. This allow users to identify important "cross-talks" between signalling modules via connections with key components or "hubs". The knowledgebase will facilitate a "systems-wide" understanding across many protein, signalling and transcriptional regulatory networks triggered by multiple environmental cues, and also serve as a platform for future efforts to computationally and mathematically model the system behavior of inflammatory processes and tumourigenesis.

Proper citation: pSTIING (RRID:SCR_002045) Copy   

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https://enigma.lbl.gov/regprecise/

Collection of manually curated inferences of regulons in prokaryotic genomes. Database for capturing, visualization and analysis of transcription factor regulons that were reconstructed by comparative genomic approach in wide variety of prokaryotic genomes., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: RegPrecise (RRID:SCR_002149) Copy   

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http://eyegene.ophthy.med.umich.edu/madeline/

Software tool designed for preparing, visualizing, and exploring human pedigree data used in genetic linkage studies. It converts pedigree and marker data into formats required by popular linkage analysis packages, provides powerful ways to query pedigree data sets, and produces Postscript pedigree drawings that are useful for rapid data review.

Proper citation: MADELINE (RRID:SCR_001979) Copy   

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http://giladlab.uchicago.edu/orthoExon/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. Database of orthologous exon regions in the genomes of human, chimpanzee, and rhesus macaque. It can be used in analysis of multi-species RNA-seq expression data, allowing for comparisons of exon-level expression across primates, as well as comparative examination of alternative splicing and transcript isoforms.

Proper citation: Primate Orthologous Exon Database (RRID:SCR_002065) Copy   

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http://atgc.lbl.gov/atgc/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. ATGC stands for Alignable Tight Genomic Cluster, which is cluster of closely related prokaryotic genomes. ATGC is the principal notion of this web resource. The purpose of this web resource is to prepare ATGC-derived data sets for a variety of research projects in functional and evolutionary genomics. Unique features of ATGC include: * Reliable identification of orthologs (high degree of similarity between the genomes in the set allow an extensive use of synteny in ortholog identification); * Fine granularity of protein classification (in comparisons of more distant genomes, proteins belonging to families of paralogs are often lumped into a singlegroup; under the ATGC approach, comparison of genomic sequences from highly similar genomes allows one to track each set of orthologs separately); * Relative rarity of changes of any kind (in sequence, genome organization and gene content) allows the use of parsimony-related methods of analysis.

Proper citation: Alignable Tight Genomic Cluster (RRID:SCR_001894) Copy   

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http://www.pubgene.org/

It helps users retrieve information on genes and proteins. The underlying structure of PubGene can be viewed as a gene-centric database. Gene and protein names are cross-referenced to each other and to terms that are relevant to understanding their biological function, importance in disease and relationship to chemical substances. The result is a literature network organizing information in a form that is easy to navigate.

Proper citation: PubGene (RRID:SCR_002119) Copy   

•   Source: SciCrunch Registry

http://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-8-335

Cell signaling pathways can be explored using PathFinder, the interactive, online graphical representation of cell signaling pathways. The user can use PathFinder to explore the relationships between different cell signaling pathway components while being presented with our high quality small molecules, antibodies, enzymes, siRNA for gene knockdown and qPCR components to aid them in their research.

Proper citation: Cell Signaling Pathways (RRID:SCR_002070) Copy   

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http://cancer.sanger.ac.uk/cancergenome/projects/cosmic/

Database to store and display somatic mutation information and related details and contains information relating to human cancers. The mutation data and associated information is extracted from the primary literature. In order to provide a consistent view of the data a histology and tissue ontology has been created and all mutations are mapped to a single version of each gene. The data can be queried by tissue, histology or gene and displayed as a graph, as a table or exported in various formats.
Some key features of COSMIC are:
* Contains information on publications, samples and mutations. Includes samples which have been found to be negative for mutations during screening therefore enabling frequency data to be calculated for mutations in different genes in different cancer types.
* Samples entered include benign neoplasms and other benign proliferations, in situ and invasive tumours, recurrences, metastases and cancer cell lines.

Proper citation: COSMIC - Catalogue Of Somatic Mutations In Cancer (RRID:SCR_002260) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/swissprot/hpi/hpi.html

THIS RESOURCE IS NO LONGER IN SERVICE, documented on August 03, 2011. IT HAS BEEN REPLACED BY A NEW UniProtKB/Swiss-Prot ANNOTATION PROGRAM CALLED UniProt Chordata protein annotation program. The Human Proteome Initiative (HPI) aims to annotate all known human protein sequences, as well as their orthologous sequences in other mammals, according to the quality standards of UniProtKB/Swiss-Prot. In addition to accurate sequences, we strive to provide, for each protein, a wealth of information that includes the description of its function, domain structure, subcellular location, similarities to other proteins, etc. Although as complete as currently possible, the human protein set they provide is still imperfect, it will have to be reviewed and updated with future research results. They will also create entries for newly discovered human proteins, increase the number of splice variants, explore the full range of post-translational modifications (PTMs) and continue to build a comprehensive view of protein variation in the human population. The availability of the human genome sequence has enabled the exploration and exploitation of the human genome and proteome to begin. Research has now focused on the annotation of the genome and in particular of the proteome. With expert annotation extracted from the literature by biologists as the foundation, it has been possible to expand into the areas of data mining and automatic annotation. With further development and integration of pattern recognition methods and the application of alignments clustering, proteome analysis can now be provided in a meaningful way. These various approaches have been integrated to attach, extract and combine as much relevant information as possible to the proteome. This resource should be valuable to users from both research and industry. We maintain a file containing all human UniProtKB/Swiss-Prot entries. This file is updated at every biweekly release of UniProt and can be downloaded by FTP download, HTTP download or by using a mirroring program which automatically retrieves the file at regular intervals.

Proper citation: Human Proteomics Initiative (RRID:SCR_002373) Copy   

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http://www.HGPD.jp

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 4,2023.The Human Gene and Protein Database presents SDS-PAGE patterns and other informations of human genes and proteins. The HGPD was constructed from full-length cDNAs. For conversion to Gateway entry clones, we first determined an open reading frame (ORF) region in each cDNA meeting the criteria. Those ORF regions were PCR-amplified utilizing selected resource cDNAs as templates. All the details of the construction and utilization of entry clones will be published elsewhere. Amino acid and nucleotide sequences of an ORF for each cDNA and sequence differences of Gateway entry clones from source cDNAs are presented in the GW: Gateway Summary window. Utilizing those clones with a very efficient cell-free protein synthesis system featuring wheat germ, we have produced a large number of human proteins in vitro. Expressed proteins were detected in almost all cases. Proteins in both total and supernatant fractions are shown in the PE: Protein Expression window. In addition, we have also successfully expressed proteins in HeLa cells and determined subcellular localizations of human proteins. These biological data are presented on the frame of cDNA clusters in the Human Gene and Protein Database. To build the basic frame of HGPD, sequences of FLJ full-length cDNAs and others deposited in public databases (Human ESTs, RefSeq, Ensembl, MGC, etc.) are assembled onto the genome sequences (NCBI Build 35 (UCSC hg17)). The majority of analysis data for cDNA sequences in HGPD are shared with the FLJ Human cDNA Database (http://flj.hinv.jp/) constructed as a human cDNA sequence analysis database focusing on mRNA varieties caused by variations in transcription start site (TSS) and splicing.

Proper citation: Human Gene and Protein Database (HGPD) (RRID:SCR_002889) Copy   

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http://www.ecoli-york.org/

A database that curates new experimental and bioinformatic information about the genes and gene products of the model bacterium Escherichia coli K-12 strain MG1655. It has been created to integrate information from post-genomic experiments into a single resource with the aim of providing functional predictions for the 1500 or so gene products for which we have no knowledge of their physiological function. While EchoBASE provides a basic annotation of the genome, taken from other databases, its novelty is in the curation of post-genomic experiments and their linkage to genes of unknown function. Experiments published on E. coli are curated to one of two levels. Papers dealing with the determination of function of a single gene are briefly described, while larger dataset are actually included in the database and can be searched and manipulated. This includes data for proteomics studies, protein-protein interaction studies, microarray data, functional genomic approaches (looking at multiple deletion strains for novel phenotypes) and a wide range of predictions that come out of in silico bioinformatic approaches. The aim of the database is to provide hypothesis for the functions of uncharacterized gene products that may be used by the E. coli research community to further our knowledge of this model bacterium.

Proper citation: EchoBASE (RRID:SCR_002430) Copy   

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http://weatherby.genetics.utah.edu/cgi-bin/Phevor/PhevorWeb.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on October 28,2025. Tool that integrates phenotype, gene function, and disease information with personal genomic data for improved power to identify disease-causing alleles. It works by combining knowledge resident in multiple biomedical ontologies with the outputs of variant prioritization tools. It does so using an algorithm that propagates information across and between ontologies. This process enables Phevor to accurately reprioritize potentially damaging alleles identified by variant prioritization tools in light of gene function, disease, and phenotype knowledge. Phevor is especially useful for single exome and family trio-based diagnostic analyses, the most commonly occurring clinical scenarios, and ones for which existing personal-genomes diagnostic tools are most inaccurate and underpowered. Phevor not only improves diagnostic accuracy for individuals presenting with established disease phenotypes, but also for those with previously undescribed and atypical disease presentations. Importantly, Phevor is not limited to known diseases, or known disease-causing alleles.

Proper citation: Phevor (RRID:SCR_002273) Copy   

•   Source: SciCrunch Registry