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http://string.embl.de/

Database of known and predicted protein interactions. The interactions include direct (physical) and indirect (functional) associations and are derived from four sources: Genomic Context, High-throughput experiments, (Conserved) Coexpression, and previous knowledge. STRING quantitatively integrates interaction data from these sources for a large number of organisms, and transfers information between these organisms where applicable. The database currently covers 5''214''234 proteins from 1133 organisms. (2013)

Proper citation: STRING (RRID:SCR_005223) Copy   

•   Source: SciCrunch Registry

http://bioinfo.iitk.ac.in/MIPModDB/

This is a database of comparative protein structure models of MIP (Major Intrinsic Protein) family of proteins. The nearly completed sets of MIPs have been identified from the completed genome sequence of organisms available at NCBI. The structural models of MIP proteins were created by defined protocol. The database aims to provide key information of MIPs in particular based on sequence as well as structures. This will further help to decipher the function of uncharacterized MIPs. For each MIP entry, this database contains information about the source, gene structure, sequence features, substitutions in the conserved NPA motifs, structural model, the residues forming the selectivity filter and channel radius profile. For selected set of MIPs, it is possible to derive structure-based sequence alignment and evolutionary relationship. Sequences and structures of selected MIPs can be downloaded from MIPModDB database.

Proper citation: MIPModDB (RRID:SCR_006058) Copy   

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http://operons.ibt.unam.mx/OperonPredictor/

The Prokaryotic Operon DataBase (ProOpDB) constitutes one of the most precise and complete repository of operon predictions in our days. Using our novel and highly accurate operon algorithm, we have predicted the operon structures of more than 1,200 prokaryotic genomes. ProOpDB offers diverse alternatives by which a set of operon predictions can be retrieved including: i) organism name, ii) metabolic pathways, as defined by the KEGG database, iii) gene orthology, as defined by the COG database, iv) conserved protein motifs, as defined by the Pfam database, v) reference gene, vi) reference operon, among others. In order to limit the operon output to non-redundant organisms, ProOpDB offers an efficient protocol to select the more representative organisms based on a precompiled phylogenetic distances matrix. In addition, the ProOpDB operon predictions are used directly as the input data of our Gene Context Tool (GeConT) to visualize their genomic context and retrieve the sequence of their corresponding 5�� regulatory regions, as well as the nucleotide or amino acid sequences of their genes. The prediction algorithm The algorithm is a multilayer perceptron neural network (MLP) classifier, that used as input the intergenic distances of contiguous genes and the functional relationship scores of the STRING database between the different groups of orthologous proteins, as defined in the COG database. Nevertheless, the operon prediction of our method is not restricted to only those genes with a COG assignation, since we successfully defined new groups of orthologous genes and obtained, by extrapolation, a set of equivalent STRING-like scores based on conserved gene pairs on different genomes. Since the STRING functional relationships scores are determined in an un-bias manner and efficiently integrates a large amount of information coming from different sources and kind of evidences, the prediction made by our MLP are considerably less influenced by the bias imposed in the training procedure using one specific organism.

Proper citation: ProOpDB (RRID:SCR_006111) Copy   

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http://prorepeat.bioinformatics.nl/

ProRepeat is an integrated curated repository and analysis platform for in-depth research on the biological characteristics of amino acid tandem repeats. ProRepeat collects repeats from all proteins included in the UniProt knowledgebase, together with 85 completely sequenced eukaryotic proteomes contained within the RefSeq collection. It contains non-redundant perfect tandem repeats, approximate tandem repeats and simple, low-complexity sequences, covering the majority of the amino acid tandem repeat patterns found in proteins. The ProRepeat web interface allows querying the repeat database using repeat characteristics like repeat unit and length, number of repetitions of the repeat unit and position of the repeat in the protein. Users can also search for repeats by the characteristics of repeat containing proteins, such as entry ID, protein description, sequence length, gene name and taxon. ProRepeat offers powerful analysis tools for finding biological interesting properties of repeats, such as the strong position bias of leucine repeats in the N-terminus of eukaryotic protein sequences, the differences of repeat abundance among proteomes, the functional classification of repeat containing proteins and GC content constrains of repeats' corresponding codons.

Proper citation: ProRepeat (RRID:SCR_006113) Copy   

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https://www.arb-silva.de

High quality ribosomal RNA databases providing comprehensive, quality checked and regularly updated datasets of aligned small (16S/18S, SSU) and large subunit (23S/28S, LSU) ribosomal RNA (rRNA) sequences for all three domains of life (Bacteria, Archaea and Eukarya). Supplementary services include a rRNA gene aligner, online tools for probe and primer evaluation and optimized browsing, searching and downloading on the website. The extensively curated SILVA taxonomy and the new non-redundant SILVA datasets provide an ideal reference for high-throughput classification of data from next-generation sequencing approaches. Alignment tool, SINA, is available for download as well as available for use online.

Proper citation: SILVA (RRID:SCR_006423) Copy   

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http://viralzone.expasy.org/

ViralZone is a SIB Swiss Institute of Bioinformatics web-resource for all viral genus and families, providing general molecular and epidemiological information, along with virion and genome figures. Each virus or family page gives an easy access to UniProtKB/Swiss-Prot viral protein entries. ViralZone project is handled by the virus program of SwissProt group. Proteins popups were developed in collaboration with Prof. Christian von Mering and Andrea Franceschini, Bioinformatics Group , Institute of Molecular Life Sciences, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland, funded in part by the SIB Swiss Institute of bioinformatics. All pictures in ViralZone are copyright of the SIB Swiss Institute of Bioinformatics.

Proper citation: ViralZone (RRID:SCR_006563) Copy   

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http://www.broadinstitute.org/annotation/tetraodon/

This database have been funded by the National Human Genome Research Institute (NHGRI) to produce shotgun sequence of the Tetraodon nigriviridis genome. The strategy involves Whole Genome Shotgun (WGS) sequencing, in which sequence from the entire genome is generated. Whole genome shotgun libraries were prepared from Tetraodon genomic DNA obtained from the laboratory of Jean Weissenbach at Genoscope. Additional sequence data of approximately 2.5X coverage of Tetraodon has also been generated by Genoscope in plasmid and BAC end reads. Broad and Genoscope intend to pool their data and generate whole genome assemblies. Tetraodon nigroviridis is a freshwater pufferfish of the order Tetraodontiformes and lives in the rivers and estuaries of Indonesia, Malaysia and India. This species is 20-30 million years distant from Fugu rubripes, a marine pufferfish from the same family. The gene repertoire of T. nigroviridis is very similar to that of other vertebrates. However, its relatively small genome of 385 Mb is eight times more compact than that of human, mostly because intergenic and intronic sequences are reduced in size compared to other vertebrate genomes. These genome characteristics along with the large evolutionary distance between bony fish and mammals make Tetraodon a compact vertebrate reference genome - a powerful tool for comparative genetics and for quick and reliable identification of human genes.

Proper citation: Tetraodon nigroviridis Database (RRID:SCR_007123) Copy   

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http://mouse.perlegen.com/mouse/index.html

THIS RESOURCE IS NO LONGER IN SERVICE, Documented on August 12, 2014. Data, grouped by chromosome, available as flat files for download, of identified DNA polymorphisms (SNPs) in 15 commonly used strains of inbred laboratory mice. Perlegen's SNP, genotype (empirical and imputed), haplotype, trace, and PCR primer data has been compiled with NCBI Mouse Build information to produce data files for public use. Using high-density oligonuclueotide array technology, the study identified over 8 million SNPs and other genetic differences between these strains and the previously sequenced C57BL/6J reference strains (Phase 1). By leveraging data provided by Mark Daly's research team at the Broad Institute, genotypes were also predicted for 40 other common strains (Phase 2). Under an extension to the contract, Eleazar Eskin's group at UCLA has used this data to evaluate SNP associations with phenotypes from the Mouse Phenome Project (the Mouse Phenome Database), and to construct haplotype maps for a total of 94 inbred strains (the Mouse HapMap Project). SNP and genotype positions have been mapped from their original reference coordinates to NCBI Mouse Build 37 coordinates. Note that C57BL6/J strain was not selected for re-sequencing as this data would have been almost entirely redundant with the NCBI reference sequence. Since we did not actually determine genotypes for C57BL6/J, we did not submit genotypes for this strain to dbSNP. However, implicit genotypes for C57BL6/J can be obtained from the reference sequence at each SNP position (the reference allele is the first allele in the ALLELES column). The data is available for download in two different compressed file formats. The files are saved as both PC .zip files and Unix compressed .gz files. At this website, you can: * Learn more about the goals of the Perlegen mouse resequencing project. * Learn more about the array-based resequencing technology used in the project. * Download the SNPs, genotypes, and other data generated by the project, plus sequences of the long-range PCR primers used for SNP discovery. * Browse the mouse genome for SNPs. * View the haplotype blocks within the mouse genome. Mouse Genome Browser The Mouse Genome Browser can be used to visualize genes and the SNPs discovered in this study of genome-wide DNA variation in 15 commonly used, genetically diverse strains of inbred laboratory mice. The reference genome is the C57BL/6J strain NCBI build 37 mouse sequence. In addition to the experimentally-derived genotypes for the original 15 strains, the imputed genotypes for 40 additional inbred mouse strains can also be accessed. Mouse Haplotype Analysis The sequences of 16 commonly used, genetically diverse strains of inbred laboratory mice were analyzed to determine their haplotype structure. The Ancestry Browser shows which ancestral sequence each inbred strain most resembles, along with statistics on the pairwise similarity between the ancestral strains. The Haplotype Viewer shows the haplotype block boundaries and the pairwise similarity for all 56 strains: the 15 used for SNP discovery, the reference strain (C57BL/6J), and the 40 additional strains for which the genotypes were imputed.

Proper citation: Perlegen/NIEHS National Toxicology: Mouse Genome Resequencing Project (RRID:SCR_000726) Copy   

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http://www.broad.mit.edu/mpr/lung

Data set of a molecular taxonomy of lung carcinoma, the leading cause of cancer death in the United States and worldwide. Using oligonucleotide microarrays, researchers analyzed mRNA expression levels corresponding to 12,600 transcript sequences in 186 lung tumor samples, including 139 adenocarcinomas resected from the lung. Hierarchical and probabilistic clustering of expression data defined distinct sub-classes of lung adenocarcinoma. Among these were tumors with high relative expression of neuroendocrine genes and of type II pneumocyte genes, respectively. Retrospective analysis revealed a less favorable outcome for the adenocarcinomas with neuroendocrine gene expression. The diagnostic potential of expression profiling is emphasized by its ability to discriminate primary lung adenocarcinomas from metastases of extra-pulmonary origin. These results suggest that integration of expression profile data with clinical parameters could aid in diagnosis of lung cancer patients.

Proper citation: Classification of Human Lung Carcinomas by mRNA Expression Profiling Reveals Distinct Adenocarcinoma Sub-classes (RRID:SCR_003010) Copy   

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http://www.uniprot.org/program/Chordata

Data set of manually annotated chordata-specific proteins as well as those that are widely conserved. The program keeps existing human entries up-to-date and broadens the manual annotation to other vertebrate species, especially model organisms, including great apes, cow, mouse, rat, chicken, zebrafish, as well as Xenopus laevis and Xenopus tropicalis. A draft of the complete human proteome is available in UniProtKB/Swiss-Prot and one of the current priorities of the Chordata protein annotation program is to improve the quality of human sequences provided. To this aim, they are updating sequences which show discrepancies with those predicted from the genome sequence. Dubious isoforms, sequences based on experimental artifacts and protein products derived from erroneous gene model predictions are also revisited. This work is in part done in collaboration with the Hinxton Sequence Forum (HSF), which allows active exchange between UniProt, HAVANA, Ensembl and HGNC groups, as well as with RefSeq database. UniProt is a member of the Consensus CDS project and thye are in the process of reviewing their records to support convergence towards a standard set of protein annotation. They also continuously update human entries with functional annotation, including novel structural, post-translational modification, interaction and enzymatic activity data. In order to identify candidates for re-annotation, they use, among others, information extraction tools such as the STRING database. In addition, they regularly add new sequence variants and maintain disease information. Indeed, this annotation program includes the Variation Annotation Program, the goal of which is to annotate all known human genetic diseases and disease-linked protein variants, as well as neutral polymorphisms.

Proper citation: UniProt Chordata protein annotation program (RRID:SCR_007071) 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   

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http://kinasedb.ontology.ims.u-tokyo.ac.jp

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. KinasePathwayDatabase is an integrated database concerning completed sequenced major eukaryotes, which contains the classification of protein kinases and their functional conservation and orthologous tables among species, protein-protein interaction data, domain information, structural information, and automatic pathway graph image interface. The protein-protein interactions are extracted by natural language processing (NLP) from abstracts using basic word pattern and protein name dictionary GENA: developed by our group. In this system, pathways are easily compared among species using protein interactions data more than 47,000 and orthologous tables.

Proper citation: Kinase Pathway Database (RRID:SCR_008199) Copy   

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http://www.roselab.jhu.edu/coil/

The Protein Coil Library is a library of protein structure fragments derived from the Protein Data Bank (PDB). The fragments in this library are those fragments in the PDB that cannot be classified as either alpha-helix or beta-strand. Three-dimensional structures as well as side-chain and backbone torsion angles are stored in the database. The Protein Coil Library allows rapid and comprehensive access to non-alpha-helix and non-beta-strand fragments contained in the Protein Data Bank (PDB). The library contains both sequence and structure information together with calculated torsion angles for both the backbone and side chains. Several search options are implemented, including a query function that uses output from popular PDB-culling servers directly. Additionally, several popular searches are stored and updated for immediate access. The library is a useful tool for exploring conformational propensities, turn motifs, and a recent model of the unfolded state. The library stores the complete torsion angle descriptions for the fragments as well as the three dimensional structures of the fragments themselves. The goal of extracting and pre-calculating this data is to allow for more straightforward investigation of peptide structure without the background of secondary structure elements. In addition to searching by PDB ID, it is possible to download a particular size class, perform a batch search of PDB/chain ID''s, or download precompiled lists of PDB ID''s of interest (PDB Select, etc.). For users interested in browsing the entire database at once or maintaining their own locally-updated copy of the library, FTP access instructions are also provided. The files stored in the coil library FTP site or returned after a batch search are organized heirarchically by PDB ID. This is done to reduce filesystem access times and fascilitate searches using the UNIX find utility. At the lowest directory level in the heirarchy, files are further sorted by fragment length. As a result, the number of files in a particular directory is generally less then 50, yielding relatively fast access on UNIX/Linux filesystems. The heirarchical organization is based on the middle two letters of the PDB ID. For example, hen egg lysozyme, which has a PDB ID of 1HEL, will be located in the directory h/he/. At the final level, fragments of varying sizes are stored in directories that correspond to their fragment length. Again, using lysozyme as an example, any seven-residue fragments, if they exist, will reside in the directory h/he/7/. Similarly, seven-residue fragments from 2HEX and 1HE0 will also be in this location. Sponsors: The Protein Coil Library is funded by Johns Hopkins University.

Proper citation: The Protein Coil Library (RRID:SCR_008233) Copy   

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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://animal.dna.affrc.go.jp/agp/index.html

Database of comparative gene mapping between species to assist the mapping of the genes related to phenotypic traits in livestock. The linkage maps, cytogenetic maps, polymerase chain reaction primers of pig, cattle, mouse and human, and their references have been included in the database, and the correspondence among species have been stipulated in the database. AGP is an animal genome database developed on a Unix workstation and maintained by a relational database management system. It is a joint project of National Institute of Agrobiological Sciences (NIAS) and Institute of the Society for Techno-innovation of Agriculture, Forestry and Fisheries (STAFF-Institute), under cooperation with other related research institutes. AGP also contains the Pig Expression Data Explorer (PEDE), a database of porcine EST collections derived from full-length cDNA libraries and full-length sequences of the cDNA clones picked from the EST collection. The EST sequences have been clustered and assembled, and their similarity to sequences in RefSeq, and UniGene determined. The PEDE database system was constructed to store sequences and similarity data of swine full-length cDNA libraries and to make them available to users. It provides interfaces for keyword and ID searches of BLAST results and enables users to obtain sequence data and names of clones of interest. Putative SNPs in EST assemblies have been classified according to breed specificity and their effect on coding amino acids, and the assemblies are equipped with an SNP search interface. The database contains porcine nucleotide sequences and cDNA clones that are ready for analyses such as expression in mammalian cells, because of their high likelihood of containing full-length CDS. PEDE will be useful for researchers who want to explore genes that may be responsible for traits such as disease susceptibility. The database also offers information regarding major and minor porcine-specific antigens, which might be investigated in regard to the use of pigs as models in various medical research applications.

Proper citation: Animal Genome Database (RRID:SCR_008165) Copy   

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http://www.uni-wh.de/pcogr

THIS RESOURCE IS NO LONGER IN SERVICE, documented on August 20,2019.The COG-database has become a powerful tool in the field of comparative genomics. The construction of this data-base is based on sequence homologies of proteins from different completely sequenced genomes. Highly homologous proteins are assigned to clusters of orthologous groups. The updated collection of orthologous protein sets for prokaryotes and eukaryotes is expected to be a useful platform for functional annotation of newly sequenced genomes, including those of complex eukaryotes, and genome-wide evolutionary studies. The availability of multiple, essentially complete genome sequences of prokaryotes and eukaryotes spurred both the demand and the opportunity for the construction of an evolutionary classification of genes from these genomes. Such a classification system based on orthologous relationships between genes appears to be a natural framework for comparative genomics and should facilitate both functional annotation of genomes and large-scale evolutionary studies. Here is a major update of the previously developed system for delineation of Clusters of Orthologous Groups of proteins (COGs) from the sequenced genomes of prokaryotes and unicellular eukaryotes and the construction of clusters of predicted orthologs for 7 eukaryotic genomes, which we named KOGs after eukaryotic orthologous groups. The COG collection currently consists of 138,458 proteins, which form 4873 COGs and comprise 75% of the 185,505 (predicted) proteins encoded in 66 genomes of unicellular organisms. The eukaryotic orthologous groups (KOGs) include proteins from 7 eukaryotic genomes: three animals (the nematode Caenorhabditis elegans, the fruit fly Drosophila melanogaster and Homo sapiens), one plant, Arabidopsis thaliana, two fungi (Saccharomyces cerevisiae and Schizosaccharomyces pombe), and the intracellular microsporidian parasite Encephalitozoon cuniculi. The current KOG set consists of 4852 clusters of orthologs, which include 59,838 proteins, or approximately 54% of the analyzed eukaryotic 110,655 gene products. Compared to the coverage of the prokaryotic genomes with COGs, a considerably smaller fraction of eukaryotic genes could be included into the KOGs; addition of new eukaryotic genomes is expected to result in substantial increase in the coverage of eukaryotic genomes with KOGs. Examination of the phyletic patterns of KOGs reveals a conserved core represented in all analyzed species and consisting of approximately 20% of the KOG set. This conserved portion of the KOG set is much greater than the ubiquitous portion of the COG set (approximately 1% of the COGs). In part, this difference is probably due to the small number of included eukaryotic genomes, but it could also reflect the relative compactness of eukaryotes as a clade and the greater evolutionary stability of eukaryotic genomes.

Proper citation: Phylogenetic Clusters of Orthologous Groups Ranking (RRID:SCR_008223) Copy   

•   Source: SciCrunch Registry

http://www.nisc.nih.gov/projects/comp_seq.html

Generates data for use in developing and refining computational tools for comparing genomic sequence from multiple species. The NISC Comparative Sequencing Program's goal is to establish a data resource consisting of sequences for the same set of targeted genomic regions derived from multiple animal species. The broader program includes plans for a diverse set of analytical studies using the generated sequence and the publication of a series of papers describing the results of those analysis in peer-reviewed journals in a timely fashion. Experimentally, this project involves the shotgun sequencing of mapped BAC clones. For each BAC, an assembly is first performed when a sufficient number of sequence reads have been generated to provide full shotgun coverage of the clone. At that time, the assembled sequence is submitted to the HTGS division of GenBank. Subsequent refinements of the sequence, including the generation of higher-accuracy finished sequence, results in the updating of the sequence record in GenBank. By immediately submitting our BAC-derived sequences to GenBank, it makes their data available as a public service to allow colleagues to speed up their research, consistent with the now well-established routine of sequencing centers participating in the Human Genome Project. However, at the same time, it has made considerable investment in acquiring these mapping and sequence data, including sizable efforts of graduate students, postdoctoral fellows, and other trainees. Furthermore, in most cases, large data sets involving multiple BAC sequences from multiple species must first be generated, often taking many months to accumulate, before the planned analysis can be performed and the resulting papers written and submitted for publication.

Proper citation: Comparative Vertebrate Sequencing (RRID:SCR_008213) Copy   

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http://genome.jgi.doe.gov/programs/metagenomes/index.jsf

Portal providing access to metagenomics projects, data and tools supported by the DOE Joint Genome Institute (JGI). A primary motivation for metagenomics is that most microbes found in nature exist in complex, interdependent communities and cannot readily be grown in isolation in the laboratory. One can, however, isolate DNA or RNA from the community as a whole, and studies of such communities have revealed a diversity of microbes far beyond those found in culture collections. It is suspected that these uncultivated organisms must harbor considerable as-yet undiscovered genomic, functional, and metabolic features and capabilities. Thus to fully explore microbial genomics, it is imperative that we access the genomes of these elusive players.

Proper citation: Metagenomics Program at JGI (RRID:SCR_008804) Copy   

•   Source: SciCrunch Registry

http://lemur.amu.edu.pl/share/php/mirnest/home.php

A database of animal, plant and virus microRNA data maintained at the University of Poznan. The database provides: * 9980 miRNA candiates from 420 animal and plant species predicted in Expressed Sequence Tags * predicted targets for plant candidates * RNA-seq reads mapped to candidates from 29 species * external data from 12 databases that includes sequences, polymorphism, expression and regulation. miRNEST 1.0, it contains miRNA from 563 animals, plants and viruses plant species.

Proper citation: miRNEST (RRID:SCR_008907) Copy   

•   Source: SciCrunch Registry

http://cgap.nci.nih.gov/Chromosomes/Mitelman

The web site includes genomic data for humans and mice, including transcript sequence, gene expression patterns, single-nucleotide polymorphisms, clone resources, and cytogenetic information. Descriptions of the methods and reagents used in deriving the CGAP datasets are also provided. An extensive suite of informatics tools facilitates queries and analysis of the CGAP data by the community. One of the newest features of the CGAP web site is an electronic version of the Mitelman Database of Chromosome Aberrations in Cancer. The data in the Mitelman Database is manually culled from the literature and subsequently organized into three distinct sub-databases, as follows: -The sub-database of cases contains the data that relates chromosomal aberrations to specific tumor characteristics in individual patient cases. It can be searched using either the Cases Quick Searcher or the Cases Full Searcher. -The sub-database of molecular biology and clinical associations contains no data from individual patient cases. Instead, the data is pulled from studies with distinct information about: -Molecular biology associations that relate chromosomal aberrations and tumor histologies to genomic sequence data, typically genes rearranged as a consequence of structural chromosome changes. -Clinical associations that relate chromosomal aberrations and/or gene rearrangements and tumor histologies to clinical variables, such as prognosis, tumor grade, and patient characteristics. It can be searched using the Molecular Biology and Clinical (MBC) Associations Searcher -The reference sub-database contains all the references culled from the literature i.e., the sum of the references from the cases and the molecular biology and clinical associations. It can be searched using the Reference Searcher. CGAP has developed six web search tools to help you analyze the information within the Mitelman Database: -The Cases Quick Searcher allows you to query the individual patient cases using the four major fields: aberration, breakpoint, morphology, and topography. -The Cases Full Searcher permits a more detailed search of the same individual patient cases as above, by including more cytogenetic field choices and adding search fields for patient characteristics and references. -The Molecular Biology Associations Searcher does not search any of the individual patient cases. It searches studies pertaining to gene rearrangements as a consequence of cytogenetic aberrations. -The Clinical Associations Searcher does not search any of the individual patient cases. It searches studies pertaining to clinical associations of cytogenetic aberrations and/or gene rearrangements. -The Recurrent Chromosome Aberrations Searcher provides a way to search for structural and numerical abnormalities that are recurrent, i.e., present in two or more cases with the same morphology and topography. -The Reference Searcher queries only the references themselves, i.e., the references from the individual cases and the molecular biology and clinical associations. Sponsors: This database is sponsored by the University of Lund, Sweden and have support from the Swedish Cancer Society and the Swedish Children''s Cancer Foundation

Proper citation: Mitelman Database of Chromosome Aberrations in Cancer (RRID:SCR_012877) Copy   

•   Source: SciCrunch Registry