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DriverDB: an exome sequencing database for cancer driver gene identification.

Nucleic acids research | Jan 30, 2014

Exome sequencing (exome-seq) has aided in the discovery of a huge amount of mutations in cancers, yet challenges remain in converting oncogenomics data into information that is interpretable and accessible for clinical care. We constructed DriverDB (http://ngs.ym.edu.tw/driverdb/), a database which incorporates 6079 cases of exome-seq data, annotation databases (such as dbSNP, 1000 Genome and Cosmic) and published bioinformatics algorithms dedicated to driver gene/mutation identification. We provide two points of view, 'Cancer' and 'Gene', to help researchers to visualize the relationships between cancers and driver genes/mutations. The 'Cancer' section summarizes the calculated results of driver genes by eight computational methods for a specific cancer type/dataset and provides three levels of biological interpretation for realization of the relationships between driver genes. The 'Gene' section is designed to visualize the mutation information of a driver gene in five different aspects. Moreover, a 'Meta-Analysis' function is provided so researchers may identify driver genes in customer-defined samples. The novel driver genes/mutations identified hold potential for both basic research and biotech applications.

Pubmed ID: 24214964 RIS Download

Mesh terms: Databases, Nucleic Acid | Exome | Genes, Neoplasm | High-Throughput Nucleotide Sequencing | Humans | Internet | Molecular Sequence Annotation | Mutation

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This is a list of tools and resources that we have found mentioned in this publication.


The Cancer Genome Atlas

Project exploring the spectrum of genomic changes involved in more than 20 types of human cancer that provides a platform for researchers to search, download, and analyze data sets generated. As a pilot project it confirmed that an atlas of changes could be created for specific cancer types. It also showed that a national network of research and technology teams working on distinct but related projects could pool the results of their efforts, create an economy of scale and develop an infrastructure for making the data publicly accessible. Its success committed resources to collect and characterize more than 20 additional tumor types. Components of the TCGA Research Network: * Biospecimen Core Resource (BCR); Tissue samples are carefully cataloged, processed, checked for quality and stored, complete with important medical information about the patient. * Genome Characterization Centers (GCCs); Several technologies will be used to analyze genomic changes involved in cancer. The genomic changes that are identified will be further studied by the Genome Sequencing Centers. * Genome Sequencing Centers (GSCs); High-throughput Genome Sequencing Centers will identify the changes in DNA sequences that are associated with specific types of cancer. * Proteome Characterization Centers (PCCs); The centers, a component of NCI's Clinical Proteomic Tumor Analysis Consortium, will ascertain and analyze the total proteomic content of a subset of TCGA samples. * Data Coordinating Center (DCC); The information that is generated by TCGA will be centrally managed at the DCC and entered into the TCGA Data Portal and Cancer Genomics Hub as it becomes available. Centralization of data facilitates data transfer between the network and the research community, and makes data analysis more efficient. The DCC manages the TCGA Data Portal. * Cancer Genomics Hub (CGHub); Lower level sequence data will be deposited into a secure repository. This database stores cancer genome sequences and alignments. * Genome Data Analysis Centers (GDACs) - Immense amounts of data from array and second-generation sequencing technologies must be integrated across thousands of samples. These centers will provide novel informatics tools to the entire research community to facilitate broader use of TCGA data. TCGA is actively developing a network of collaborators who are able to provide samples that are collected retrospectively (tissues that had already been collected and stored) or prospectively (tissues that will be collected in the future).

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ClinVar

Freely accessible, public archive of aggregated information about sequence variation and its relationship to human health that provides reports of the relationships among human variations and phenotypes along with supporting evidence. Submissions from clinical testing labs, research labs, locus-specific databases, expert panels and professional societies are welcome. The database has a flexible data model, so submissions may be minimal or very detailed. ClinVar collects reports of variants found in patient samples, assertions made regarding their clinical significance, information about the submitter, and other supporting data. The alleles described in the submissions are mapped to reference sequences, and reported according to the HGVS standard. ClinVar then presents the data for individual users, laboratories that want to incorporate it in their daily workflow, and organizations that want to incorporate it into their own applications. They will work in collaboration with interested organizations to meet the needs of the medical genetics community as efficiently and effectively as possible. They emphasize reporting structured evidence supporting any genotype-phenotype relationship, in order to support computational (re)evaluation, both of genotypes and assertions, enabling the ongoing evolution and development of knowledge regarding variations and associated phenotypes. The ClinVar archive versions submissions, so when submitters update their records, the previous version is retained for review. The level of confidence in the accuracy of variation calls and assertions of clinical significance depends in large part on the supporting evidence, so this information, when available, is collected and visible to users. Since the availability of supporting evidence may vary, particularly in regard to retrospective data aggregated from published literature, the archive accepts submissions from multiple groups, and aggregates related information, to transparently reflect both consensus and conflicting assertions of clinical significance. A review status is also assigned to any assertion, to support communication about the trustworthiness of any assertion. Accessions, of the format SCV000000000.0, are assigned to each record. Reports about sets of assertions about the same variation/phenotype relationship can be aggregated and submitted as a reference accession of the format RCV000000000.0. Groups wishing to evaluate a set of ClinVar records thus can submit a review of a set of SCV or RCV records, with the result being the creation of a novel RCV record.

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OMIM

A comprehensive and detailed collection of human genes and genetic phenotypes, focusing on the relationship between phenotype and genotype. The full-text, referenced overviews in OMIM contain information on all known mendelian disorders and a variety of related genes. It is updated daily, and the entries contain copious links to other genetics resources.

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BioCarta Pathways

BioCarta Pathways allows users to observe how genes interact in dynamic graphical models. Online maps available within this resource depict molecular relationships from areas of active research. In an open source approach, this community-fed forum constantly integrates emerging proteomic information from the scientific community. It also catalogs and summarizes important resources providing information for over 120,000 genes from multiple species. Find both classical pathways as well as current suggestions for new pathways.

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Bioconductor

A catalog of tools and software packages for the analysis and comprehension of high-throughput genomic data that uses the R statistical programming language. Bioconductor has a development version to which new features and packages are added prior to incorporation in the release. A large number of meta-data packages provide pathway, organism, microarray and other annotations. The broad goals of the Bioconductor project are: to provide widespread access to a broad range of powerful statistical and graphical methods for the analysis of genomic data; to facilitate the inclusion of biological metadata in the analysis of genomic data; to provide a common software platform that enables the rapid development and deployment of extensible, scalable, and interoperable software; and to train researchers on computational and statistical methods for the analysis of genomic data.

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IntAct

Open source database system and analysis tools for molecular interaction data. All interactions are derived from literature curation or direct user submissions. Direct user submissions of molecular interaction data are encouraged, which may be deposited prior to publication in a peer-reviewed journal. The IntAct Database contains (Jun. 2014): * 447368 Interactions * 33021 experiments * 12698 publications * 82745 Interactors IntAct provides a two-tiered view of the interaction data. The search interface allows the user to iteratively develop complex queries, exploiting the detailed annotation with hierarchical controlled vocabularies. Results are provided at any stage in a simplified, tabular view. Specialized views then allows "zooming in" on the full annotation of interactions, interactors and their properties. IntAct source code and data are freely available.

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