Massively parallel sequencing technologies have enabled characterization of genomic alterations across multiple tumor types. Efforts have focused on identifying driver mutations because they represent potential targets for therapy. However, because of the presence of driver and passenger mutations, it is often challenging to assign the clinical relevance of specific mutations observed in patients.
Hosts driver mutations in cancers that are potentially actionable to support annotation in clinical genomic testing laboratories by molecular pathologists, laboratory directors, and bioinformaticians. CanDL database contains mutations in 56 genes with 330 distinct variants with 160 unique matching references across multiple cancers. Entries can be searched by gene(s) or by amino acid variants, or downloaded for custom analyses. Although the default output includes normal amino acid, peptide position, variant amino acid, cancer type, and the reference for a given gene, users have the option to customize output to include additional information (eg, exon, mutation coding DNA sequence, transcript). The users can either upload candidate mutations or download the entire log from the website.
A graph database for storage and querying of conditional relationships between molecular events observed at different stages of colorectal oncogenesis. EpiGeNet integrates a Neo4j-based framework to capture and explore semantic relationships observed at different pathological levels in colorectal cancer development and to manage genetic–epigenetic interdependencies. In a graph database, concepts are represented by nodes and their associations by edges. EpiGeNet provides a more natural way of representing highly interconnected data, with exploration of stored content benefitting additionally from the use of different graph algorithms.
Couples statistical tests with spatial information to provide context behind decisions identifying cancer driver genes. SBCDDB is a database that contains 19 primary mouse models of human tumor types from both published and provisional studies. This resource provides a step beyond identifying drivers to categorize drivers as acting in an oncogenic or tumor suppressive manner, based on the transposon insertion patterns.
Incorporates approximately 6000 cases of exome-seq data, in addition to annotation databases and published bioinformatics algorithms dedicated to driver gene/mutation identification. The database provides two points of view, 'Cancer' and 'Gene', to help researchers visualize the relationships between cancers and driver genes/mutations. In the updated DriverDBv2 database, we incorporated >9500 cancer-related RNA-seq datasets and >7000 more exome-seq datasets from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and published papers. Furthermore, there are two main new features, 'Expression' and 'Hotspot', in the 'Gene' section. 'Expression' displays two expression profiles of a gene in terms of sample types and mutation types, respectively. 'Hotspot' indicates the hotspot mutation regions of a gene according to the results provided by four bioinformatics tools. A new function, 'Gene Set', allows users to investigate the relationships among mutations, expression levels and clinical data for a set of genes, a specific dataset and clinical features.
Compiles driver mutations in protein kinases (PKs) with experimental evidence demonstrating their functional role. Kin-driver is a manual expert-curated database that pays special attention to activating mutations (AMs) and can serve as a validation set to develop new generation tools focused on the prediction of gain-of-function driver mutations. It also offers an easy and intuitive environment to facilitate the visualization and analysis of mutations in PKs.
Provides a resource of known drivers, oncogenes and tumor suppressors in a wide variety of cancer types. CancerMine employs a text mining approach that permits users to discern complicated descriptions of cancer gene roles with a high level of precision. This method can also extract other types of biological knowledge with only minor changes. Data are accessible through a web viewer or a download file.