Nucleic Acids Res. 2023 Nov 13:gkad1060. doi: 10.1093/nar/gkad1060. Online ahead of print.
ABSTRACT
Advancements in high-throughput technology offer researchers an extensive range of multi-omics data that provide deep insights into the complex landscape of cancer biology. However, traditional statistical models and databases are inadequate to interpret these high-dimensional data within a multi-omics framework. To address this limitation, we introduce DriverDBv4, an updated iteration of the DriverDB cancer driver gene database (http:////driverdb.bioinfomics.org//). This updated version offers several significant enhancements: (i) an increase in the number of cohorts from 33 to 70, encompassing approximately 24 000 samples; (ii) inclusion of proteomics data, augmenting the existing types of omics data and thus expanding the analytical scope; (iii) implementation of multiple multi-omics algorithms for identification of cancer drivers; (iv) new visualization features designed to succinctly summarize high-context data and redesigned existing sections to accommodate the increased volume of datasets and (v) two new functions in Customized Analysis, specifically designed for multi-omics driver identification and subgroup expression analysis. DriverDBv4 facilitates comprehensive interpretation of multi-omics data across diverse cancer types, thereby enriching the understanding of cancer heterogeneity and aiding in the development of personalized clinical approaches. The database is designed to foster a more nuanced understanding of the multi-faceted nature of cancer.
PMID:37956338 | DOI:10.1093/nar/gkad1060
