Back to Search Start Over

DriverDBv4: a multi-omics integration database for cancer driver gene research.

Authors :
Liu CH
Lai YL
Shen PC
Liu HC
Tsai MH
Wang YD
Lin WJ
Chen FH
Li CY
Wang SC
Hung MC
Cheng WC
Source :
Nucleic acids research [Nucleic Acids Res] 2024 Jan 05; Vol. 52 (D1), pp. D1246-D1252.
Publication Year :
2024

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.<br /> (© The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Details

Language :
English
ISSN :
1362-4962
Volume :
52
Issue :
D1
Database :
MEDLINE
Journal :
Nucleic acids research
Publication Type :
Academic Journal
Accession number :
37956338
Full Text :
https://doi.org/10.1093/nar/gkad1060