DAVID Bioinformatics Resources: expanded annotation database and novel algorithms to better extract biology from large gene lists

By Yongjian Guo. Nucleic Acids Res., 2007

1,967 citations recorded by the source corpus.

All tools in the DAVID Bioinformatics Resources aim to provide functional interpretation of large lists of genes derived from genomic studies. The newly updated DAVID Bioinformatics Resources consists of the DAVID Knowledgebase and five integrated, web-based functional annotation tool suites: the DAVID Gene Functional Classification Tool, the DAVID Functional Annotation Tool, the DAVID Gene ID Conversion Tool, the DAVID Gene Name Viewer and the DAVID NIAID Pathogen Genome Browser. The expanded DAVID Knowledgebase now integrates almost all major and well-known public bioinformatics resources centralized by the DAVID Gene Concept, a single-linkage method to agglomerate tens of millions of diverse gene/protein identifiers and annotation terms from a variety of public bioinformatics databases. For any uploaded gene list, the DAVID Resources now provides not only the typical gene-term enrichment analysis, but also new tools and functions that allow users to condense large gene lists into gene functional groups, convert between gene/protein identifiers, visualize many-genes-to-many-terms relationships, cluster redundant and heterogeneous terms into groups, search for interesting and related genes or terms, dynamically view genes from their lists on bio-pathways and more. With DAVID (http://david.niaid.nih.gov), investigators gain more power to interpret the biological mechanisms associated with large gene lists.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/5248a01a50c85af1eed551c166464f7b4c4db0e0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

Citations in the corpus

Mining breast cancer genes with a network based noise-tolerant approach (2013, methodology): “For the KnownSet, we used three tools (DAVID [53], GOstats [54], GOEAST [55]) to perform enrichment analyses in the BP (Biological Process) sub-ontology.”
NetworkAnalyst for statistical, visual and network-based meta-analysis of gene expression data (2015, methodology): “Opening up multiple connections to NetworkAnalyst within the same browser will cause unpredictable behavior of the application and the results.”
Predicting Drug Response and Synergy Using a Deep Learning Model of Human Cancer Cells (2020, methodology): “Pathways enriched for differentially expressed genes were determined using DAVID (Huang et al., 2007).”
Reference Maps of Human ES and iPS Cell Variation Enable High-Throughput Characterization of Pluripotent Cell Lines (2011, positive): “(B) Chromosomal distribution of the 1000 most variable genes in terms of DNA diagram also shows the chromosomal distribution of all genes with sufficient DN (C) Comparison of the 1000 most variable genes in terms of DNA methylation differences of male versus female cell lines, all X-linked and Y-lin”
Extracting Biological Meaning from Large Gene Lists with DAVID (2009, positive): “This protocol describes several DAVID tools, all of which aim at extracting biological meaning from large gene lists (Huang et al., 2007a,b).”
Pathway and Network Analysis of Cancer Genomes (2015, neutral): “The basic form of this test is applied in many tools ( Supplementary Table 1 ) including the widely used but no longer updated web service DAVID 24 .”