Bioinformatics Resources | David

Developed by the Laboratory of Human Retrovirology and Immunoinformatics (LHRI) at the NIH, DAVID was created to bridge the gap between large-scale data acquisition and biological meaning. The tool was designed to systematically extract biological themes from lists of genes or proteins.

Forgetting to change the species or using an incorrect background list is the most common user error. If you analyze a list of human kinases against a default yeast background, every single term will appear massively enriched (but falsely so). david bioinformatics resources

After years of successful operation and a major transition to the University of Maryland, Baltimore County (UMBC), the resource rebranded as the . Today, the platform is managed by a dedicated team ensuring that it remains updated, secure, and accessible. The recent release of DAVID 2023 (Version 2.0) represents a massive overhaul, including updated gene identifiers, improved algorithms, and a more intuitive user interface, solidifying its reputation as a "must-use" resource. Core Features: What Makes DAVID Indispensable? DAVID is not just a single tool; it is an integrated ecosystem of resources. Its power lies in its ability to aggregate over 90 different annotation databases into a single, user-friendly platform. Here are its critical components. 1. Functional Annotation Clustering (The "Crown Jewel") The most celebrated feature of DAVID is Functional Annotation Clustering . Traditional enrichment analysis suffers from redundancy. For example, if you analyze a list of immune genes, you might get 50 redundant terms like "immune response," "immune system process," "defense response," and "inflammatory response." Developed by the Laboratory of Human Retrovirology and

This is where comes into play. Standing for the Database for Annotation, Visualization, and Integrated Discovery , DAVID has become a cornerstone platform for functional genomic analysis. Since its inception at the National Institute of Allergy and Infectious Diseases (NIAID/NIH), DAVID has helped over 40,000 unique users from more than 100 countries transform raw gene lists into meaningful biological hypotheses. If you analyze a list of human kinases

Highly studied genes (e.g., TP53 , AKT1 , MAPK1 ) appear in many papers and are thus overrepresented in databases. Consequently, these genes frequently, and sometimes trivially, show up as "enriched" in large lists.

Navigate to david.ncifcrf.gov . Paste your gene list (e.g., a column of 200 gene symbols) into the upload window. Select the correct identifier type (e.g., "OFFICIAL_GENE_SYMBOL"). Choose the list type ("Gene List").