Post by DAVID user on Jul 22, 2020 15:16:43 GMT -5
I have a question about my results from the functional annotation chart. I am working on C. elegans. I have uploaded a list of genes to DAVID 6.8 and run a query against the GO annotations. I have got a list of ~180 GO categories, one of them is GO:0006915~apoptotic process. As I didn't expect to see this category in the list I have taken a closer look at the list of genes in the results. But Unfortunately the genes which are listed in the results are not part of this category.
two of the genes for example are NARS-1 or RPL-20. But when searching the QuickGO website for these genes, they are not part of this GO category.
For that reason I am questioning the results now.
The worse thing about it is, that when running the same analysis with DAVID 6.7, these GO category (and others, which shows wrongly categorized genes) are missing. So the older version shows better, more accurate results than the newer version.
I would like to know, where the results of the GO enrichment analysis are coming from. How reliable are the results? Does DAVID check the accuracy of the gene<-> GO category in any way before loading them to the Knowledge base?
Post by DAVID user on Jul 22, 2020 15:16:56 GMT -5
Thank you for contacting us and bringing this to our attention.
DAVID pulls GO annotations from NCBI and Uniprot. The annotation of GO:0006915~apoptotic process to NARS-1 and RPL-20 came from NCBI which seems to have obtained the mapping from WormBase. This association for both genes was last seen in WormBase release WS260 which cites the following paper:
Rebecca A. Green, Huey-Ling Kao, Anjon Audhya, Swathi Arur, Jonathan R. Mayers, Heidi N. Fridolfsson, Monty Schulman, Siegfried Schloissnig, Sherry Niessen, Kimberley Laband, Shaohe Wang, Daniel A. Starr, Anthony A. Hyman, Tim Schedl, Arshad Desai, Fabio Piano, Kristin C. Gunsalus, Karen Oegema, A High-Resolution C. elegans Essential Gene Network Based on Phenotypic Profiling of a Complex Tissue, Cell, Volume 145, Issue 3, 2011, Pages 470-482, ISSN 0092-8674, doi.org/10.1016/j.cell.2011.03.037. (http://www.sciencedirect.com/science/article/pii/S0092867411003710) Abstract: Summary High-content screening for gene profiling has generally been limited to single cells. Here, we explore an alternative approach—profiling gene function by analyzing effects of gene knockdowns on the architecture of a complex tissue in a multicellular organism. We profile 554 essential C. elegans genes by imaging gonad architecture and scoring 94 phenotypic features. To generate a reference for evaluating methods for network construction, genes were manually partitioned into 102 phenotypic classes, predicting functions for uncharacterized genes across diverse cellular processes. Using this classification as a benchmark, we developed a robust computational method for constructing gene networks from high-content profiles based on a network context-dependent measure that ranks the significance of links between genes. Our analysis reveals that multi-parametric profiling in a complex tissue yields functional maps with a resolution similar to genetic interaction-based profiling in unicellular eukaryotes—pinpointing subunits of macromolecular complexes and components functioning in common cellular processes.
Wormbase and subsequently NCBI no longer list this GO term associated with these genes. We are currently working on a database update for DAVID and I have confirmed that this association is no longer present. We hope to release the update in the near future.