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However, AEA isolates and tests for overrepresentation only individual annotation terms or groups of similar terms and is limited in its ability to uncover complex phenomena involving relationship between multiple annotation terms from various knowledge bases.
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These genes were associated with several cellular themes which were each associated with multiple location annotation terms.
PFPDB is a collection of GO terms from multiple annotation resources, including UniProt-Swiss-Prot.
In all tools, the input list of genes is mapped to the biological terms in databases, and then statistical analysis examines the enrichment of gene members for each of the annotation terms and corrects for multiple testing [6].
Examination of the genes that responded at the low concentration did not reveal any significantly enriched functional annotation terms after adjustment for multiple comparisons.
Notably, the use of multiple annotation sources does not only increase the number of annotation terms per protein, but also improves the specificity of the annotations.
In case of multiple appearances of similar gene families or functional annotation terms, the cluster with the higher enrichment factor was selected.
Using GREAT, we found that a variety of annotation terms were significantly enriched after correcting for multiple testing using q-values (false discovery rates).
Because gene names are constantly evolving, multiple annotation tables are searched for relevant information on the input term.
In Table 2, we show the effects of combining multiple annotation resources (from which annotations are transferred) for the updated PFPDB in terms of the sequence coverage and the GO coverage.
Evaluation of Gene Ontology terms associated with the differentially regulated genes was only moderately informative, since the genes belonged to multiple annotation categories that only rarely overlapped (Additional file 3: Table S1).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com