ArticleBMC bioinformatics2006
Genome comparison using Gene Ontology (GO) with statistical testing.
Article in BMC bioinformatics, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Ultrasmall oral Saccharibacteria modulate gingival immunoactivation through type IV pili and TLR2-dependent endocytosis.Nature communications · 2026Article
- High-Throughput Sequencing-Based Identification of Arabidopsis miRNAs Induced byFrontiers in microbiology · 2020Article
- Genome ofG3 (Bethesda, Md.) · 2019Article
- Variation in hybrid gene expression: Implications for the evolution of genetic incompatibilities in interbreeding species.Molecular ecology · 2019Article
- The draft genome of Ruellia speciosa (Beautiful Wild Petunia: Acanthaceae).DNA research : an international journal for rapid publication of reports on genes and genomes · 2017Article
- KOMODO: a web tool for detecting and visualizing biased distribution of groups of homologous genes in monophyletic taxa.Nucleic acids research · 2012Article
- Comparison of lists of genes based on functional profiles.BMC bioinformatics · 2011Article
- High-throughput functional annotation and data mining with the Blast2GO suite.Nucleic acids research · 2008Article
- Bioinformatics in China: a personal perspective.PLoS computational biology · 2008Review
- Altered patterns of gene duplication and differential gene gain and loss in fungal pathogens.BMC genomics · 2008Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAutomated comparison of complete sets of genes encoded in two genomes can provide insight on the genetic basis of differences in biological traits between species. Gene ontology (GO) is used as a common vocabulary to annotate genes for comparison. Current approaches calculate the fold of unweighted or weighted differences between two species at the high-level GO functional categories. However, to ensure the reliability of the differences detected, it is important to evaluate their statistical significance. It is also useful to search for differences at all levels of GO.
resultsWe propose a statistical approach to find reliable differences between the complete sets of genes encoded in two genomes at all levels of GO. The genes are first assigned GO terms from BLAST searches against genes with known GO assignments, and for each GO term the abundance of genes in the two genomes is compared using a chi-squared test followed by false discovery rate (FDR) correction. We applied this method to find statistically significant differences between two cyanobacteria, Synechocystis sp. PCC6803 and Anabaena sp. PCC7120. We then studied how the set of identified differences vary when different BLAST cutoffs are used. We also studied how the results vary when only subsets of the genes were used in the comparison of human vs. mouse and that of Saccharomyces cerevisiae vs. Schizosaccharomyces pombe.
conclusionThere is a surprising lack of statistical approaches for comparing complete genomes at all levels of GO. With the rapid increase of the number of sequenced genomes, we hope that the approach we proposed and tested can make valuable contribution to comparative genomics.
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