Dataset Information

Genome comparison using Gene Ontology (GO) with statistical testing.

ABSTRACT: BACKGROUND: Automated 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. RESULTS: We 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. CONCLUSION: There 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.

SUBMITTER: Cai Z

PROVIDER: S-EPMC1569881 | biostudies-literature | 2006

REPOSITORIES: biostudies-literature

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Genome comparison using Gene Ontology (GO) with statistical testing.

Cai Zhaotao Z Mao Xizeng X Li Songgang S Wei Liping L

BMC bioinformatics 20060811

<h4>Background</h4>Automated 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 t ...[more]

PMID: 16901353

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Project description:The S-domain subfamily of receptor-like kinases (SDRLKs) in plants is poorly characterized. Most members of this subfamily are currently assigned gene function based on the S-locus Receptor Kinase from Brassica that acts as the female determinant of self-incompatibility (SI). However, Brassica like SI mechanisms does not exist in most plants. Thus, automated Gene Ontology (GO) pipelines are not sufficient for functional annotation of SDRLK subfamily members and lead to erroneous association with the GO biological process of SI. Here, we show that manual bio-curation can help to correct and improve the gene annotations and association with relevant biological processes. Using publicly available genomic and transcriptome datasets, we conducted a detailed analysis of the expansion of the rice (Oryza sativa) SDRLK subfamily, the structure of individual genes and proteins, and their expression.The 144-member SDRLK family in rice consists of 82 receptor-like kinases (RLKs) (67 full-length, 15 truncated),12 receptor-like proteins, 14 SD kinases, 26 kinase-like and 10 GnK2 domain-containing kinases and RLKs. Except for nine genes, all other SDRLK family members are transcribed in rice, but they vary in their tissue-specific and stress-response expression profiles. Furthermore, 98 genes show differential expression under biotic stress and 98 genes show differential expression under abiotic stress conditions, but share 81 genes in common.Our analysis led to the identification of candidate genes likely to play important roles in plant development, pathogen resistance, and abiotic stress tolerance. We propose a nomenclature for 144 SDRLK gene family members based on gene/protein conserved structural features, gene expression profiles, and literature review. Our biocuration approach, rooted in the principles of findability, accessibility, interoperability and reusability, sets forth an example of how manual annotation of large-gene families can fill in the knowledge gap that exists due to the implementation of automated GO projections, thereby helping to improve the quality and contents of public databases.

Dataset Information

Genome comparison using Gene Ontology (GO) with statistical testing.

Publications

Genome comparison using Gene Ontology (GO) with statistical testing.

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