Dataset Information

MeSH ORA framework: R/Bioconductor packages to support MeSH over-representation analysis.

ABSTRACT:

Background

In genome-wide studies, over-representation analysis (ORA) against a set of genes is an essential step for biological interpretation. Many gene annotation resources and software platforms for ORA have been proposed. Recently, Medical Subject Headings (MeSH) terms, which are annotations of PubMed documents, have been used for ORA. MeSH enables the extraction of broader meaning from the gene lists and is expected to become an exhaustive annotation resource for ORA. However, the existing MeSH ORA software platforms are still not sufficient for several reasons.

Results

In this work, we developed an original MeSH ORA framework composed of six types of R packages, including MeSH.db, MeSH.AOR.db, MeSH.PCR.db, the org.MeSH.XXX.db-type packages, MeSHDbi, and meshr.

Conclusions

Using our framework, users can easily conduct MeSH ORA. By utilizing the enriched MeSH terms, related PubMed documents can be retrieved and saved on local machines within this framework.

SUBMITTER: Tsuyuzaki K

PROVIDER: S-EPMC4343279 | biostudies-literature | 2015 Feb

REPOSITORIES: biostudies-literature

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Publications

MeSH ORA framework: R/Bioconductor packages to support MeSH over-representation analysis.

Tsuyuzaki Koki K Morota Gota G Ishii Manabu M Nakazato Takeru T Miyazaki Satoru S Nikaido Itoshi I

BMC bioinformatics 20150215

<h4>Background</h4>In genome-wide studies, over-representation analysis (ORA) against a set of genes is an essential step for biological interpretation. Many gene annotation resources and software platforms for ORA have been proposed. Recently, Medical Subject Headings (MeSH) terms, which are annotations of PubMed documents, have been used for ORA. MeSH enables the extraction of broader meaning from the gene lists and is expected to become an exhaustive annotation resource for ORA. However, the ...[more]

PMID: 25887539

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Project description:BackgroundThis paper describes the results of a Gene Ontology (GO) term enrichment analysis of chicken microarray data using the Bioconductor packages. By checking the enriched GO terms in three contrasts, MM8-PM8, MM8-MA8, and MM8-MM24, of the provided microarray data during this workshop, this analysis aimed to investigate the host reactions in chickens occurring shortly after a secondary challenge with either a homologous or heterologous species of Eimeria. The results of GO enrichment analysis using GO terms annotated to chicken genes and GO terms annotated to chicken-human orthologous genes were also compared. Furthermore, a locally adaptive statistical procedure (LAP) was performed to test differentially expressed chromosomal regions, rather than individual genes, in the chicken genome after Eimeria challenge.ResultsGO enrichment analysis identified significant (raw p-value < 0.05) GO terms for all three contrasts included in the analysis. Some of the GO terms linked to, generally, primary immune responses or secondary immune responses indicating the GO enrichment analysis is a useful approach to analyze microarray data. The comparisons of GO enrichment results using chicken gene information and chicken-human orthologous gene information showed more refined GO terms related to immune responses when using chicken-human orthologous gene information, this suggests that using chicken-human orthologous gene information has higher power to detect significant GO terms with more refined functionality. Furthermore, three chromosome regions were identified to be significantly up-regulated in contrast MM8-PM8 (q-value < 0.01).ConclusionOverall, this paper describes a practical approach to analyze microarray data in farm animals where the genome information is still incomplete. For farm animals, such as chicken, with currently limited gene annotation, borrowing gene annotation information from orthologous genes in well-annotated species, such as human, will help improve the pathway analysis results substantially. Furthermore, LAP analysis approach is a relatively new and very useful way to be applied in microarray analysis.

Dataset Information

MeSH ORA framework: R/Bioconductor packages to support MeSH over-representation analysis.

Background

Results

Conclusions

Publications

MeSH ORA framework: R/Bioconductor packages to support MeSH over-representation analysis.

Similar Datasets

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