Title |
MeSH ORA framework: R/Bioconductor packages to support MeSH over-representation analysis
|
---|---|
Published in |
BMC Bioinformatics, February 2015
|
DOI | 10.1186/s12859-015-0453-z |
Pubmed ID | |
Authors |
Koki Tsuyuzaki, Gota Morota, Manabu Ishii, Takeru Nakazato, Satoru Miyazaki, Itoshi Nikaido |
Abstract |
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. 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. 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. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Japan | 2 | 13% |
Germany | 1 | 6% |
United States | 1 | 6% |
Unknown | 12 | 75% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 8 | 50% |
Scientists | 8 | 50% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 76 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 15 | 20% |
Student > Ph. D. Student | 13 | 17% |
Student > Doctoral Student | 8 | 11% |
Student > Bachelor | 7 | 9% |
Student > Master | 7 | 9% |
Other | 11 | 14% |
Unknown | 15 | 20% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 23 | 30% |
Biochemistry, Genetics and Molecular Biology | 16 | 21% |
Computer Science | 8 | 11% |
Veterinary Science and Veterinary Medicine | 5 | 7% |
Engineering | 2 | 3% |
Other | 4 | 5% |
Unknown | 18 | 24% |