A collection of bioconductor methods to visualize gene-list annotations

被引：42

作者：

Feng G. ^{[1
]}

Du P. ^{[1
]}

Krett N.L. ^{[1
]}

Tessel M. ^{[1
]}

Rosen S. ^{[1
]}

Kibbe W.A. ^{[1
]}

Lin S.M. ^{[1
]}

机构：

[1] Northwestern University Biomedical Informatics Center (NUBIC Part of the Northwestern CTSA), Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago

来源：

BMC Research Notes | / 3卷 / 1期

基金：

美国国家卫生研究院;

关键词：

Gene Ontology; Visualization Method; Myeloma Cell Line; Cross Tabulation; Hypergeometic Test;

D O I：

10.1186/1756-0500-3-10

中图分类号：

学科分类号：

摘要：

Background. Gene-list annotations are critical for researchers to explore the complex relationships between genes and functionalities. Currently, the annotations of a gene list are usually summarized by a table or a barplot. As such, potentially biologically important complexities such as one gene belonging to multiple annotation categories are difficult to extract. We have devised explicit and efficient visualization methods that provide intuitive methods for interrogating the intrinsic connections between biological categories and genes. Findings. We have constructed a data model and now present two novel methods in a Bioconductor package, "GeneAnswers", to simultaneously visualize genes, concepts (a.k.a. annotation categories), and concept-gene connections (a.k.a. annotations): the "Concept-and-Gene Network" and the "Concept-and-Gene Cross Tabulation". These methods have been tested and validated with microarray-derived gene lists. Conclusions. These new visualization methods can effectively present annotations using Gene Ontology, Disease Ontology, or any other user-defined gene annotations that have been pre-associated with an organism's genome by human curation, automated pipelines, or a combination of the two. The gene-annotation data model and associated methods are available in the Bioconductor package called "GeneAnswers " described in this publication. © 2010 Lin et al.; licensee BioMed Central Ltd.

引用

共 3 条

[1]

Ashburner M., Ball C.A., Blake J.A., Et al., Gene ontology: Tool for the unification of biology. the Gene Ontology Consortium, Nat Genet, 25, pp. 25-29, (2000)

[2]

Du P., Feng G., Flatow J., Et al., From disease ontology to disease-ontology lite: Statistical methods to adapt a general-purpose ontology for the test of gene-ontology associations, Bioinfomatics, 25, pp. 963-968, (2009)

[3]

Adams W.T., Skopek T.R., Statistical test for the comparison of samples from mutational spectra, J Mol Biol, 194, pp. 391-396, (1987)

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