Feng Gang, Du Pan, Krett Nancy L, Tessel Michael, Rosen Steven, Kibbe Warren A, Lin Simon M
Northwestern University Biomedical Informatics Center (NUBIC, part of the Northwestern CTSA) and The Robert H, Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL 60611, USA.
BMC Res Notes. 2010 Jan 19;3:10. doi: 10.1186/1756-0500-3-10.
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.
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.
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.
基因列表注释对于研究人员探索基因与功能之间的复杂关系至关重要。目前,基因列表的注释通常通过表格或柱状图进行总结。因此,诸如一个基因属于多个注释类别的潜在生物学重要复杂性难以提取。我们设计了明确且高效的可视化方法,为探究生物学类别与基因之间的内在联系提供了直观的方法。
我们构建了一个数据模型,现在在一个名为“GeneAnswers”的生物导体包中展示两种新方法,以同时可视化基因、概念(即注释类别)和概念 - 基因联系(即注释):“概念与基因网络”和“概念与基因交叉制表”。这些方法已通过源自微阵列的基因列表进行了测试和验证。
这些新的可视化方法可以使用基因本体论、疾病本体论或任何其他通过人工策划、自动化流程或两者结合预先与生物体基因组相关联的用户定义基因注释有效地呈现注释。基因注释数据模型及相关方法可在本出版物中描述的名为“GeneAnswers ”的生物导体包中获取。