A graph-theoretic modeling on GO space for biological interpretation of gene clusters

Abstract

We present a novel methodology for biological interpretation of gene clusters. Our graph theoretic algorithm extracts common biological attributes of the genes within a cluster or a group of interest through the modified structure of gene ontology (GO) called GO tree. After genes are annotated with GO terms, the hierarchical nature of GO terms is used to find the representative biological meanings of the gene clusters. In addition, the biological significance of gene clusters can be assessed quantitatively by defining a distance function on the GO tree. Our approach has a complementary meaning to many statistical clustering techniques; we can see clustering problems from a different viewpoint by use of biological ontology. We applied this algorithm to the well-known data set and successfully obtained the biological features of the gene clusters with the quantitative biological assessment of clustering quality through GO Biological Process.

Integrative Visual Data Mining of Biomedical Data: Investigating Cases in Chronic Fatigue Syndrome and Acute Lymphoblastic Leukaemia

“…This novel methodology for biological interpretation of gene clusters utilizes the hierarchical nature of GO terms to select possible biological interpretation of the gene clusters. Similar to our approach has been proposed in (Lee et al, 2004). The BayGO developers (Vêncio et al, 2006) also went in a similar direction.…”

Integrative Analysis of Microarray Data with Gene Ontology to Select Perturbed Molecular Functions using Gene Ontology Functional Code

“…However, GO DAG is difficult to be visualized as a directed tree since a GO term may have more than one parent by providing multiple paths from the root. Thus, we have adopted the definition of GO code (Lee et al, 2004;Fig. 1), in which GO terms can be efficiently handled in a tree structure by defining one or more GO code(s) to each GO term, to visualize the hierarchical classification of GO terms as a network. That is, a GO code is assigned to a GO term in each location of the classification of gene ontology.…”

“…To construct an ordered GO tree for the purpose of global visualization, GO terms need to be distinguished from one another if they are occurred in different locations on the hierarchical classification of gene ontology. Based on the biological point of view, Lee et al (2004) justified that what is more important is not a GO term itself, but which path the GO terms takes from the root in the gene ontology. It means that each location of a GO term could be considered distinct if a distinct path leads to it from the root of gene ontology.…”

“…Note the N represents the number of probes belong to each GFC code, (DEG) represents the average of log-fold change of probes for each GFC code, and Z-transformed (DEG) represents the average of log-fold change of probes with z-transformation for each GFC code. Lee et al (2004). Note that a unique GO code is assigned to a GO term in each location of the classification of gene ontology.…”

Gene-based and semantic structure of the Gene Ontology as a complex network

“…Indeed, the idea of searching for communities of GO terms is not new. However, one usually looks for communities of the semantic GO network only [7][8][9][10][11][12][13][14]. Our approach is different.…”