An increasing body of literature shows that genomes of eukaryotes can contain
clusters of functionally related genes. Most approaches to identify gene clusters utilize
microarray data or metabolic pathway databases to find groups of genes on chromo-
somes that are linked by common attributes. A generalized method that can find
gene clusters, regardless of the mechanism of origin, would provide researchers with
an unbiased method for finding clusters and studying the evolutionary forces that
give rise to them.
I present a basis of algorithm to identify gene clusters in eukaryotic genomes
that utilizes functional categories defined in graph-based vocabularies such as the
Gene Ontology (GO). Clusters identified in this manner need only have a common
function and are not constrained by gene expression or other properties. I tested the
algorithm by analyzing genomes of a representative set of species. I identified species
specific variation in percentage of clustered genes as well as in properties of gene
clusters, including size distribution and functional annotation. These properties may
be diagnostic of the evolutionary forces that lead to the formation of gene clusters.
The approach finds all gene clusters in the data set and ranks them by their likelihood
of occurrence by chance. The method successfully identified clusters.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-1079 |
| Date | 15 May 2009 |
| Creators | Yi, Gang Man |
| Contributors | Sze, Sing-Hoi, Thon, Michael |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Thesis, text |
| Format | electronic, application/pdf, born digital |
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