title: Application of Subspace Clustering in DNA Sequence Analysis.
abstract: Identification and clustering of orthologous genes plays an important role in developing evolutionary models such as validating convergent and divergent phylogeny and predicting functional proteins in newly sequenced species of unverified nucleotide protein mappings. Here, we introduce an application of subspace clustering as applied to orthologous gene sequences and discuss the initial results. The working hypothesis is based upon the concept that genetic changes between nucleotide sequences coding for proteins among selected species and groups may lie within a union of subspaces for clusters of the orthologous groups. Estimates for the subspace dimensions were computed for a small population sample. A series of experiments was performed to cluster randomly selected sequences. The experimental design allows for both false positives and false negatives, and estimates for the statistical significance are provided. The clustering results are consistent with the main hypothesis. A simple random mutation binary tree model is used to simulate speciation events that show the interdependence of the subspace rank versus time and mutation rates. The simple mutation model is found to be largely consistent with the observed subspace clustering singular value results. Our study indicates that the subspace clustering method may be applied in orthology analysis.
authors.affiliation: 1 Department of Computer Science, Tennessee State University , Nashville, Tennessee.
authors.forename: Tim
authors.initials: T
authors.lastname: Wallace
authors.affiliation: 1 Department of Computer Science, Tennessee State University , Nashville, Tennessee.
authors.forename: Ali
authors.initials: A
authors.lastname: Sekmen
authors.affiliation: 2 Department of Biological Sciences, Tennessee State University , Nashville, Tennessee.
authors.forename: Xiaofei
authors.initials: X
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D019143:Evolution, Molecular; D049810:Genetic Speciation; D008957:Models, Genetic; D059645:Mutation Rate; D010802:Phylogeny; D017422:Sequence Analysis, DNA / Q000379:methods*; D012689:Sequence Homology, Nucleic Acid
keywords: algorithms; statistics.