|Title||Sequence complexity profiles of prokaryotic genomic sequences: a fast algorithm for calculating linguistic complexity.|
|Publication Type||Journal Article|
|Year of Publication||2002|
|Authors||Troyanskaya, OG, Arbell, O, Koren, Y, Landau, GM, Bolshoy, A|
|Date Published||2002 May|
|Keywords||Algorithms, Base Sequence, Computing Methodologies, Databases, Genetic, Decision Trees, Feasibility Studies, Genome, Haemophilus influenzae, Models, Genetic, Molecular Sequence Data, Prokaryotic Cells, Quality Control, Repetitive Sequences, Nucleic Acid, Sensitivity and Specificity, Sequence Analysis, DNA, Time Factors|
MOTIVATION: One of the major features of genomic DNA sequences, distinguishing them from texts in most spoken or artificial languages, is their high repetitiveness. Variation in the repetitiveness of genomic texts reflects the presence and density of different biologically important messages. Thus, deviation from an expected number of repeats in both directions indicates a possible presence of a biological signal. Linguistic complexity corresponds to repetitiveness of a genomic text, and potential regulatory sites may be discovered through construction of typical patterns of complexity distribution.
RESULTS: We developed software for fast calculation of linguistic sequence complexity of DNA sequences. Our program utilizes suffix trees to compute the number of subwords present in genomic sequences, thereby allowing calculation of linguistic complexity in time linear in genome size. The measure of linguistic complexity was applied to the complete genome of Haemophilus influenzae. Maps of complexity along the entire genome were obtained using sliding windows of 40, 100, and 2000 nucleotides. This approach provided an efficient way to detect simple sequence repeats in this genome. In addition, local profiles of complexity distribution around the starts of translation were constructed for 21 complete prokaryotic genomes. We hypothesize that complexity profiles correspond to evolutionary relationships between organisms. We found principal differences in profiles of the GC-rich and other (non-GC-rich) genomes. We also found characteristic differences in profiles of AT genomes, which probably reflect individual species variations in translational regulation.
AVAILABILITY: The program is available upon request from Alexander Bolshoy or at http://csweb.haifa.ac.il/library/#complex.