title: Optimal word sizes for dissimilarity measures and estimation of the degree of dissimilarity between DNA sequences.
abstract: BACKGROUND
  Several measures of DNA sequence dissimilarity have been developed. The purpose of this paper is 3-fold. Firstly, we compare the performance of several word-based or alignment-based methods. Secondly, we give a general guideline for choosing the window size and determining the optimal word sizes for several word-based measures at different window sizes. Thirdly, we use a large-scale simulation method to simulate data from the distribution of SK-LD (symmetric Kullback-Leibler discrepancy). These simulated data can be used to estimate the degree of dissimilarity beta between any pair of DNA sequences.
  
  
  RESULTS
  Our study shows (1) for whole sequence similiarity/dissimilarity identification the window size taken should be as large as possible, but probably not >3000, as restricted by CPU time in practice, (2) for each measure the optimal word size increases with window size, (3) when the optimal word size is used, SK-LD performance is superior in both simulation and real data analysis, (4) the estimate beta of beta based on SK-LD can be used to filter out quickly a large number of dissimilar sequences and speed alignment-based database search for similar sequences and (5) beta is also applicable in local similarity comparison situations. For example, it can help in selecting oligo probes with high specificity and, therefore, has potential in probe design for microarrays.
  
  
  BACKGROUND
  The algorithm SK-LD, estimate beta and simulation software are implemented in MATLAB code, and are available at http://www.stat.ncku.edu.tw/tjwu
authors.affiliation: Department of Statistics, National Cheng-Kung University, Tainan, Taiwan. tjwu@stat.ncku.edu.tw
authors.forename: Tiee-Jian
authors.initials: TJ
authors.lastname: Wu
authors.forename: Ying-Hsueh
authors.initials: YH
authors.lastname: Huang
authors.forename: Lung-An
authors.initials: LA
authors.lastname: Li
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D003201:Computers; D004247:DNA / Q000737:chemistry; D030541:Databases, Genetic; D030562:Databases, Protein; D004926:Escherichia coli / Q000235:genetics; D005798:Genes, Bacterial; D016678:Genome; D006801:Humans; D008071:Lipoprotein Lipase / Q000235:genetics; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis; D015345:Oligonucleotide Probes / Q000737:chemistry; D016366:Open Reading Frames; D010363:Pattern Recognition, Automated; D010802:Phylogeny; D045473:Severe acute respiratory syndrome-related coronavirus / Q000235:genetics; D017422:Sequence Analysis, DNA / Q000379:methods*; D012763:Shigella flexneri / Q000235:genetics; D012984:Software; D013045:Species Specificity
chemical_list: D015345:Oligonucleotide Probes; D004247:DNA; D008071:Lipoprotein Lipase