title: Extracting DNA words based on the sequence features: non-uniform distribution and integrity.
abstract: BACKGROUND
  DNA sequence can be viewed as an unknown language with words as its functional units. Given that most sequence alignment algorithms such as the motif discovery algorithms depend on the quality of background information about sequences, it is necessary to develop an ab initio algorithm for extracting the "words" based only on the DNA sequences.
  
  
  METHODS
  We considered that non-uniform distribution and integrity were two important features of a word, based on which we developed an ab initio algorithm to extract "DNA words" that have potential functional meaning. A Kolmogorov-Smirnov test was used for consistency test of uniform distribution of DNA sequences, and the integrity was judged by the sequence and position alignment. Two random base sequences were adopted as negative control, and an English book was used as positive control to verify our algorithm. We applied our algorithm to the genomes of Saccharomyces cerevisiae and 10 strains of Escherichia coli to show the utility of the methods.
  
  
  RESULTS
  The results provide strong evidences that the algorithm is a promising tool for ab initio building a DNA dictionary.
  
  
  CONCLUSIONS
  Our method provides a fast way for large scale screening of important DNA elements and offers potential insights into the understanding of a genome.
authors.affiliation: Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, 030001, China. china140106@126.com.
authors.forename: Zhi
authors.initials: Z
authors.lastname: Li
authors.affiliation: Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, 030001, China. cao_hong_yan@qq.com.
authors.forename: Hongyan
authors.initials: H
authors.lastname: Cao
authors.affiliation: Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, 030001, China. cui@stt.msu.edu.|Department of Statistics and Probability, Michigan State University, East Lansing, MI, 48824, USA. cui@stt.msu.edu.
authors.forename: Yuehua
authors.initials: Y
authors.lastname: Cui
authors.affiliation: Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, 030001, China. sxmuzyb@126.com.
authors.forename: Yanbo
authors.initials: Y
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D002875:Chromosomes; D019295:Computational Biology / Q000379:methods*; D004247:DNA / Q000032:analysis*; D004926:Escherichia coli / Q000235:genetics; D016680:Genome, Bacterial; D016681:Genome, Fungal; D010957:Plasmids / Q000235:genetics; D012441:Saccharomyces cerevisiae / Q000235:genetics; D016415:Sequence Alignment / Q000379:methods*; D012984:Software
chemical_list: D004247:DNA
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