Optimal word sizes for dissimilarity measures and estimation of the degree of dissimilarity between DNA sequences | Zendy

TieeJian Wu | Zendy; Ying-Hsueh Huang | Zendy; LungAn Li | Zendy

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Optimal word sizes for dissimilarity measures and estimation of the degree of dissimilarity between DNA sequences

Author(s) -

TieeJian Wu,

Ying-Hsueh Huang,

LungAn Li

Publication year - 2005

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/bti658

Subject(s) - word (group theory) , degree (music) , estimation , computer science , word length , dna , statistics , natural language processing , artificial intelligence , pattern recognition (psychology) , mathematics , genetics , biology , geometry , economics , physics , management , acoustics

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.

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