Journal Article

Markov model plus <i>k</i>-word distributions: a synergy that produces novel statistical measures for sequence comparison

Qi Dai, Yanchun Yang and Tianming Wang

in Bioinformatics

Volume 24, issue 20, pages 2296-2302
Published in print October 2008 | ISSN: 1367-4803
Published online August 2008 | e-ISSN: 1460-2059 | DOI:
Markov model plus k-word distributions: a synergy that produces novel statistical measures for sequence comparison

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Motivation: Many proposed statistical measures can efficiently compare biological sequences to further infer their structures, functions and evolutionary information. They are related in spirit because all the ideas for sequence comparison try to use the information on the k-word distributions, Markov model or both. Motivated by adding k-word distributions to Markov model directly, we investigated two novel statistical measures for sequence comparison, called wre.k.r and S2.k.r.

Results: The proposed measures were tested by similarity search, evaluation on functionally related regulatory sequences and phylogenetic analysis. This offers the systematic and quantitative experimental assessment of our measures. Moreover, we compared our achievements with these based on alignment or alignment-free. We grouped our experiments into two sets. The first one, performed via ROC (receiver operating curve) analysis, aims at assessing the intrinsic ability of our statistical measures to search for similar sequences from a database and discriminate functionally related regulatory sequences from unrelated sequences. The second one aims at assessing how well our statistical measure is used for phylogenetic analysis. The experimental assessment demonstrates that our similarity measures intending to incorporate k-word distributions into Markov model are more efficient.

Availability: The software, data and supplement material are freely available at


Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  5082 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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