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Bioinformatics 2007 23(13):i249-i255; doi:10.1093/bioinformatics/btm211
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© 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

A statistical method for alignment-free comparison of regulatory sequences

Miriam R. Kantorovitz 1,2, Gene E. Robinson 2,3 and Saurabh Sinha 1,3,*

1Department of Computer Science, 2Department of Entomology, 3Institute for Genomic Biology, University of Illinois, Urbana-Champaign, Illinois, USA

*To whom correspondence should be addressed.


  Abstract

Motivation: The similarity of two biological sequences has traditionally been assessed within the well-established framework of alignment. Here we focus on the task of identifying functional relationships between cis-regulatory sequences that are non-orthologous or greatly diverged. ‘Alignment-free’ measures of sequence similarity are required in this regime.

Results: We investigate the use of a new score for alignment-free sequence comparison, called the Formula score. It is based on comparing the frequencies of all fixed-length words in the two sequences. An important, novel feature of the score is that it is comparable across sequence pairs drawn from arbitrary background distributions. We present a method that gives quadratic improvement in the time complexity of calculating the Formula score, over the naïve method. We then evaluate the score on several tissue-specific families of cis-regulatory modules (in Drosophila and human). The new score is highly successful in discriminating functionally related regulatory sequences from unrelated sequence pairs. The performance of the Formula score is compared to five other alignment-free similarity measures, and shown to be consistently superior to all of these measures.

Availability: Our implementation of the Formula score will be made freely available as source code, upon publication of this article, at: http://veda.cs.uiuc.edu/d2z/

Contact: sinhas{at}cs.uiuc.edu

Supplementary information: Supplementary data are available at Bioinformatics online.


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