Research article

An analysis of the positional distribution of DNA motifs in promoter regions and its biological relevance

Ana C Casimiro¹ , Susana Vinga^1,2 , Ana T Freitas¹ and Arlindo L Oliveira¹

¹  INESC-ID/IST, Rua Alves Redol, 9 1000-029 Lisboa, Portugal

²  FCM/UNL, Campo dos Mártires da Pátria 130, 1169-056 Lisboa, Portugal

author email corresponding author email

BMC Bioinformatics 2008, 9:89doi:10.1186/1471-2105-9-89

Published:	7 February 2008

Abstract

Background

Motif finding algorithms have developed in their ability to use computationally efficient methods to detect patterns in biological sequences. However the posterior classification of the output still suffers from some limitations, which makes it difficult to assess the biological significance of the motifs found. Previous work has highlighted the existence of positional bias of motifs in the DNA sequences, which might indicate not only that the pattern is important, but also provide hints of the positions where these patterns occur preferentially.

Results

We propose to integrate position uniformity tests and over-representation tests to improve the accuracy of the classification of motifs. Using artificial data, we have compared three different statistical tests (Chi-Square, Kolmogorov-Smirnov and a Chi-Square bootstrap) to assess whether a given motif occurs uniformly in the promoter region of a gene. Using the test that performed better in this dataset, we proceeded to study the positional distribution of several well known cis-regulatory elements, in the promoter sequences of different organisms (S. cerevisiae, H. sapiens, D. melanogaster, E. coli and several Dicotyledons plants). The results show that position conservation is relevant for the transcriptional machinery.

Conclusion

We conclude that many biologically relevant motifs appear heterogeneously distributed in the promoter region of genes, and therefore, that non-uniformity is a good indicator of biological relevance and can be used to complement over-representation tests commonly used. In this article we present the results obtained for the S. cerevisiae data sets.