Discovery of Regulatory Elements by a Computational Method for Phylogenetic Footprinting

Abstract

Phylogenetic footprinting is a method for the discovery of regulatory elements in a set of orthologous regulatory regions from multiple species. It does so by identifying the best conserved motifs in those orthologous regions. We describe a computer algorithm designed specifically for this purpose, making use of the phylogenetic relationships among the sequences under study to make more accurate predictions. The program is guaranteed to report all sets of motifs with the lowest parsimony scores, calculated with respect to the phylogenetic tree relating the input species. We report the results of this algorithm on several data sets of interest. A large number of known functional binding sites are identified by our method, but we also find several highly conserved motifs for which no function is yet known.One of the great challenges currently facing biologists is to understand the varied and complex mechanisms that regulate gene expression. We focus on one important aspect of this challenge, the identification of binding sites for the factors involved in such regulation.A number of computer algorithms have been proposed for the discovery of novel regulatory elements in nucleotide sequences. Most of these try to deduce the regulatory elements by considering the regulatory regions of several (putatively) coregulated genes from a single genome. Such algorithms search for overrepresented motifs in this collection of regulatory regions, these motifs being good candidates for regulatory elements. Examples of this approach include van

Identification and phylogenetic comparison of p53 in two distinct mussel species (Mytilus)

“…Selective pressure would cause functional elements to evolve at a slower rate than that of nonfunctional sequences. Phylogenetic footprinting deduces novel regulatory elements by considering orthologous regions of a single gene from several species (Blanchette et al, 2002). We ran a series of phylogenetic footprint analyses using FootPrinter 2.1 (Blanchette et al, 2003) for a very diverse group of p53 sequences to potentially identify novel regulatory elements conserved across species.…”

Surveying phylogenetic footprints in large gene clusters: applications to Hox cluster duplications

“…This approach was successful to identify the regulatory elements in many cases, see e.g. [18,23,29,9,6] and the review [11]. In a related approach, the rVISTA tool uses pairwise alignments of orthologous regions to determine the significance of putative transcription factor binding sites found by comparison with a database of binding motifs [19].…”

Systematic Prediction of cis-Regulatory Elements in the Chlamydomonas reinhardtii Genome Using Comparative Genomics

“…multiple CREs are often co-occurring in short regions; Frith et al, 2001;Zhou and Wong, 2004;Gupta and Liu, 2005;Hu et al, 2008). Alternative methods require evolutionary conservation of motifs to further filter false-positive discoveries (Loots et al, 2000;Blanchette and Tompa, 2002;Wang and Stormo, 2003;Liu et al, 2004;Sinha et al, 2004;Elemento and Tavazoie, 2005;. The rationale is that functional motifs should be evolutionarily conserved across multiple species (Loots et al, 2000), Considering evolutionary conservation as an additional criterion indeed has been shown effective in identifying bona fide CRE motifs, whereas how to better quantify the evolutionary conservation for CRE motif finding is worth further investigation.…”

“…the overrepresentation property of motifs; Stormo and Hartzell, 1989;Lawrence et al, 1993;Bailey and Elkan, 1994). However, because of the degenerative nature of motifs, overrepresentation alone is often not enough to distinguish true motifs from random patterns formed by DNA segments (Blanchette and Tompa, 2002;Wang and Stormo, 2003). To improve the sensitivity and specificity of CRE motif prediction, dozens of motif-finding methods have been developed to exploit the cooccurrence property of motifs (i.e.…”