Affiliations: Université de Lyon, CNRS, INSA-Lyon, LIRIS,
UMR5205, F-69621, Villeurbanne, France | Université Lyon 1, Centre de
Génétique Moléculaire et Cellulaire, UMR5534, F-69622,
Villeurbanne, France
Abstract: There is a critical need for new and efficient computational methods
aimed at discovering putative transcription factor binding sites (TFBSs) in
promoter sequences. Among the existing methods, two families can be
distinguished: statistical or stochastic approaches, and combinatorial
approaches. Here we focus on a complete approach incorporating a combinatorial
exhaustive motif extraction, together with a statistical Twilight Zone
Indicator (TZI), in two datasets: a positive set and a negative one, which
represents the result of a classical differential expression experiment. Our
approach relies on the existence of prior biological information in the form of
two sets of promoters of differentially expressed genes. We describe the
complete procedure used for extracting either exact or degenerated motifs,
ranking these motifs, and finding their known related TFBSs. We exemplify this
approach using two different sets of promoters. The first set consists in
promoters of genes either repressed or not by the transforming form of the
v-erbA oncogene. The second set consists in genes the expression of which
varies between self-renewing and differentiating progenitors. The biological
meaning of the found TFBSs is discussed and, for one TF, its biological
involvement is demonstrated. This study therefore illustrates the power of
using relevant biological information, in the form of a set of differentially
expressed genes that is a classical outcome in most of transcriptomics studies.
This allows to severely reduce the search space and to design an adapted
statistical indicator. Taken together, this allows the biologist to concentrate
on a small number of putatively interesting TFs.
