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Abstract:
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Motivation: The transcription of a gene is largely determined
by short sequence motifs that serve as binding sites for
transcription factors. Recent findings suggest direct relationships
between the motifs and gene expression levels. In this
work, we present a method for identifying regulatory motifs.
Our method makes use of tree-based techniques for recovering
the relationships between motifs and gene expression
levels.
Results: We treat regulatory motifs and gene expression
levels as predictor variables and responses, respectively, and
use a regression tree model to identify the structural relationships
between them. The regression tree methodology is
extended to handle responses from multiple experiments by
modifying the split function. The significance of regulatory elements
is determined by analyzing tree structures and using a
variable importance measure. When applied to two data sets
of the yeast Saccharomyces cerevisiae, the method successfully
identifies most of the regulatory motifs that are known to
control gene transcription under the given experimental conditions,
and suggests several new putative motifs. Analysis of
the tree structures also reconfirms several pairs of motifs that
are known to regulate gene transcription in combination. |