Learning "graph-mer" motifs that predict gene expression trajectories in development.

ABSTRACT: A key problem in understanding transcriptional regulatory networks is deciphering what cis regulatory logic is encoded in gene promoter sequences and how this sequence information maps to expression. A typical computational approach to this problem involves clustering genes by their expression profiles and then searching for overrepresented motifs in the promoter sequences of genes in a cluster. However, genes with similar expression profiles may be controlled by distinct regulatory programs. Moreover, if many gene expression profiles in a data set are highly correlated, as in the case of whole organism developmental time series, it may be difficult to resolve fine-grained clusters in the first place. We present a predictive framework for modeling the natural flow of information, from prom

SUBMITTER: Li X 

PROVIDER: S-EPMC2861633 | biostudies-literature | 2010 Apr

REPOSITORIES: biostudies-literature