Learning “graph-mer” Motifs that Predict Gene Expression Trajectories in Development

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 promoter sequence to expression, to learn cis regulatory motifs and characterize gene expression patterns in developmental time courses. We introduce a cluster-free algorithm based on a graph-regularized version of partial least squares (PLS) regression to learn sequence patterns—represented by graphs of k-mers, or “graph-mers”—that predict gene expression trajectories. Applying the approach to wildtype germline development in Caenorhabditis elegans, we found that the first and second latent PLS factors mapped to expression profiles for oocyte and sperm genes, respectively. We extracted both known and novel motifs from the graph-mers associated to these germline-specific patterns, including novel CG-rich motifs specific to oocyte genes. We found evidence supporting the functional relevance of these putative regulatory elements through analysis of positional bias, motif conservation and in situ gene expression. This study demonstrates that our regression model can learn biologically meaningful latent structure and identify potentially functional motifs from subtle developmental time course expression data.Author Summary: A major challenge in functional genomics is to decipher the gene regulatory networks operating in multi-cellular organisms, such as the nematode C. elegans. The expression level of a gene is controlled, to a great extent, by regulatory proteins called transcription factors that bind short motifs in the gene's promoter (regulatory region in the non-coding DNA). In a temporal regulatory process, for example in development, the “regulatory logic” of DNA motifs in the promoter largely determines the gene's expression trajectory, as the gene responds over time to changing concentrations of the transcription factors that control it. This study addresses the problem of learning DNA motifs that predict temporal expression profiles, using genomewide expression data from developmental time series in C. elegans. We developed a novel algorithm based on techniques from multivariate regression that sets up a correspondence between sequence patterns and expression trajectories. Sequence motifs are represented as graphs of sequence-similar k-length subsequences called “graph-mers”. By applying the method to germline development in C. elegans, we found both known and novel DNA motifs associated with oocyte and sperm genes.