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BACKGROUND As more methods are developed to analyze RNA-profiling data, assessing their performance using control datasets becomes increasingly important. RESULTS We present a 'spike-in' experiment for Affymetrix GeneChips that provides a defined dataset of 3,860 RNA species, which we use to evaluate analysis options for identifying differentially(More)
Gene expression is regulated by transcription factors that interact with cis-regulatory elements. Predicting these elements from sequence data has proven difficult. We describe here a successful computational search for elements that direct expression in a particular temporal-spatial pattern in the Drosophila embryo, based on a single well characterized(More)
Ras signaling elicits diverse outputs, yet how Ras specificity is generated remains incompletely understood. We demonstrate that Wingless (Wg) and Decapentaplegic (Dpp) confer competence for receptor tyrosine kinase-mediated induction of a subset of Drosophila muscle and cardiac progenitors by acting both upstream of and in parallel to Ras. In addition to(More)
Convergent intercellular signals must be precisely integrated in order to elicit specific biological responses. During specification of muscle and cardiac progenitors from clusters of equivalent cells in the Drosophila embryonic mesoderm, the Ras/MAPK pathway--activated by both epidermal and fibroblast growth factor receptors--functions as an inductive(More)
A subfamily of Drosophila homeodomain (HD) transcription factors (TFs) controls the identities of individual muscle founder cells (FCs). However, the molecular mechanisms by which these TFs generate unique FC genetic programs remain unknown. To investigate this problem, we first applied genome-wide mRNA expression profiling to identify genes that are(More)
The Drosophila heart is composed of two distinct cell types, the contractile cardial cells (CCs) and the surrounding non-muscle pericardial cells (PCs), development of which is regulated by a network of conserved signaling molecules and transcription factors (TFs). Here, we used machine learning with array-based chromatin immunoprecipitation (ChIP) data and(More)
Here we used discriminative training methods to uncover the chromatin, transcription factor (TF) binding and sequence features of enhancers underlying gene expression in individual cardiac cells. We used machine learning with TF motifs and ChIP data for a core set of cardiogenic TFs and histone modifications to classify Drosophila cell-type-specific cardiac(More)
An important but largely unmet challenge in understanding the mechanisms that govern the formation of specific organs is to decipher the complex and dynamic genetic programs exhibited by the diversity of cell types within the tissue of interest. Here, we use an integrated genetic, genomic, and computational strategy to comprehensively determine the(More)
While combinatorial models of transcriptional regulation can be inferred for metazoan systems from a priori biological knowledge, validation requires extensive and time-consuming experimental work. Thus, there is a need for computational methods that can evaluate hypothesized cis regulatory codes before the difficult task of experimental verification is(More)
Contemporary high-throughput technologies permit the rapid identification of transcription factor (TF) target genes on a genome-wide scale, yet the functional significance of TFs requires knowledge of target gene expression patterns, cooperating TFs, and cis-regulatory element (CRE) structures. Here we investigated the myogenic regulatory network downstream(More)