Dataset Information

Expression-based segmentation of the Drosophila genome.

ABSTRACT: It is generally accepted that gene order in eukaryotes is nonrandom, with adjacent genes often sharing expression patterns across tissues, and that this organization may be important for gene regulation. Here we describe a novel method, based on an explicit probability model instead of correlation analysis, for identifying coordinately expressed gene clusters ('coexpression segments'), apply it to Drosophila melanogaster, and look for epigenetic associations using publicly available data.We find that two-thirds of Drosophila genes fall into multigenic coexpression segments, and that such segments are of two main types, housekeeping and tissue-restricted. Consistent with correlation-based studies, we find that adjacent genes within the same segment tend to be physically closer to each other than to the adjacent genes in different segments, and that tissue-restricted segments are enriched for testis-expressed genes. Our segmentation pattern correlates with Hi-C based physical interaction domains, but segments are generally much smaller than domains. Intersegment regions (including those which do not correspond to physical domain boundaries) are enriched for insulator binding sites.We describe a novel approach for identifying coexpression clusters that does not require arbitrary cutoff values or heuristics, and find that coexpression of adjacent genes is widespread in the Drosophila genome. Coexpression segments appear to reflect a level of regulatory organization related to, but below that of physical interaction domains, and depending in part on insulator binding.

SUBMITTER: Rubin AF

PROVIDER: S-EPMC3909303 | biostudies-literature | 2013 Nov

REPOSITORIES: biostudies-literature

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Expression-based segmentation of the Drosophila genome.

Rubin Alan F AF Green Phil P

BMC genomics 20131120

<h4>Background</h4>It is generally accepted that gene order in eukaryotes is nonrandom, with adjacent genes often sharing expression patterns across tissues, and that this organization may be important for gene regulation. Here we describe a novel method, based on an explicit probability model instead of correlation analysis, for identifying coordinately expressed gene clusters ('coexpression segments'), apply it to Drosophila melanogaster, and look for epigenetic associations using publicly ava ...[more]

PMID: 24256206

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Project description:The segmentation gene network of Drosophila consists of maternal and zygotic factors that generate, by transcriptional (cross-) regulation, expression patterns of increasing complexity along the anterior-posterior axis of the embryo. Using known binding site information for maternal and zygotic gap transcription factors, the computer algorithm Ahab recovers known segmentation control elements (modules) with excellent success and predicts many novel modules within the network and genome-wide. We show that novel module predictions are highly enriched in the network and typically clustered proximal to the promoter, not only upstream, but also in intronic space and downstream. When placed upstream of a reporter gene, they consistently drive patterned blastoderm expression, in most cases faithfully producing one or more pattern elements of the endogenous gene. Moreover, we demonstrate for the entire set of known and newly validated modules that Ahab's prediction of binding sites correlates well with the expression patterns produced by the modules, revealing basic rules governing their composition. Specifically, we show that maternal factors consistently act as activators and that gap factors act as repressors, except for the bimodal factor Hunchback. Our data suggest a simple context-dependent rule for its switch from repressive to activating function. Overall, the composition of modules appears well fitted to the spatiotemporal distribution of their positive and negative input factors. Finally, by comparing Ahab predictions with different categories of transcription factor input, we confirm the global regulatory structure of the segmentation gene network, but find odd skipped behaving like a primary pair-rule gene. The study expands our knowledge of the segmentation gene network by increasing the number of experimentally tested modules by 50%. For the first time, the entire set of validated modules is analyzed for binding site composition under a uniform set of criteria, permitting the definition of basic composition rules. The study demonstrates that computational methods are a powerful complement to experimental approaches in the analysis of transcription networks.

Dataset Information

Expression-based segmentation of the Drosophila genome.

Publications

Expression-based segmentation of the Drosophila genome.

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