Predicting tissue-specific enhancers in the human genome

Abstract

Determining how transcriptional regulatory signals are encoded in vertebrate genomes is essential for understanding the origins of multicellular complexity; yet the genetic code of vertebrate gene regulation remains poorly understood. In an attempt to elucidate this code, we synergistically combined genome-wide gene-expression profiling, vertebrate genome comparisons, and transcription factor binding-site analysis to define sequence signatures characteristic of candidate tissue-specific enhancers in the human genome. We applied this strategy to microarray-based gene expression profiles from 79 human tissues and identified 7187 candidate enhancers that defined their flanking gene expression, the majority of which were located outside of known promoters. We cross-validated this method for its ability to de novo predict tissue-specific gene expression and confirmed its reliability in 57 of the 79 available human tissues, with an average precision in enhancer recognition ranging from 32% to 63% and a sensitivity of 47%. We used the sequence signatures identified by this approach to successfully assign tissue-specific predictions to ∼328,000 human-mouse conserved noncoding elements in the human genome. By overlapping these genome-wide predictions with a data set of enhancers validated in vivo, in transgenic mice, we were able to confirm our results with a 28% sensitivity and 50% precision. These results indicate the power of combining complementary genomic data sets as an initial computational foray into a global view of tissue-specific gene regulation in vertebrates.

Promoter architectures and developmental gene regulation

“…They are characterised by clusters of binding sites for many different TFs and chromatin regulators [109,110]. Transcriptional activation by enhancers is temporally and spatially restricted and produces highly specific expression patterns during development [111].…”

Genomic regulatory blocks underlie extensive microsynteny conservation in insects

“…Many HCNEs have been characterized as long-range enhancers, first in studies of individual genes (Gottgens et al 2000;Sumiyama and Ruddle 2003;Kimura-Yoshida et al 2004;Milewski et al 2004), followed by systematic studies in zebrafish, Xenopus, and mouse (de la Calle-Mustienes et al 2005;Shin et al 2005;Woolfe et al 2005;Pennacchio et al 2006). Genome-wide analyses of HCNE sequences have detected several overrepresented motifs that are believed to be associated with context-specific enhancer activity (Bailey et al 2006;Pennacchio et al 2007). …”

Identifying regulatory elements in eukaryotic genomes

“…If the identity of TFs active in the cell type of interest and their motifs is known, the predictive power of the methods increases for that cell type [144][145][146][147][148][149][150]. In a complementary approach, the loci of genes with a similar function can be searched for common TF binding sites [151][152][153][154]. In such approaches, TFs specific to that function can also be learned.…”

“…Drosophila developmental genes; human muscle data HexDiff [168] The frequency of all hexamers is computed for known CRMs and a set of control sequences; hexamers which have a higher frequency in CRMs are chosen and used to build a linear model which can be used to scan and score new DNA windows Drosophila developmental data CMA [153] Upstream regions of co-regulated genes are modelled as a combination of one of more composite modules, each of which contains one TF binding site or a pair (from a library of PWMs) constrained by a spacer length distribution and orientation Human T-cell data; yeast cell-cycle data EI [152], DiRE [178] A linear model based on motifs from a library of TFs is learned, which selects combinations of motifs in conserved regions of loci of coexpressed genes, while simultaneously learning regions most likely to be CRMs…”