Systematic identification of mammalian regulatory motifs' target genes and functions

Abstract

We have developed an algorithm ("Lever") that systematically maps metazoan DNA regulatory motifs or motif combinations to the sets of genes that they likely regulate. Lever accomplishes this by assessing whether the motifs are enriched within cis regulatory modules (CRMs), predicted by our "PhylCRM" algorithm, in the noncoding sequences surrounding genes in a collection of gene sets. When these gene sets correspond to Gene Ontology (GO) categories, the results of Lever analysis allow the unbiased assignment of functional annotations to the regulatory motifs and also to the candidate CRMs that comprise the genomic motif occurrences. We demonstrate these methods using human myogenic differentiation as a model system, for which we statistically assessed greater than 25,000 pairings of gene sets and motifs / motif combinations. These results allowed us to assign functional annotations to candidate regulatory motifs predicted previously, and to identify gene sets that are likely to be co-regulated via shared regulatory motifs. Lever allows moving beyond the identification of putative regulatory motifs in mammalian genomes, towards understanding their biological roles. This approach is general and can be applied readily to any cell type, gene expression pattern, or organism of interest.

Tup/Islet1 integrates time and position to specify muscle identity in Drosophila

“…sites (Philippakis et al, 2006;Warner et al, 2008) (A. Aboukhabil and M. Bulyk, personal communication). Taking into account ChiP-ChIP data, and sequence conservation during evolution, we identified a 3 kb fragment, designated below as DME (dorsal muscle enhancer), that drives lacZ expression in heart cells, the dorsal and the alary muscles ( Fig.…”

Discovery and verification of functional single nucleotide polymorphisms in regulatory genomic regions: Current and developing technologies

“…Within a given gene network, such as the p53 or NRF2 pathways, members of the pathway typically have cis-regulatory sequences within 10 kb (sometimes larger) of the transcription start site. Computational methods for the identification of cis-regulatory sequences have been successfully applied to simple organisms such as yeast and worm, and while some methods have been plagued by high false positive rates in mammals primarily because of the very large quantity of intergenic sequence present [25], many recent new bioinformatics algorithms have improved prediction [26]. These include examining evolutionarily conserved regulatory sequences in upstream sequences of orthologous genes across species [7,[27][28][29] and identifying statistically over-represented motifs in the upstream regions of genes that are co-regulated in microarray expression profiles [26,30].…”

Systematic dissection of regulatory motifs in 2000 predicted human enhancers using a massively parallel reporter assay

“…Genome-wide genetic association studies suggest that nearly 85% of disease-associated variants lie outside protein-coding regions (Hindorff et al 2009), emphasizing the importance of a systematic understanding of regulatory elements in the human genome at the nucleotide level. In recent years, the prediction of human regulatory regions has benefited tremendously from advances in highthroughput experimental (Bernstein et al 2010;Myers et al 2011), computational (Berman et al 2002;Sinha et al 2008;Warner et al 2008), and comparative (Bejerano et al 2004;Moses et al 2004;Xie et al 2005;Kheradpour et al 2007;Visel et al 2008;Lindblad-Toh et al 2011) methods, leading to a large number of putative regulatory elements (Pennacchio et al 2006;Visel et al 2009). The dissection of individual sequences and their evaluation in transient assays led to a greatly increased understanding of enhancer biology for human (Ney et al 1990;Liu et al 1992), fly (Zeng et al 1994;Kapoun and Kaufman 1995), and worm ( Jantsch-Plunger and Fire 1994).…”