Nature Methods
- 5, 347 - 353 (2008)
Published online: 2 March 2008; | doi:10.1038/nmeth.1188
Systematic identification of mammalian regulatory motifs' target genes and functionsJason B Warner1, 7, Anthony A Philippakis1, 3, 4, 7, Savina A Jaeger1, 7, Fangxue Sherry He1, 6, Jolinta Lin1, 5 & Martha L Bulyk1, 2, 3, 41
Division of Genetics, Department of Medicine, Harvard Medical School, Harvard Medical School New Research Building, Room 466D, 77 Ave. Louis Pasteur, Boston, Massachusetts 02115, USA. 2
Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Harvard Medical School New Research Building, Room 466D, 77 Ave. Louis Pasteur, Boston, Massachusetts 02115, USA. 3
Harvard–Massachusetts Institute of Technology (MIT) Division of Health Sciences and Technology (HST), Harvard Medical School, Harvard Medical School New Research Building, Room 466D, 77 Ave. Louis Pasteur, Boston, Massachusetts 02115, USA. 4
Committee on Higher Degrees in Biophysics, Harvard University, Cambridge, Massachusetts 02138, USA. 5
Department of Biology, MIT, 77 Massachusetts Ave., Cambridge, Massachusetts 02139. 6
Present address: Science Applications International Corporation–Frederick Inc., 1700 W. 7th St., Frederick, Maryland 21702, USA. 7
These authors contributed equally to this work.
Correspondence should be addressed to Martha L Bulyk mlbulyk@receptor.med.harvard.edu We developed an algorithm, Lever, that systematically maps metazoan DNA regulatory motifs or motif combinations to sets of genes. Lever assesses whether the motifs are enriched in cis-regulatory modules (CRMs), predicted by our PhylCRM algorithm, in the noncoding sequences surrounding the genes. Lever analysis allows unbiased inference of functional annotations to regulatory motifs and candidate CRMs. We used human myogenic differentiation as a model system to statistically assess greater than 25,000 pairings of gene sets and motifs or motif combinations. We assigned functional annotations to candidate regulatory motifs predicted previously and identified 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, toward understanding their biological roles. This approach is general and can be applied readily to any cell type, gene expression pattern or organism of interest.
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