Warner Jason B, Philippakis Anthony A, Jaeger Savina A, He Fangxue Sherry, Lin Jolinta, Bulyk Martha L
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.
Nat Methods. 2008 Apr;5(4):347-53. doi: 10.1038/nmeth.1188. Epub 2008 Mar 2.
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.
我们开发了一种名为Lever的算法，该算法可将后生动物DNA调控基序或基序组合系统地映射到基因集。Lever评估这些基序是否在由我们的PhylCRM算法预测的顺式调控模块（CRM）中富集，这些模块位于基因周围的非编码序列中。Lever分析允许对调控基序和候选CRM进行无偏的功能注释推断。我们使用人类肌肉生成分化作为模型系统，对超过25000个基因集与基序或基序组合进行统计评估。我们为先前预测的候选调控基序赋予功能注释，并鉴定出可能通过共享调控基序共同调控的基因集。Lever使我们能够超越在哺乳动物基因组中识别假定调控基序，进而了解它们的生物学作用。这种方法具有通用性，可轻松应用于任何感兴趣的细胞类型、基因表达模式或生物体。
