Discovering Motifs in Ranked Lists of DNA Sequences

By Eran Eden, Doron Lipson, Sivan Yogev, Zohar Yakhini. PLoS Comput. Biol., 2007

740 citations recorded by the source corpus.

Computational methods for discovery of sequence elements that are enriched in a target set compared with a background set are fundamental in molecular biology research. One example is the discovery of transcription factor binding motifs that are inferred from ChIP–chip (chromatin immuno-precipitation on a microarray) measurements. Several major challenges in sequence motif discovery still require consideration: (i) the need for a principled approach to partitioning the data into target and background sets; (ii) the lack of rigorous models and of an exact p-value for measuring motif enrichment; (iii) the need for an appropriate framework for accounting for motif multiplicity; (iv) the tendency, in many of the existing methods, to report presumably significant motifs even when applied to randomly generated data. In this paper we present a statistical framework for discovering enriched sequence elements in ranked lists that resolves these four issues. We demonstrate the implementation of this framework in a software application, termed DRIM (discovery of rank imbalanced motifs), which identifies sequence motifs in lists of ranked DNA sequences. We applied DRIM to ChIP–chip and CpG methylation data and obtained the following results. (i) Identification of 50 novel putative transcription factor (TF) binding sites in yeast ChIP–chip data. The biological function of some of them was further investigated to gain new insights on transcription regulation networks in yeast. For example, our discoveries enable the elucidation of the network of the TF ARO80. Another finding concerns a systematic TF binding enhancement to sequences containing CA repeats. (ii) Discovery of novel motifs in human cancer CpG methylation data. Remarkably, most of these motifs are similar to DNA sequence elements bound by the Polycomb complex that promotes histone methylation. Our findings thus support a model in which histone methylation and CpG methylation are mechanistically linked. Overall, we demonstrate that the statistical framework embodied in the DRIM software tool is highly effective for identifying regulatory sequence elements in a variety of applications ranging from expression and ChIP–chip to CpG methylation data. DRIM is publicly available at http://bioinfo.cs.technion.ac.il/drim.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/bfb71182ca30d8385fd1473759e6df230077fcee

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Citations in the corpus

RNA-methylation-dependent RNA processing controls the speed of the circadian clock. (2013, positive): “GO enrichment analysis was carried out with single gene IDs using GOrilla (Eden et al., 2007; Eden et al., 2009), either using a single ranked list of genes or using two unranked lists (target and background).”
Monocyte-derived alveolar macrophages drive lung fibrosis and persist in the lung over the life span (2017, methodology): “Gene ontologies were evaluated using GOrilla (Eden et al., 2007, 2009).”
Exome and whole genome sequencing of esophageal adenocarcinoma identifies recurrent driver events and mutational complexity (2013, methodology): “To explore the functional impact of the mutations, we performed unbiased, GO-term enrichment in the overall ranked MutSig list using the 8,356 genes with at least one non-silent mutation 56,57 .”