Dataset Information

Artificial ants deposit pheromone to search for regulatory DNA elements.

ABSTRACT:

Background

Identification of transcription-factor binding motifs (DNA sequences) can be formulated as a combinatorial problem, where an efficient algorithm is indispensable to predict the role of multiple binding motifs. An ant algorithm is a biology-inspired computational technique, through which a combinatorial problem is solved by mimicking the behavior of social insects such as ants. We developed a unique version of ant algorithms to select a set of binding motifs by considering a potential contribution of each of all random DNA sequences of 4- to 7-bp in length.

Results

Human chondrogenesis was used as a model system. The results revealed that the ant algorithm was able to identify biologically known binding motifs in chondrogenesis such as AP-1, NFkappaB, and sox9. Some of the predicted motifs were identical to those previously derived with the genetic algorithm. Unlike the genetic algorithm, however, the ant algorithm was able to evaluate a contribution of individual binding motifs as a spectrum of distributed information and predict core consensus motifs from a wider DNA pool.

Conclusion

The ant algorithm offers an efficient, reproducible procedure to predict a role of individual transcription-factor binding motifs using a unique definition of artificial ants.

SUBMITTER: Liu Y

PROVIDER: S-EPMC1586019 | biostudies-literature | 2006 Aug

REPOSITORIES: biostudies-literature

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Publications

Artificial ants deposit pheromone to search for regulatory DNA elements.

Liu Yunlong Y Yokota Hiroki H

BMC genomics 20060830

<h4>Background</h4>Identification of transcription-factor binding motifs (DNA sequences) can be formulated as a combinatorial problem, where an efficient algorithm is indispensable to predict the role of multiple binding motifs. An ant algorithm is a biology-inspired computational technique, through which a combinatorial problem is solved by mimicking the behavior of social insects such as ants. We developed a unique version of ant algorithms to select a set of binding motifs by considering a po ...[more]

PMID: 16942615

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Project description:It is now established that, as compared to normal cells, the cancer cell genome has an overall inverse distribution of DNA methylation ("methylome"), i.e., predominant hypomethylation and localized hypermethylation, within "CpG islands" (CGIs). Moreover, although cancer cells have reduced methylation "fidelity" and genomic instability, accurate maintenance of aberrant methylomes that underlie malignant phenotypes remains necessary. However, the mechanism(s) of cancer methylome maintenance remains largely unknown. Here, we assessed CGI methylation patterns propagated over 1, 3, and 5 divisions of A2780 ovarian cancer cells, concurrent with exposure to the DNA cross-linking chemotherapeutic cisplatin, and observed cell generation-successive increases in total hyper- and hypo-methylated CGIs. Empirical bayesian modeling revealed five distinct modes of methylation propagation: (1) heritable (i.e., unchanged) high-methylation (1186 probe loci in CGI microarray); (2) heritable (i.e., unchanged) low-methylation (286 loci); (3) stochastic hypermethylation (i.e., progressively increased, 243 loci); (4) stochastic hypomethylation (i.e., progressively decreased, 247 loci); and (5) considerable "random" methylation (582 loci). These results support a "stochastic model" of DNA methylation equilibrium deriving from the efficiency of two distinct processes, methylation maintenance and de novo methylation. A role for cis-regulatory elements in methylation fidelity was also demonstrated by highly significant (p<2.2×10(-5)) enrichment of transcription factor binding sites in CGI probe loci showing heritably high (118 elements) and low (47 elements) methylation, and also in loci demonstrating stochastic hyper-(30 elements) and hypo-(31 elements) methylation. Notably, loci having "random" methylation heritability displayed nearly no enrichment. These results demonstrate an influence of cis-regulatory elements on the nonrandom propagation of both strictly heritable and stochastically heritable CGIs.

Dataset Information

Artificial ants deposit pheromone to search for regulatory DNA elements.

Background

Results

Conclusion

Publications

Artificial ants deposit pheromone to search for regulatory DNA elements.

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