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Artificial ants deposit pheromone to search for regulatory DNA elements.

Liu Y, Yokota H - BMC Genomics (2006)

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Division of Biostatistics, Department of Medicine, Center for Computational Biology and Bioinformatics, Indiana University--Purdue University Indianapolis, Indianapolis, IN 46202, USA. yunliu@iupui.edu

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.

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Motif length analysis. The motif length analysis among the four models (4-, 5-, 6-, and 7-bp DNA sequences as potential transcription-factor binding motifs) with ε = 10 and δ = 0.1. (A) Pheromone spectrum for 4-bp DNA sequences. (B) Pheromone spectrum for 5-bp DNA sequences. (C) Pheromone spectrum for 6-bp DNA sequences. (D) Pheromone spectrum for 7-bp DNA sequences.
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Figure 4: Motif length analysis. The motif length analysis among the four models (4-, 5-, 6-, and 7-bp DNA sequences as potential transcription-factor binding motifs) with ε = 10 and δ = 0.1. (A) Pheromone spectrum for 4-bp DNA sequences. (B) Pheromone spectrum for 5-bp DNA sequences. (C) Pheromone spectrum for 6-bp DNA sequences. (D) Pheromone spectrum for 7-bp DNA sequences.

Mentions: Since the length of known transcription-factor binding motifs vary from 4 bp up to more than 10 bp, we examined sequence similarities among the predicted motifs ranging from 4 to 7 bp (Fig. 4). There are 136, 512, 2080, and 8192 DNA sequences in total for 4-, 5-, 6-, and 7-bp binding motifs, respectively. Interestingly, the motifs consisting of particular DNA sequences such as GCCC, CAGG, and CTGA repeatedly appeared with a high concentration of pheromone.


Artificial ants deposit pheromone to search for regulatory DNA elements.

Liu Y, Yokota H - BMC Genomics (2006)

Motif length analysis. The motif length analysis among the four models (4-, 5-, 6-, and 7-bp DNA sequences as potential transcription-factor binding motifs) with ε = 10 and δ = 0.1. (A) Pheromone spectrum for 4-bp DNA sequences. (B) Pheromone spectrum for 5-bp DNA sequences. (C) Pheromone spectrum for 6-bp DNA sequences. (D) Pheromone spectrum for 7-bp DNA sequences.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC1586019&req=5

Figure 4: Motif length analysis. The motif length analysis among the four models (4-, 5-, 6-, and 7-bp DNA sequences as potential transcription-factor binding motifs) with ε = 10 and δ = 0.1. (A) Pheromone spectrum for 4-bp DNA sequences. (B) Pheromone spectrum for 5-bp DNA sequences. (C) Pheromone spectrum for 6-bp DNA sequences. (D) Pheromone spectrum for 7-bp DNA sequences.
Mentions: Since the length of known transcription-factor binding motifs vary from 4 bp up to more than 10 bp, we examined sequence similarities among the predicted motifs ranging from 4 to 7 bp (Fig. 4). There are 136, 512, 2080, and 8192 DNA sequences in total for 4-, 5-, 6-, and 7-bp binding motifs, respectively. Interestingly, the motifs consisting of particular DNA sequences such as GCCC, CAGG, and CTGA repeatedly appeared with a high concentration of pheromone.

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Division of Biostatistics, Department of Medicine, Center for Computational Biology and Bioinformatics, Indiana University--Purdue University Indianapolis, Indianapolis, IN 46202, USA. yunliu@iupui.edu

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.

Show MeSH