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Discovery of possible gene relationships through the application of self-organizing maps to DNA microarray databases.

Chavez-Alvarez R, Chavoya A, Mendez-Vazquez A - PLoS ONE (2014)

Bottom Line: In this paper, we propose the use of one of such techniques--an unsupervised artificial neural network called a Self-Organizing Map (SOM)-which has been successfully applied to processes involving very noisy signals, classifying and organizing them, and assisting in the discovery of behavior patterns without requiring prior knowledge about the process under analysis.This result suggested that some of these genes might be biologically related or might have a regulatory relationship, as was corroborated by comparing some of the clusters obtained with SOMs against a previously reported regulatory network that was generated using biological knowledge, such as protein-protein interactions, gene expression levels, metabolism dynamics, promoter binding, and modification, regulation and transport of proteins.The methodology described in this paper could be applied to the study of gene relationships of other biological processes in different organisms.

View Article: PubMed Central - PubMed

Affiliation: Department of Information Systems CUCEA, Universidad de Guadalajara, Zapopan, Jalisco, Mexico.

ABSTRACT
DNA microarrays and cell cycle synchronization experiments have made possible the study of the mechanisms of cell cycle regulation of Saccharomyces cerevisiae by simultaneously monitoring the expression levels of thousands of genes at specific time points. On the other hand, pattern recognition techniques can contribute to the analysis of such massive measurements, providing a model of gene expression level evolution through the cell cycle process. In this paper, we propose the use of one of such techniques--an unsupervised artificial neural network called a Self-Organizing Map (SOM)-which has been successfully applied to processes involving very noisy signals, classifying and organizing them, and assisting in the discovery of behavior patterns without requiring prior knowledge about the process under analysis. As a test bed for the use of SOMs in finding possible relationships among genes and their possible contribution in some biological processes, we selected 282 S. cerevisiae genes that have been shown through biological experiments to have an activity during the cell cycle. The expression level of these genes was analyzed in five of the most cited time series DNA microarray databases used in the study of the cell cycle of this organism. With the use of SOM, it was possible to find clusters of genes with similar behavior in the five databases along two cell cycles. This result suggested that some of these genes might be biologically related or might have a regulatory relationship, as was corroborated by comparing some of the clusters obtained with SOMs against a previously reported regulatory network that was generated using biological knowledge, such as protein-protein interactions, gene expression levels, metabolism dynamics, promoter binding, and modification, regulation and transport of proteins. The methodology described in this paper could be applied to the study of gene relationships of other biological processes in different organisms.

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Related in: MedlinePlus

Genes clusters consistently located close to each other on the maps from the five databases.Some of these genes regulate each other either directly or indirectly according to the regulatory network reported by Alberghina et
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pone-0093233-g006: Genes clusters consistently located close to each other on the maps from the five databases.Some of these genes regulate each other either directly or indirectly according to the regulatory network reported by Alberghina et

Mentions: Other genes whose corresponding neurons are consistently located close to each other on the maps were found, such as ace2, clb1, clb2, mob1, and swi5, as well as another group formed by cln1, cln2, swe1, and yox1 (see Figure 6). According to the gene regulatory network published by Alberghina et al. [22], some of these genes regulate each other directly; for instance, clb1 regulates clb2, ace2 regulates both swi5 and clb2, and swe1 regulates yox1. Other genes have a reported indirect regulatory relationship; for example ace2 regulates mob1 through clb3, and cln1 and swe1 are regulated by cdc28[22]. We also found other pairs of genes that are consistently clustered very closely on the maps and which do not have an evident regulatory relationship in the reported network, such as mcd1 and pol30, cdc5 and myo1, and pcl9 and sic1 –except in the case of the cdc15 database, which has no data for pcl9. The consistent proximity of the neurons corresponding to these genes on the maps suggests that they may have a more direct regulatory relationship not yet discovered by biological methods.


Discovery of possible gene relationships through the application of self-organizing maps to DNA microarray databases.

Chavez-Alvarez R, Chavoya A, Mendez-Vazquez A - PLoS ONE (2014)

Genes clusters consistently located close to each other on the maps from the five databases.Some of these genes regulate each other either directly or indirectly according to the regulatory network reported by Alberghina et
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3974722&req=5

pone-0093233-g006: Genes clusters consistently located close to each other on the maps from the five databases.Some of these genes regulate each other either directly or indirectly according to the regulatory network reported by Alberghina et
Mentions: Other genes whose corresponding neurons are consistently located close to each other on the maps were found, such as ace2, clb1, clb2, mob1, and swi5, as well as another group formed by cln1, cln2, swe1, and yox1 (see Figure 6). According to the gene regulatory network published by Alberghina et al. [22], some of these genes regulate each other directly; for instance, clb1 regulates clb2, ace2 regulates both swi5 and clb2, and swe1 regulates yox1. Other genes have a reported indirect regulatory relationship; for example ace2 regulates mob1 through clb3, and cln1 and swe1 are regulated by cdc28[22]. We also found other pairs of genes that are consistently clustered very closely on the maps and which do not have an evident regulatory relationship in the reported network, such as mcd1 and pol30, cdc5 and myo1, and pcl9 and sic1 –except in the case of the cdc15 database, which has no data for pcl9. The consistent proximity of the neurons corresponding to these genes on the maps suggests that they may have a more direct regulatory relationship not yet discovered by biological methods.

Bottom Line: In this paper, we propose the use of one of such techniques--an unsupervised artificial neural network called a Self-Organizing Map (SOM)-which has been successfully applied to processes involving very noisy signals, classifying and organizing them, and assisting in the discovery of behavior patterns without requiring prior knowledge about the process under analysis.This result suggested that some of these genes might be biologically related or might have a regulatory relationship, as was corroborated by comparing some of the clusters obtained with SOMs against a previously reported regulatory network that was generated using biological knowledge, such as protein-protein interactions, gene expression levels, metabolism dynamics, promoter binding, and modification, regulation and transport of proteins.The methodology described in this paper could be applied to the study of gene relationships of other biological processes in different organisms.

View Article: PubMed Central - PubMed

Affiliation: Department of Information Systems CUCEA, Universidad de Guadalajara, Zapopan, Jalisco, Mexico.

ABSTRACT
DNA microarrays and cell cycle synchronization experiments have made possible the study of the mechanisms of cell cycle regulation of Saccharomyces cerevisiae by simultaneously monitoring the expression levels of thousands of genes at specific time points. On the other hand, pattern recognition techniques can contribute to the analysis of such massive measurements, providing a model of gene expression level evolution through the cell cycle process. In this paper, we propose the use of one of such techniques--an unsupervised artificial neural network called a Self-Organizing Map (SOM)-which has been successfully applied to processes involving very noisy signals, classifying and organizing them, and assisting in the discovery of behavior patterns without requiring prior knowledge about the process under analysis. As a test bed for the use of SOMs in finding possible relationships among genes and their possible contribution in some biological processes, we selected 282 S. cerevisiae genes that have been shown through biological experiments to have an activity during the cell cycle. The expression level of these genes was analyzed in five of the most cited time series DNA microarray databases used in the study of the cell cycle of this organism. With the use of SOM, it was possible to find clusters of genes with similar behavior in the five databases along two cell cycles. This result suggested that some of these genes might be biologically related or might have a regulatory relationship, as was corroborated by comparing some of the clusters obtained with SOMs against a previously reported regulatory network that was generated using biological knowledge, such as protein-protein interactions, gene expression levels, metabolism dynamics, promoter binding, and modification, regulation and transport of proteins. The methodology described in this paper could be applied to the study of gene relationships of other biological processes in different organisms.

Show MeSH
Related in: MedlinePlus