Blog
About

  • Record: found
  • Abstract: found
  • Article: not found

A novel biclustering approach with iterative optimization to analyze gene expression data

Read this article at

ScienceOpenPublisherPMC
Bookmark
      There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

      Video abstract

      Video

      Abstract

      Objective

      With the dramatic increase in microarray data, biclustering has become a promising tool for gene expression analysis. Biclustering has been proven to be superior over clustering in identifying multifunctional genes and searching for co-expressed genes under a few specific conditions; that is, a subgroup of all conditions. Biclustering based on a genetic algorithm (GA) has shown better performance than greedy algorithms, but the overlap state for biclusters must be treated more systematically.

      Results

      We developed a new biclustering algorithm (binary-iterative genetic algorithm [BIGA]), based on an iterative GA, by introducing a novel, ternary-digit chromosome encoding function. BIGA searches for a set of biclusters by iterative binary divisions that allow the overlap state to be explicitly considered. In addition, the average of the Pearson’s correlation coefficient was employed to measure the relationship of genes within a bicluster, instead of the mean square residual, the popular classical index. As compared to the six existing algorithms, BIGA found highly correlated biclusters, with large gene coverage and reasonable gene overlap. The gene ontology (GO) enrichment showed that most of the biclusters are significant, with at least one GO term over represented.

      Conclusion

      BIGA is a powerful tool to analyze large amounts of gene expression data, and will facilitate the elucidation of the underlying functional mechanisms in living organisms.

      Related collections

      Most cited references 46

      • Record: found
      • Abstract: found
      • Article: not found

      Cluster analysis and display of genome-wide expression patterns.

      A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression. The output is displayed graphically, conveying the clustering and the underlying expression data simultaneously in a form intuitive for biologists. We have found in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function, and we find a similar tendency in human data. Thus patterns seen in genome-wide expression experiments can be interpreted as indications of the status of cellular processes. Also, coexpression of genes of known function with poorly characterized or novel genes may provide a simple means of gaining leads to the functions of many genes for which information is not available currently.
        Bookmark
        • Record: found
        • Abstract: not found
        • Article: not found

        Individual Comparisons by Ranking Methods

          Bookmark
          • Record: found
          • Abstract: found
          • Article: not found

          Genomic expression programs in the response of yeast cells to environmental changes.

          We explored genomic expression patterns in the yeast Saccharomyces cerevisiae responding to diverse environmental transitions. DNA microarrays were used to measure changes in transcript levels over time for almost every yeast gene, as cells responded to temperature shocks, hydrogen peroxide, the superoxide-generating drug menadione, the sulfhydryl-oxidizing agent diamide, the disulfide-reducing agent dithiothreitol, hyper- and hypo-osmotic shock, amino acid starvation, nitrogen source depletion, and progression into stationary phase. A large set of genes (approximately 900) showed a similar drastic response to almost all of these environmental changes. Additional features of the genomic responses were specialized for specific conditions. Promoter analysis and subsequent characterization of the responses of mutant strains implicated the transcription factors Yap1p, as well as Msn2p and Msn4p, in mediating specific features of the transcriptional response, while the identification of novel sequence elements provided clues to novel regulators. Physiological themes in the genomic responses to specific environmental stresses provided insights into the effects of those stresses on the cell.
            Bookmark

            Author and article information

            Affiliations
            [1 ]Department of Biological Sciences, Graduate School of Biosciences and Biotechnology, Tokyo Institute of Technology, Tokyo, Japan
            [2 ]Graduate School of Information Sciences, Tohoku University, Miyagi, Japan
            [3 ]Institute of Development, Aging and Cancer, Tohoku University, Miyagi, Japan
            [4 ]Graduate School of Information Sciences, Nagoya University, Nagoya, Japan
            Author notes
            Correspondence: Kengo Kinoshita, Laboratory of Systems Bioinformatics, Graduate School of Information Science, Tohoku University, Aoba-ku, Sendai, 980-8579, Japan, Email kengo@ 123456ecei.tohoku.ac.jp
            Journal
            Adv Appl Bioinform Chem
            Adv Appl Bioinform Chem
            Advances and Applications in Bioinformatics and Chemistry : AABC
            Dove Medical Press
            1178-6949
            2012
            07 September 2012
            : 5
            : 23-59
            23055751
            3459542
            10.2147/AABC.S32622
            aabc-5-023
            © 2012 Sutheeworapong et al, publisher and licensee Dove Medical Press Ltd.

            This is an Open Access article which permits unrestricted noncommercial use, provided the original work is properly cited.

            Categories
            Methodology
            ScienceOpen disciplines:
            Keywords:

            Comments

            Comment on this article