Abstract
DNA microarray technology enables the simultaneous measurement of the transcript level of thousands of genes. Primary analysis can be done with basic statistical tools and cluster analysis, but effective and in depth analysis of the vast amount of transcription data requires integration with data from several heterogeneous data sources, such as upstream promoter sequences, genome-scale metabolic models, annotation databases and other experimental data. In this review, we discuss how experimental design, normalisation, heterogeneous data and mathematical modelling can enhance analysis of Saccharomyces cerevisiae whole genome transcription data. A special focus is on the quantitative aspects of normalisation and mathematical modelling approaches, since they are expected to play an increasing role in future DNA microarray analysis studies. Data analysis is exemplified with cluster analysis, and newly developed co-clustering methods, where the DNA microarray analysis is enhanced by integrating data from multiple, heterogeneous sources.
Keywords: systems biology, saccharomyces cerevisiae, dna microarray, cluster analysis, mathematical modelling, functional genomics
Current Genomics
Title: Enhancing Yeast Transcription Analysis Through Integration of Heterogeneous Data
Volume: 5 Issue: 8
Author(s): T. Grotkjaer and J. Nielsen
Affiliation:
Keywords: systems biology, saccharomyces cerevisiae, dna microarray, cluster analysis, mathematical modelling, functional genomics
Abstract: DNA microarray technology enables the simultaneous measurement of the transcript level of thousands of genes. Primary analysis can be done with basic statistical tools and cluster analysis, but effective and in depth analysis of the vast amount of transcription data requires integration with data from several heterogeneous data sources, such as upstream promoter sequences, genome-scale metabolic models, annotation databases and other experimental data. In this review, we discuss how experimental design, normalisation, heterogeneous data and mathematical modelling can enhance analysis of Saccharomyces cerevisiae whole genome transcription data. A special focus is on the quantitative aspects of normalisation and mathematical modelling approaches, since they are expected to play an increasing role in future DNA microarray analysis studies. Data analysis is exemplified with cluster analysis, and newly developed co-clustering methods, where the DNA microarray analysis is enhanced by integrating data from multiple, heterogeneous sources.
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Cite this article as:
Grotkjaer T. and Nielsen J., Enhancing Yeast Transcription Analysis Through Integration of Heterogeneous Data, Current Genomics 2004; 5 (8) . https://dx.doi.org/10.2174/1389202043348472
DOI https://dx.doi.org/10.2174/1389202043348472 |
Print ISSN 1389-2029 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5488 |
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