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Novel DNA microarray system for analysis of nascent mRNAs.

ABSTRACT: Transcriptional activation and repression are a key step in the regulation of all cellular activities. The development of comprehensive analysis methods such as DNA microarray has advanced our understanding of the correlation between the regulation of transcription and that of cellular mechanisms. However, DNA microarray analysis based on steady-state mRNA (total mRNA) does not always correspond to transcriptional activation or repression. To comprehend these transcriptional regulations, the detection of nascent RNAs is more informative. Although the nuclear run-on assay can detect nascent RNAs, it has not been fully applied to DNA microarray analysis. In this study, we have developed a highly efficient method for isolating bromouridine-labeled nascent RNAs that can be successfully applied to DNA microarray analysis. This method can linearly amplify small amounts of mRNAs with little bias. Furthermore, we have applied this method to DNA microarray analysis from mouse G2-arrested cells and have identified several genes that exhibit novel expression profiles. This method will provide important information in the field of transcriptome analysis of various cellular processes.

SUBMITTER: Ohtsu M

PROVIDER: S-EPMC2575885 | biostudies-other | 2008 Aug

REPOSITORIES: biostudies-other

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Novel DNA microarray system for analysis of nascent mRNAs.

Ohtsu Masaya M Kawate Mika M Fukuoka Masashi M Gunji Wataru W Hanaoka Fumio F Utsugi Takahiko T Onoda Fumitoshi F Murakami Yasufumi Y

DNA research : an international journal for rapid publication of reports on genes and genomes 20080708 4

Transcriptional activation and repression are a key step in the regulation of all cellular activities. The development of comprehensive analysis methods such as DNA microarray has advanced our understanding of the correlation between the regulation of transcription and that of cellular mechanisms. However, DNA microarray analysis based on steady-state mRNA (total mRNA) does not always correspond to transcriptional activation or repression. To comprehend these transcriptional regulations, the det ...[more]

PMID: 18611946

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Project description:BackgroundData clustering analysis has been extensively applied to extract information from gene expression profiles obtained with DNA microarrays. To this aim, existing clustering approaches, mainly developed in computer science, have been adapted to microarray data analysis. However, previous studies revealed that microarray datasets have very diverse structures, some of which may not be correctly captured by current clustering methods. We therefore approached the problem from a new starting point, and developed a clustering algorithm designed to capture dataset-specific structures at the beginning of the process.ResultsThe clustering algorithm is named Fuzzy clustering by Local Approximation of MEmbership (FLAME). Distinctive elements of FLAME are: (i) definition of the neighborhood of each object (gene or sample) and identification of objects with "archetypal" features named Cluster Supporting Objects, around which to construct the clusters; (ii) assignment to each object of a fuzzy membership vector approximated from the memberships of its neighboring objects, by an iterative converging process in which membership spreads from the Cluster Supporting Objects through their neighbors. Comparative analysis with K-means, hierarchical, fuzzy C-means and fuzzy self-organizing maps (SOM) showed that data partitions generated by FLAME are not superimposable to those of other methods and, although different types of datasets are better partitioned by different algorithms, FLAME displays the best overall performance. FLAME is implemented, together with all the above-mentioned algorithms, in a C++ software with graphical interface for Linux and Windows, capable of handling very large datasets, named Gene Expression Data Analysis Studio (GEDAS), freely available under GNU General Public License.ConclusionThe FLAME algorithm has intrinsic advantages, such as the ability to capture non-linear relationships and non-globular clusters, the automated definition of the number of clusters, and the identification of cluster outliers, i.e. genes that are not assigned to any cluster. As a result, clusters are more internally homogeneous and more diverse from each other, and provide better partitioning of biological functions. The clustering algorithm can be easily extended to applications different from gene expression analysis.

Dataset Information

Novel DNA microarray system for analysis of nascent mRNAs.

Publications

Novel DNA microarray system for analysis of nascent mRNAs.

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