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Gene Expression

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Alvis Brazma – One of the best experts on this subject based on the ideXlab platform.

  • eLS – Gene Expression Databases
    Encyclopedia of Life Sciences, 2007
    Co-Authors: Alvis Brazma, Ugis Sarkans
    Abstract:

    Gene Expression databases store information about the absolute or relative abundance of Gene transcription products in various biological samples, such as cells from a particular tissue in a particular organism, or a particular cell line. These databases allow one to access, select, retrieve and combine for analysis Gene Expression datasets Generated by one’s own or other laboratories. Keywords: Gene Expression; microarrays; database

  • Gene Expression data analysis
    FEBS Letters, 2000
    Co-Authors: Alvis Brazma, Jaak Vilo
    Abstract:

    Microarrays are one of the latest breakthroughs in experimental molecular biology, which allow monitoring of Gene Expression for tens of thousands of Genes in parallel and are already producing huge amounts of valuable data. Analysis and handling of such data is becoming one of the major bottlenecks in the utilization of the technology. The raw microarray data are images, which have to be transformed into Gene Expression matrices – tables where rows represent Genes, columns represent various samples such as tissues or experimental conditions, and numbers in each cell characterize the Expression level of the particular Gene in the particular sample. These matrices have to be analyzed further, if any knowledge about the underlying biological processes is to be extracted. In this paper we concentrate on discussing bioinformatics methods used for such analysis. We briefly discuss supervised and unsupervised data analysis and its applications, such as predicting Gene function classes and cancer classification. Then we discuss how the Gene Expression matrix can be used to predict putative regulatory signals in the genome sequences. In conclusion we discuss some possible future directions.

Jaak Vilo – One of the best experts on this subject based on the ideXlab platform.

  • Gene Expression data analysis
    FEBS Letters, 2000
    Co-Authors: Alvis Brazma, Jaak Vilo
    Abstract:

    Microarrays are one of the latest breakthroughs in experimental molecular biology, which allow monitoring of Gene Expression for tens of thousands of Genes in parallel and are already producing huge amounts of valuable data. Analysis and handling of such data is becoming one of the major bottlenecks in the utilization of the technology. The raw microarray data are images, which have to be transformed into Gene Expression matrices – tables where rows represent Genes, columns represent various samples such as tissues or experimental conditions, and numbers in each cell characterize the Expression level of the particular Gene in the particular sample. These matrices have to be analyzed further, if any knowledge about the underlying biological processes is to be extracted. In this paper we concentrate on discussing bioinformatics methods used for such analysis. We briefly discuss supervised and unsupervised data analysis and its applications, such as predicting Gene function classes and cancer classification. Then we discuss how the Gene Expression matrix can be used to predict putative regulatory signals in the genome sequences. In conclusion we discuss some possible future directions.

Suren M. Zakian – One of the best experts on this subject based on the ideXlab platform.

  • Monoallelic Gene Expression in mammals
    Chromosoma, 2009
    Co-Authors: Irina S. Zakharova, Alexander I. Shevchenko, Suren M. Zakian
    Abstract:

    Three systems of monoallelic Gene Expression in mammals are known, namely, X-chromosome inactivation, imprinting, and allelic exclusion. In all three systems, monoallelic Expression is regulated epiGenetically and is frequently directed by long non-coding RNAs (ncRNAs). This review briefs all three systems of monoallelic Gene Expression in mammals focusing on chromatin modifications, spatial chromosome organization in the nucleus, and the functioning of ncRNAs.