The Experts below are selected from a list of 1655685 Experts worldwide ranked by ideXlab platform
Alvis Brazma - One of the best experts on this subject based on the ideXlab platform.
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eLS - Gene Expression Databases
Encyclopedia of Life Sciences, 2007Co-Authors: Alvis Brazma, Ugis SarkansAbstract: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
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Gene Expression data analysis
FEBS Letters, 2000Co-Authors: Alvis Brazma, Jaak ViloAbstract: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.
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Gene Expression data analysis
FEBS Letters, 2000Co-Authors: Alvis Brazma, Jaak ViloAbstract: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.
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Monoallelic Gene Expression in mammals
Chromosoma, 2009Co-Authors: Irina S. Zakharova, Alexander I. Shevchenko, Suren M. ZakianAbstract: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.
Li Q. Zhang - One of the best experts on this subject based on the ideXlab platform.
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Gene Expression profiling of human diseases by serial analysis of Gene Expression
Journal of Biomedical Science, 2002Co-Authors: Shui Qing Ye, David C. Usher, Li Q. ZhangAbstract:Until recently, the approach to understanding the molecular basis of complex syndromes such as cancer, coronary artery disease, and diabetes was to study the behavior of individual Genes. However, it is Generally recognized that Expression of a number of Genes is coordinated both spatially and temporally and that this coordination changes during the development and progression of diseases. Newly developed functional genomic approaches, such as serial analysis of Gene Expression (SAGE) and DNA microarrays have enabled researchers to determine the Expression pattern of thousands of Genes simultaneously. One attractive feature of SAGE compared to microarrays is its ability to quantify Gene Expression without prior sequence information or information about Genes that are thought to be expressed. SAGE has been successfully applied to the Gene Expression profiling of a number of human diseases. In this review, we will first discuss SAGE technique and contrast it to microarray. We will then highlight new biological insights that have emerged from its application to the study of human diseases.
Daphne Koller - One of the best experts on this subject based on the ideXlab platform.
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population genomics of human Gene Expression
Nature Genetics, 2007Co-Authors: Barbara Elaine Stranger, Alexandra C Nica, Matthew Forrest, Antigone S Dimas, Christine P Bird, Claude Beazley, Catherine E Ingle, Mark J Dunning, Paul Flicek, Daphne KollerAbstract:Genetic variation influences Gene Expression, and this variation in Gene Expression can be efficiently mapped to specific genomic regions and variants. Here we have used Gene Expression profiling of Epstein-Barr virus‐transformed lymphoblastoid cell lines of all 270 individuals genotyped in the HapMap Consortium to elucidate the detailed features of Genetic variation underlying Gene Expression variation. We find that Gene Expression is heritable and that differentiation between populations is in agreement with earlier small-scale studies. A detailed association analysis of over 2.2 million common SNPs per population (5% frequency in HapMap) with Gene Expression identified at least 1,348 Genes with association signals in cis and at least 180 in trans. Replication in at least one independent population was achieved for 37% of cis signals and 15% of trans signals, respectively. Our results strongly support an abundance of cis-regulatory variation in the human genome. Detection of trans effects is limited but suggests that regulatory variation may be the key primary effect contributing to phenotypic variation in humans. We also explore several methodologies that improve the current state of analysis of Gene Expression variation.
