Yevgeny Doctor & Guy Nimrod
CS-LS 2nd year, Hebrew University of Jerusalem Israel
Gene Expression : Clustering
Abstract
The rapid advance of genome-scale sequencing has driven the development of
methods to exploit this information by characterizing biological processes in new ways.
Microarrays are one of the latest breakthroughs in molecular biology as a tool for
monitoring the expression level of each gene in the cell. Their applications embody
much of what is implied by the term ‘genomics’ – they are broad in scope, large in
scale and take advantage of all available sequence information for experimental design
and data interpretation in pursuit of biological understanding.
In order to process the large amounts of information produced by this technique,
new algorithmical approaches were developed, and we will present one of them –
clustering. This method organizes genes in clusters according to similarity in patterns of
gene expression, and displays the data graphically in a way that is intuitive for
biologists. The clustering of the genes can be used to gain understanding of the
functions of the genes and their regulation, as well of global cellular processes. We
present two large-scale experiments in yeast that used the methods described above.
The first research explores the genomic expression patterns in yeast in response
to diverse environmental transitions. A large set of genes (~900) showed a similar
drastic response to almost all environmental changes, and there were additional features
of the genomic responses specialized for specific conditions. This work gives a partial
sketch of the complex regulation of the environmental stress response of yeast and
suggests several hypotheses for further investigation.
The second work uses the expession pattern analysis in order to identify and
characterize genes whose transcript levels vary periodically within the cell cycle. The
authors explore the functional properties of the gene clusters, and look for known and
new promoter elements regulating those genes. The resulting data gives a partial picture
of the circuitry of the transcriptional controls in the cell cycle.
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