Abstract–Gene Expression
A lot can be learned about genes by examining their behavior patterns as they are
expressed in a series of different conditions. During the years, a large amount of
information about gene expression has been accumulated, primarily by utilizing
microarray technology. A need to process all this information has arisen, in order to
create a global picture of the state of the cell in various conditions. The articles, to which
we will refer, describe new approaches to investigating the expression of the entire
genome, as opposed to past research studies, which dealt only with the expression of a
specific gene or a group of several genes.
As a result of these trends in biological research, the Hierarchical Clustering algorithm
was developed to group together genes with similar patterns of expression. The input for
the algorithm is data about the expression of each gene in a set of conditions. The
algorithm arranges the genes in the form of a tree whose branch lengths represent the
degree of similarity between them 1. The tree dictates the order on the genes, which is
displayed graphically, representing the relationships among them. One of the main
conclusions of applying this algorithm on the yeast Saccharomyces Cerevisiae genome
was that arranging its genes in clusters according to the patterns of their expression
correlates with their biological function in the cell.
An alternative algorithm attempts to overcome some limitations of traditional clustering
methods by assigning genes to context-dependent and potentially overlapping
‘transcription modules’ 2. A ‘transcription module’ refers both to a group of co-regulated
genes and the experimental conditions that trigger this co-regulation 2. This algorithm is
referred to as the ‘signature algorithm’.
Using these algorithms on gene expression in various stress conditions can lead to
significant biological conclusions. It is possible to infer biological function for an
unannotated gene by matching its expression pattern to annotated genes with the same
profile 5. Promotor analysis of a co-regulated group of genes can reveal a common
sequence. This sequence may be a known binding site for a certain transcription factor
that is responsible for this co-regulation. In other cases, this may be a novel element that
provides clues to novel regulators 3.
A yeast cell cycle research used a periodicity algorithm, which is based on Fourier
transform, and the correlation algorithm. Based on the results of this analysis, a
comprehensive catalogue of yeast genes, whose transcript levels periodically vary within
the cell cycle, was created 4. This research has provided insight into the tight regulation
of the cell cycle.
1. Eisen MB et al. (1998) Cluster analysis and display of genome-wide expression pattern. Proc. Natl. Acad. Sci. USA; 95(25):14863-14868.
2. Ihmels J et al. (2002) Revealing Modular organization in the yeast transcriptional network. Nat Genet. 31(4):370-377.
3. Gasch AP, et al. (2000) Genomic expression programs in the response of yeast cells to environmental changes. Mol. Biol. Cell. 11(12) :4241-4257.
4. Spellman, P.T. et al. (2000). Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray
hybridization. Mol. Biol. Cell. 9(12):3273-3297.
5. Pierre Legrain, Jerome Wojcik and Jean-Michel Gauthier. (2001). Protein – protein Interactions maps: a lead towards cellular functions. Trends
Genet. 17(6): 346-352.