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Introduction |
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With the genomes of many microorganisms
completely sequenced, and new ones emerging almost every month, science is
faced with the challenge of understanding the function of all the newly
discovered genes. The sequence of the entire genome of Saccharomyces
cerevisiae has revealed almost
6,000
protein-encoding genes, of which the function of fewer than half is
known with any confidence, indicating the enormous task that is ahead.
Interestingly, there is a growing number of open reading-frames (ORFs) in
yeast that show sequence similarities to genes of other species, but in
none of these is the function of the gene known. It appears that these
genes have escaped the classical ("function-first") genetic
approach, and their presence is revealed only by systematic sequencing.
The classical genetic approach is characterized by its qualitative nature,
i.e. by results that (in principle) give yes-or-no answers. However, the
genes that have escaped such classical analyses will probably most
commonly have quantitative, rather than qualitative, phenotypes. The field
which is emerging to establish their role is known as functional
genomics, and it differs from classical genetics both in the
comprehensive and integrative nature of its analytical approach and the
fact that it does not rely on a one-to-one relationship between gene and
phenotype.
Our approach to the functional genomics challenge is, like that of many
others, to develop large-scale analyses of gene expression of mutants of
defined genotype. To this end, data are being gathered at the level of
the transcriptome, the proteome and, unusually, the
metabolome. Other large-scale phenotype tests
(with which we are also involved) include the assessment of developmental
and morphological characteristics and the use of sensitive and quantitative
growth rate tests, particularly in microbial systems, where both medium
composition and sublethal concentrations of a large number of inhibitors
are varied to create a physiological pattern. A related approach, somewhat
equivalent to the analysis of knockout strains, has proved particularly
beneficial in predicting the site of action of metabolic inhibitors.
The implicit functional genomics agenda, then, is that by comparison of the
large-scale (co)expression of orphan genes with those of "known"
genes, in different genetic backgrounds and under different environmental
conditions, one may acquire clues as to the function of the orphans. At all
events, the inevitable results of these types of approaches is the generation
of large amounts of raw phenotypic data which alone are meaningless and which
must be analysed properly so as to obtain a proper understanding of the
biological problem of interest, and this statement is true for all organisms,
especially those whose genome sequence is available. With one or two exceptions,
only the most rudimentary unsupervised methods are being applied to this problem,
which at root involve the analysis of inputdata of very high
dimensionality with a view to obtaining information of very much
lower dimensionality.
Consequently we are developing powerful chemometric tools for the optimal
assignment of gene function based on the very high-dimensional phenotypic
data which may be acquired.
Organisms currently being studied here include:
Streptomyces coelicolor A3(2)
Escherichia coli
and
Saccharomyces
cerevisiae,
while we are also applying our technology to
Mycobacterium tuberculosis
and
Arabidopsis thaliana.
Note that the functional classification of unknown genes into classes
requires that these classes are themselves homogenous. This is presently not the case!
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Last update: 19 February 2004
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