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By exploiting gene expression profiling, Segal and colleagues constructed a functional module map for cancer to investigate commonalities and variations between different types of tumor [42].
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Current multi-tissue eQTL mapping techniques are limited to only exploiting gene expression patterns across multiple tissues either in a joint tissue or tissue-by-tissue frameworks.
Rather than employing literature-based sets of genes sharing similar biological functions, so-called network approaches are increasingly used, which infer functional clusters of genes from the expression data itself by exploiting gene coexpression patterns hidden within the data (Zhang & Horvath, 2005).
In fact, the transcriptome maps studies mentioned above showed the biological relevance of a global view of gene expression distribution by exploiting the availability of gene expression profile data obtained by the method of SAGE [ 2, 3, 5].
This barrier activity may be exploited in gene expression studies to protect transgenes from epigenetic gene silencing.
We created a functional gene network by exploiting co-expression observations through a guilt-by-association assumption.
We detected functional candidate genes for these traits by exploiting phenotype-correlated expression.
Exploiting the vast gene expression data in public repositories is often complicated by low cross-platform comparability.
Corollary 4 (proof omitted) illustrates that exploiting time-varying gene expression is advantageous in three ways.
By analysing this gene expression resource and exploiting the extreme diversity and rapid linkage disequilibrium decay of maize6, we characterize the effect of rare alleles and evolutionary history on the regulation of expression.
Throughout development, changes in chromatin organization are exploited to control gene expression.
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