DOI: http://dx.doi.org/10.7554/eLife.05519.008 DOI: http://dx.doi.org/10.7554/eLife.05519.006 10.7554/eLife.05519.009 Box 3. Example: Gene expression study using RNA-seq A study is designed to examine differences in gene expression in kidney tissue taken from human subjects who exhibit a hypertensive phenotype and those who do not.
Many phenomena of interest to science, however for example, gene expression in an organism or ecological interactions in an environment generate data with thousands (or more) parameters for every point.
For example, Gene expression signature was identified to reflect the activation status of several oncogenic pathways that were deregulated in tumours and clinically relevant associations with disease outcomes.
For example, gene expression profiling was combined with functional genomic and proteomic data from various species to generate a network containing 118 genes linked by 866 potential functional associations for breast cancer [21].
For example, gene expression can be regulated at multiple levels that include epigenetic modulation by DNA methylation or through histone modifications that modify chromatin accessibility.
For example, gene expression alterations have been implicated in the etiologies of common diseases such as cancers [1] [3], cardiovascular diseases [4], and psychiatric disorders [5].
For example, gene expression level polymorphisms (ELPs) in Arabidopsis were observed between the Eil-0 and Lc-0 accessions [26] and these ELPs may be candidates for quantitative trait loci (QTLs) that influence phenotypic variability.
Genetical genomics linkage and association analyses of, for example, gene expression phenotypes with the help of microarray data – is a promising strategy to identify regulatory determinants of complex traits or diseases [1] [3].
For example, gene expression profiling has shown that breast tumors can be classified into subgroups displaying distinct characteristics with respect to both clinical markers and patient outcome [4] [7], which emphasizes that breast cancer is a heterogeneous disease that should be treated accordingly.
For example, gene expression profiling was used to analyze the effects of DAC on the gene expression pattern of HCT116 colon cancer cells and the results suggested that, besides gene activation of hypermethylated genes, transcriptional downregulation may be an important effect of DAC [16], [17].
For example, gene expression studies are providing clues about molecular targets for food components.
