Melanocytes respond by altering gene transcription, and these changes in
gene expression profiles result in easily quantifiable phenotypes such as modified pigment production (a hallmark of melanocyte differentiation state) and changes in morphological cell properties.
Although the database described here was based on
the gene expression profiling results of the FunGenES consortium, it can be easily adapted to incorporate available or future genomics data obtained in ES cells.
Biclustering of drug ‐ induced
gene expression profiles resulted in modules of drugs and genes, which were enriched in both drug and gene annotations.
Not exact matches
The
resulting transcriptional
profiles cluster not by postpartum day, but by milk Na: K ratio, indicating that women sampled during similar postpartum time frames could be at markedly different stages of
gene expression.
The approach to target validation utilizes RNAi, CRISPR, and ORF
expression platforms to genetically perturb candidate target
gene expression in relevant cancer cell lines, and then
profile the
resulting phenotypic changes with regard to their effect on various biochemical pathways.
Proteomic analysis to
profile protein abundance
resulted in the identification and relative quantification for 912 proteins with two or more unique peptides and 86 proteins with significant abundance changes after treatment with the neurotoxins, while microarray analyses to
profile gene expression revealed 181
genes with significant changes in mRNA after treatment.
To better illustrate
gene expression profiles in mouse ES cells, we have organized the
results in an interactive database with a number of features and tools.
Results: Here, we
profile genome - wide changes in DNA methylation,
gene expression and lipidomics in response to DR and aging in female mouse liver.
Since there is a higher than 95 % chance that cluster assignments are accurate (Supplemental File S2), and our validation analysis shows that 90.7 % of the array
expression patterns match the RNA analysis
results using other techniques (e.g., Q - PCR), we estimate that more than 86 % of the
genes in a cluster follow the corresponding average
expression profile.
The
results show comparable
gene expression profiles between microarray and Q - PCR data.