Whitehead Institute researchers report that common assumptions employed in the generation and interpretation of data from global gene expression analyses can lead to seriously flawed conclusions about gene activity and cell behavior in a wide range of current biological research. "Expression analysis is one of the most commonly used methods in modern biology," says Whitehead Member Dr. Richard Young. "So we are concerned that flawed assumptions may affect the interpretation of many biological studies." Much of today's interpretation of gene expression data relies on the assumption that all cells being analyzed have similar total amounts of messenger RNA (mRNA), the roughly 10% of a cell's RNA that acts as a blueprint for protein synthesis. However, some cells, including aggressive cancer cells, produce several times more mRNA than other cells. Traditional global gene expression analyses have typically ignored such differences. "We've highlighted this common assumption in gene expression analysis that potentially affects many researchers," says Dr. Tony Lee, a scientist in Dr. Young's lab and a corresponding author of the article published in the October 26, 2012 issue of Cell. "We provided a concrete example of the problem and a solution that can be implemented by investigators." Members of the Dr. Young lab recently uncovered the flaw while investigating genes expressed in cancer cells expressing high levels of c-Myc, a gene regulator known to be highly expressed in aggressive cancer cells. When comparing cells with high and low c-Myc levels, they were surprised to find very different results using different approaches to gene expression analysis.
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