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Mean new microRNA data analysis method gives sharper results
June 16, 2009Our understanding of the importance of microRNAs in regulating gene expression is expanding, and with it our requirement for robust methods to measure their expression levels. Now a new method published in BioMed Central's open access journal Genome Biology helps researchers to better understand the delicate interplay between differences in microRNA expression levels and their target genes.
Following their highly cited method for the analysis of reverse transcription quantitative PCR (RT-qPCR) expression data for mRNA transcripts, published in Genome Biology in 2007, Jo Vandesompele of the Center for Medical Genetics, Ghent University Hospital, Belgium and colleagues have created an innovative, straightforward and universally applicable method for quantitative RT-PCR data normalization for microRNAs. The researchers also provide a workflow for proper data normalization of both large scale (whole miRNome) and small-scale microRNA profiling experiments in their paper.
Vandesompele's team profiled 430 microRNAs along with 18 small RNA controls for a number of independent tissue sample sets. They assessed the use of the mean expression value of all expressed microRNAs in a given sample as a normalization factor for microRNA real-time quantitative PCR data, and then compared this method with the currently adopted approach of relying on one or two small RNA controls.
The mean expression value outperforms the current normalization strategy by better reducing technical variation and more accurately appreciating biological changes. Furthermore, normalization using microRNAs that resemble the mean expression value is platform independent and closely mimics normalization using the mean expression value.
"The mean expression value of all expressed microRNAs performs better than one based on only those microRNAs that are expressed in all samples," says Vandesompele. "This suggests a more accurate representation of input RNA fluctuations when all microRNAs are considered."
"Changes in microRNA expression are often very subtle but biologically relevant," says Pieter Mestdagh, working on the method in Vandesompele's lab. "Because microRNAs regulate genes acting in different pathways, deregulated microRNA expression can trigger a cascade of events that alter the biology of the cell. As a consequence, a proper normalization strategy that enables the detection of small changes is of utmost importance, especially when dealing with heterogeneous patient samples."
Reverse transcription quantitative PCR (RT-qPCR) has become the method of choice for measuring gene expression levels in terms of accuracy and specificity, both for coding and non-coding RNAs. However, result accuracy is largely dependent on effective data normalization.
BioMed Central
The mean expression value outperforms the current normalization strategy by better reducing technical variation and more accurately appreciating biological changes. Furthermore, normalization using microRNAs that resemble the mean expression value is platform independent and closely mimics normalization using the mean expression value.
"The mean expression value of all expressed microRNAs performs better than one based on only those microRNAs that are expressed in all samples," says Vandesompele. "This suggests a more accurate representation of input RNA fluctuations when all microRNAs are considered."
"Changes in microRNA expression are often very subtle but biologically relevant," says Pieter Mestdagh, working on the method in Vandesompele's lab. "Because microRNAs regulate genes acting in different pathways, deregulated microRNA expression can trigger a cascade of events that alter the biology of the cell. As a consequence, a proper normalization strategy that enables the detection of small changes is of utmost importance, especially when dealing with heterogeneous patient samples."
Reverse transcription quantitative PCR (RT-qPCR) has become the method of choice for measuring gene expression levels in terms of accuracy and specificity, both for coding and non-coding RNAs. However, result accuracy is largely dependent on effective data normalization.
BioMed Central
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Study sheds light on evolution of human complexity
A painstaking analysis of thousands of genes and the proteins they encode shows that human beings are biologically complex, at least in part, because of the way humans evolved to cope with redundancies arising from duplicate genes.
MicroRNA-mediated metastasis suppression
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Texas A&M researchers find new mechanism for circadian rhythm
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New cancer gene discovered
A new cancer gene has been discovered by a research group at the Sahlgrenska Academy. The gene causes an insidious form of glandular cancer usually in the head and neck and in women also in the breast. The discovery could lead to quicker and better diagnosis and more effective treatment.
UCSF scientists illuminate how microRNAs drive tumor progression
UCSF researchers have identified collections of tiny molecules known as microRNAs that affect distinct processes critical for the progression of cancer.
Lung cancer suppresses miR-200 to invade and spread
Primary lung cancer shifts to metastatic disease by suppressing a family of small molecules that normally locks the tumor in a noninvasive state, researchers at The University of Texas M. D. Anderson Cancer Center report in the Sept. 15 edition of Genes and Development.
Scientists Use MicroRNAs to Track Evolutionary History for First Time
The large group of segmented worms known as annelids, which includes earthworms, leeches and bristle worms, evolved millions of years ago and can be found in every corner of the world.
MicroRNAs circulating in blood show promise as biomarkers to detect pancreatic cancer
A blood test for small molecules abnormally expressed in pancreatic cancer may be a promising route to early detection of the disease.
MicroRNA in human saliva may help diagnose oral cancer
Researchers continue to add to the diagnostic alphabet of saliva by identifying the presence of at least 50 microRNAs that could aid in the detection of oral cancer, according to a report in Clinical Cancer Research, a journal of the American Association for Cancer Research.
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