Algorithm for reducing errors in transcriptome analysis

The original research, published in the journal Frontiers in Molecular Biosciences, shows that the OncoFinder method significantly reduces errors introduced by transcriptome-wide experimental techniques. Scientists compared gene expression data for the same biological samples obtained by both next generation sequencing (NGS) and microarray methods, finding that these different techniques have almost no correlation between the gene expression values for all datasets analysed. In contrast, when the OncoFinder algorithm is applied to the data, a clear correlation between next generation sequencing and microarray gene expression datasets was seen.

"For several years the potential for the use of gene expression data in research and clinical applications has been underappreciated due to the inconsistency of the data coming from the various types of equipment. There is just too much variation and complexity when comparing the massive number of individual genes. But when this complexity is reduced and the gene expression is mapped onto signalling pathways, we can evaluate the pathway activation drift and analyse the changes and transitions much more effectively. The OncoFinder algorithm enables scientists to characterise the functional states of transcriptomes more accurately than before and we hope that this will become a method of choice in genetics, physiology, biomedicine and molecular diagnostics," said Alex Zhavoronkov, PhD, director of the BGRF and co-author of the study.