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Beyond differential expression: the quest for causal mutations and effector molecules

Nicholas J Hudson*, Brian P Dalrymple and Antonio Reverter

Author Affiliations

Computational and Systems BiologyCSIRO Livestock Industries, 306 Carmody Road St. Lucia, Brisbane, QLD 4067, Australia

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BMC Genomics 2012, 13:356  doi:10.1186/1471-2164-13-356

Published: 31 July 2012


High throughput gene expression technologies are a popular choice for researchers seeking molecular or systems-level explanations of biological phenomena. Nevertheless, there has been a groundswell of opinion that these approaches have not lived up to the hype because the interpretation of the data has lagged behind its generation. In our view a major problem has been an over-reliance on isolated lists of differentially expressed (DE) genes which – by simply comparing genes to themselves – have the pitfall of taking molecular information out of context. Numerous scientists have emphasised the need for better context. This can be achieved through holistic measurements of differential connectivity in addition to, or in replacement, of DE. However, many scientists continue to use isolated lists of DE genes as the major source of input data for common readily available analytical tools. Focussing this opinion article on our own research in skeletal muscle, we outline our resolutions to these problems – particularly a universally powerful way of quantifying differential connectivity. With a well designed experiment, it is now possible to use gene expression to identify causal mutations and the other major effector molecules with whom they cooperate, irrespective of whether they themselves are DE. We explain why, for various reasons, no other currently available experimental techniques or quantitative analyses are capable of reaching these conclusions.

Differential connectivity; Differential networking; Gene expression; Causal mutation algorithm