People and ecosystems

Understanding of the links between coral reef ecosystems, the goods and services they provide to people, and the wellbeing of human societies.


Ecosystem dynamics: past, present and future

Examining the multi-scale dynamics of reefs, from population dynamics to macroevolution


Responding to a changing world

Advancing the fundamental understanding of the key processes underpinning reef resilience.

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Coral Reef Studies

From 2005 to 2022, the main node of the ARC Centre of Excellence for Coral Reef Studies was headquartered at James Cook University in Townsville, Queensland (Australia)

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Functional Association by Response Overlap (FARO)


Thursday 6 September, 4.00pm

ARC Centre of Excellence for Coral Reef Studies Conference Room, JCU, videonconferenced to Centre for Marine Studies Conference Room.
Dr. H. Bjorn Nielsen, Danish Technical University





The systematic comparison of transcriptional responses of organisms is a powerful tool in functional genomics. For example, mutants may be characterized by comparing their transcript profiles to those obtained in other experiments querying the effects on gene expression of many experimental factors including treatments, mutations and pathogen infections. Similarly, drugs may be discovered by the relationship between the transcript profiles effectuated or impacted by a candidate drug and by the target disease. The integration of such data enables systems biology to predict the interplay between experimental factors affecting a biological system. Unfortunately, direct comparisons of gene expression profiles obtained in independent, publicly available microarray experiments are typically compromised by substantial, experiment-specific biases. Here we suggest a novel yet conceptually simple approach for deriving ‘Functional Association(s) by Response Overlap’ (FARO) between microarray gene expression studies. The transcriptional response is defined by the set of differentially expressed genes independent from the magnitude or direction of the change. This approach overcomes the limited comparability between studies that is typical for methods that rely on correlation in gene expression. We apply FARO to a compendium of 242 diverse Arabidopsis microarray experimental factors, including phyto-hormones, stresses and pathogens, growth conditions/stages, tissue types and mutants. We also use FARO to confirm and further delineate the functions of Arabidopsis MAP kinase 4 in disease and stress responses. Furthermore, we find that a large, well-defined set of genes responds in opposing directions to different stress conditions and predict the effects of different stress combinations. This demonstrates the usefulness of our approach for exploiting public microarray data to derive biologically meaningful associations between experimental factors. Finally, our results indicate that FARO is more powerful in associating mutants in common pathways than existing methods such as co-expression analysis.


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