brain-coX: investigating and visualising gene co-expression in seven human brain transcriptomic datasets

Abstract

Background: The pathogenesis of neurological and mental health disorders often involves multiple genes, complex interactions, as well as brain- and development-specific biological mechanisms. These characteristics make identification of disease genes for such disorders challenging, as conventional prioritisation tools are not specifically tailored to deal with the complexity of the human brain. Thus, we developed a novel web-application-brain-coX-that offers gene prioritisation with accompanying visualisations based on seven gene expression datasets in the post-mortem human brain, the largest such resource ever assembled.

Results: We tested whether our tool can correctly prioritise known genes from 37 brain-specific KEGG pathways and 17 psychiatric conditions. We achieved average sensitivity of nearly 50%, at the same time reaching a specificity of approximately 75%. We also compared brain-coX's performance to that of its main competitors, Endeavour and ToppGene, focusing on the ability to discover novel associations. Using a subset of the curated SFARI autism gene collection we show that brain-coX's prioritisations are most similar to SFARI's own curated gene classifications.

Conclusions: brain-coX is the first prioritisation and visualisation web-tool targeted to the human brain and can be freely accessed via http://shiny.bioinf.wehi.edu.au/freytag.s/ .

Fig. 1

Accuracy of brain-coX in predicting KEGG pathways. The displayed accuracy measures were generated from leave-one-out cross-validation using 37 KEGG pathways that function in the human brain. We also examine the effect of requiring a gene to be prioritised in multiple datasets on the accuracy measures. a Specificity of brain-coX prioritisation approach. b Sensitivity of the brain-coX prioritisation approach

Fig. 2

Accuracy of brain-coX in predicting disease genes in PsyGeNet. The displayed accuracy measures were generated from leave-one-out cross-validation using 17 PsyGeNet diseases that function in the human brain. We also examine the effect of requiring a gene to be prioritised in multiple datasets on the accuracy measures. a Specificity of brain-coX prioritisation approach. b Sensitivity of the brain-coX prioritisation approach

Fig. 3

Cumulative mean score for SAFRI candidate genes. We prioritised 340 genes in the SAFRI database for autism with three different prioritisation approaches given 17 known autism genes. For the first 100 prioritised genes of each method we calculated the cumulative mean of the respective SFARI scores (2–6). Lower scores indicate genes that are more likely to be involved in autism