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brain-coX: investigating and visualising gene co-expression in seven human brain transcriptomic datasets
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 p...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5465565/ https://www.ncbi.nlm.nih.gov/pubmed/28595657 http://dx.doi.org/10.1186/s13073-017-0444-y |
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author | Freytag, Saskia Burgess, Rosemary Oliver, Karen L. Bahlo, Melanie |
author_facet | Freytag, Saskia Burgess, Rosemary Oliver, Karen L. Bahlo, Melanie |
author_sort | Freytag, Saskia |
collection | PubMed |
description | 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/. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13073-017-0444-y) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5465565 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-54655652017-06-09 brain-coX: investigating and visualising gene co-expression in seven human brain transcriptomic datasets Freytag, Saskia Burgess, Rosemary Oliver, Karen L. Bahlo, Melanie Genome Med Software 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/. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13073-017-0444-y) contains supplementary material, which is available to authorized users. BioMed Central 2017-06-08 /pmc/articles/PMC5465565/ /pubmed/28595657 http://dx.doi.org/10.1186/s13073-017-0444-y Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Software Freytag, Saskia Burgess, Rosemary Oliver, Karen L. Bahlo, Melanie brain-coX: investigating and visualising gene co-expression in seven human brain transcriptomic datasets |
title | brain-coX: investigating and visualising gene co-expression in seven human brain transcriptomic datasets |
title_full | brain-coX: investigating and visualising gene co-expression in seven human brain transcriptomic datasets |
title_fullStr | brain-coX: investigating and visualising gene co-expression in seven human brain transcriptomic datasets |
title_full_unstemmed | brain-coX: investigating and visualising gene co-expression in seven human brain transcriptomic datasets |
title_short | brain-coX: investigating and visualising gene co-expression in seven human brain transcriptomic datasets |
title_sort | brain-cox: investigating and visualising gene co-expression in seven human brain transcriptomic datasets |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5465565/ https://www.ncbi.nlm.nih.gov/pubmed/28595657 http://dx.doi.org/10.1186/s13073-017-0444-y |
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