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Disentangling function from topology to infer the network properties of disease genes
BACKGROUND: The topological features of disease genes within interaction networks are the subject of intense study, as they shed light on common mechanisms of pathology and are useful for uncovering additional disease genes. Computational analyses typically try to uncover whether disease genes exhib...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3614482/ https://www.ncbi.nlm.nih.gov/pubmed/23324116 http://dx.doi.org/10.1186/1752-0509-7-5 |
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author | Ghersi, Dario Singh, Mona |
author_facet | Ghersi, Dario Singh, Mona |
author_sort | Ghersi, Dario |
collection | PubMed |
description | BACKGROUND: The topological features of disease genes within interaction networks are the subject of intense study, as they shed light on common mechanisms of pathology and are useful for uncovering additional disease genes. Computational analyses typically try to uncover whether disease genes exhibit distinct network features, as compared to all genes. RESULTS: We demonstrate that the functional composition of disease gene sets is an important confounding factor in these types of analyses. We consider five disease sets and show that while they indeed have distinct topological features, they are also enriched in functions that a priori exhibit distinct network properties. To address this, we develop a computational framework to assess the network properties of disease genes based on a sampling algorithm that generates control gene sets that are functionally similar to the disease set. Using our function-constrained sampling approach, we demonstrate that for most of the topological properties studied, disease genes are more similar to sets of genes with similar functional make-up than they are to randomly selected genes; this suggests that these observed differences in topological properties reflect not only the distinguishing network features of disease genes but also their functional composition. Nevertheless, we also highlight many cases where disease genes have distinct topological properties even when accounting for function. CONCLUSIONS: Our approach is an important first step in extracting the residual topological differences in disease genes when accounting for function, and leads to new insights into the network properties of disease genes. |
format | Online Article Text |
id | pubmed-3614482 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-36144822013-04-05 Disentangling function from topology to infer the network properties of disease genes Ghersi, Dario Singh, Mona BMC Syst Biol Research Article BACKGROUND: The topological features of disease genes within interaction networks are the subject of intense study, as they shed light on common mechanisms of pathology and are useful for uncovering additional disease genes. Computational analyses typically try to uncover whether disease genes exhibit distinct network features, as compared to all genes. RESULTS: We demonstrate that the functional composition of disease gene sets is an important confounding factor in these types of analyses. We consider five disease sets and show that while they indeed have distinct topological features, they are also enriched in functions that a priori exhibit distinct network properties. To address this, we develop a computational framework to assess the network properties of disease genes based on a sampling algorithm that generates control gene sets that are functionally similar to the disease set. Using our function-constrained sampling approach, we demonstrate that for most of the topological properties studied, disease genes are more similar to sets of genes with similar functional make-up than they are to randomly selected genes; this suggests that these observed differences in topological properties reflect not only the distinguishing network features of disease genes but also their functional composition. Nevertheless, we also highlight many cases where disease genes have distinct topological properties even when accounting for function. CONCLUSIONS: Our approach is an important first step in extracting the residual topological differences in disease genes when accounting for function, and leads to new insights into the network properties of disease genes. BioMed Central 2013-01-16 /pmc/articles/PMC3614482/ /pubmed/23324116 http://dx.doi.org/10.1186/1752-0509-7-5 Text en Copyright © 2013 Ghersi and Singh; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Ghersi, Dario Singh, Mona Disentangling function from topology to infer the network properties of disease genes |
title | Disentangling function from topology to infer the network properties of disease genes |
title_full | Disentangling function from topology to infer the network properties of disease genes |
title_fullStr | Disentangling function from topology to infer the network properties of disease genes |
title_full_unstemmed | Disentangling function from topology to infer the network properties of disease genes |
title_short | Disentangling function from topology to infer the network properties of disease genes |
title_sort | disentangling function from topology to infer the network properties of disease genes |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3614482/ https://www.ncbi.nlm.nih.gov/pubmed/23324116 http://dx.doi.org/10.1186/1752-0509-7-5 |
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