Uncovering disease mechanisms through network biology in the era of Next Generation Sequencing
Characterizing the behavior of disease genes in the context of biological networks has the potential to shed light on disease mechanisms, and to reveal both new candidate disease genes and therapeutic targets. Previous studies addressing the network properties of disease genes have produced contradi...
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paper:paper_20452322_v6_n_p_Pinero2023-06-08T16:33:27Z Uncovering disease mechanisms through network biology in the era of Next Generation Sequencing Chernomoretz, Ariel Furlong, Laura Inés biology genetics high throughput sequencing human neoplasm procedures Computational Biology High-Throughput Nucleotide Sequencing Humans Neoplasms Characterizing the behavior of disease genes in the context of biological networks has the potential to shed light on disease mechanisms, and to reveal both new candidate disease genes and therapeutic targets. Previous studies addressing the network properties of disease genes have produced contradictory results. Here we have explored the causes of these discrepancies and assessed the relationship between the network roles of disease genes and their tolerance to deleterious germline variants in human populations leveraging on: the abundance of interactome resources, a comprehensive catalog of disease genes and exome variation data. We found that the most salient network features of disease genes are driven by cancer genes and that genes related to different types of diseases play network roles whose centrality is inversely correlated to their tolerance to likely deleterious germline mutations. This proved to be a multiscale signature, including global, mesoscopic and local network centrality features. Cancer driver genes, the most sensitive to deleterious variants, occupy the most central positions, followed by dominant disease genes and then by recessive disease genes, which are tolerant to variants and isolated within their network modules. Fil:Chernomoretz, A. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Furlong, L.I. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. 2016 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_20452322_v6_n_p_Pinero http://hdl.handle.net/20.500.12110/paper_20452322_v6_n_p_Pinero |
| institution |
Universidad de Buenos Aires |
| institution_str |
I-28 |
| repository_str |
R-134 |
| collection |
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
| topic |
biology genetics high throughput sequencing human neoplasm procedures Computational Biology High-Throughput Nucleotide Sequencing Humans Neoplasms |
| spellingShingle |
biology genetics high throughput sequencing human neoplasm procedures Computational Biology High-Throughput Nucleotide Sequencing Humans Neoplasms Chernomoretz, Ariel Furlong, Laura Inés Uncovering disease mechanisms through network biology in the era of Next Generation Sequencing |
| topic_facet |
biology genetics high throughput sequencing human neoplasm procedures Computational Biology High-Throughput Nucleotide Sequencing Humans Neoplasms |
| description |
Characterizing the behavior of disease genes in the context of biological networks has the potential to shed light on disease mechanisms, and to reveal both new candidate disease genes and therapeutic targets. Previous studies addressing the network properties of disease genes have produced contradictory results. Here we have explored the causes of these discrepancies and assessed the relationship between the network roles of disease genes and their tolerance to deleterious germline variants in human populations leveraging on: the abundance of interactome resources, a comprehensive catalog of disease genes and exome variation data. We found that the most salient network features of disease genes are driven by cancer genes and that genes related to different types of diseases play network roles whose centrality is inversely correlated to their tolerance to likely deleterious germline mutations. This proved to be a multiscale signature, including global, mesoscopic and local network centrality features. Cancer driver genes, the most sensitive to deleterious variants, occupy the most central positions, followed by dominant disease genes and then by recessive disease genes, which are tolerant to variants and isolated within their network modules. |
| author |
Chernomoretz, Ariel Furlong, Laura Inés |
| author_facet |
Chernomoretz, Ariel Furlong, Laura Inés |
| author_sort |
Chernomoretz, Ariel |
| title |
Uncovering disease mechanisms through network biology in the era of Next Generation Sequencing |
| title_short |
Uncovering disease mechanisms through network biology in the era of Next Generation Sequencing |
| title_full |
Uncovering disease mechanisms through network biology in the era of Next Generation Sequencing |
| title_fullStr |
Uncovering disease mechanisms through network biology in the era of Next Generation Sequencing |
| title_full_unstemmed |
Uncovering disease mechanisms through network biology in the era of Next Generation Sequencing |
| title_sort |
uncovering disease mechanisms through network biology in the era of next generation sequencing |
| publishDate |
2016 |
| url |
https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_20452322_v6_n_p_Pinero http://hdl.handle.net/20.500.12110/paper_20452322_v6_n_p_Pinero |
| work_keys_str_mv |
AT chernomoretzariel uncoveringdiseasemechanismsthroughnetworkbiologyintheeraofnextgenerationsequencing AT furlonglauraines uncoveringdiseasemechanismsthroughnetworkbiologyintheeraofnextgenerationsequencing |
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1768542573092143104 |