LoTo: a graphlet based method for the comparison of local topology between gene regulatory networks
One of the main challenges of the post-genomic era is the understanding of how gene expression is controlled. Changes in gene expression lay behind diverse biological phenomena such as development, disease and the adaptation to different environmental conditions. Despite the availability of well-est...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
PeerJ Inc.
2017-02-01
|
| Series: | PeerJ |
| Subjects: | |
| Online Access: | https://peerj.com/articles/3052.pdf |
| _version_ | 1827609150739185664 |
|---|---|
| author | Alberto J. Martin Sebastián Contreras-Riquelme Calixto Dominguez Tomas Perez-Acle |
| author_facet | Alberto J. Martin Sebastián Contreras-Riquelme Calixto Dominguez Tomas Perez-Acle |
| author_sort | Alberto J. Martin |
| collection | DOAJ |
| description | One of the main challenges of the post-genomic era is the understanding of how gene expression is controlled. Changes in gene expression lay behind diverse biological phenomena such as development, disease and the adaptation to different environmental conditions. Despite the availability of well-established methods to identify these changes, tools to discern how gene regulation is orchestrated are still required. The regulation of gene expression is usually depicted as a Gene Regulatory Network (GRN) where changes in the network structure (i.e., network topology) represent adjustments of gene regulation. Like other networks, GRNs are composed of basic building blocks; small induced subgraphs called graphlets. Here we present LoTo, a novel method that using Graphlet Based Metrics (GBMs) identifies topological variations between different states of a GRN. Under our approach, different states of a GRN are analyzed to determine the types of graphlet formed by all triplets of nodes in the network. Subsequently, graphlets occurring in a state of the network are compared to those formed by the same three nodes in another version of the network. Once the comparisons are performed, LoTo applies metrics from binary classification problems calculated on the existence and absence of graphlets to assess the topological similarity between both network states. Experiments performed on randomized networks demonstrate that GBMs are more sensitive to topological variation than the same metrics calculated on single edges. Additional comparisons with other common metrics demonstrate that our GBMs are capable to identify nodes whose local topology changes between different states of the network. Notably, due to the explicit use of graphlets, LoTo captures topological variations that are disregarded by other approaches. LoTo is freely available as an online web server at http://dlab.cl/loto. |
| first_indexed | 2024-03-09T07:27:08Z |
| format | Article |
| id | doaj.art-5b626fe3d49646d5be1da374cc0b982b |
| institution | Directory Open Access Journal |
| issn | 2167-8359 |
| language | English |
| last_indexed | 2024-03-09T07:27:08Z |
| publishDate | 2017-02-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ |
| spelling | doaj.art-5b626fe3d49646d5be1da374cc0b982b2023-12-03T06:52:18ZengPeerJ Inc.PeerJ2167-83592017-02-015e305210.7717/peerj.3052LoTo: a graphlet based method for the comparison of local topology between gene regulatory networksAlberto J. Martin0Sebastián Contreras-Riquelme1Calixto Dominguez2Tomas Perez-Acle3Computational Biology Laboratory (DLab), Fundacion Ciencia y Vida, Santiago, ChileComputational Biology Laboratory (DLab), Fundacion Ciencia y Vida, Santiago, ChileComputational Biology Laboratory (DLab), Fundacion Ciencia y Vida, Santiago, ChileComputational Biology Laboratory (DLab), Fundacion Ciencia y Vida, Santiago, ChileOne of the main challenges of the post-genomic era is the understanding of how gene expression is controlled. Changes in gene expression lay behind diverse biological phenomena such as development, disease and the adaptation to different environmental conditions. Despite the availability of well-established methods to identify these changes, tools to discern how gene regulation is orchestrated are still required. The regulation of gene expression is usually depicted as a Gene Regulatory Network (GRN) where changes in the network structure (i.e., network topology) represent adjustments of gene regulation. Like other networks, GRNs are composed of basic building blocks; small induced subgraphs called graphlets. Here we present LoTo, a novel method that using Graphlet Based Metrics (GBMs) identifies topological variations between different states of a GRN. Under our approach, different states of a GRN are analyzed to determine the types of graphlet formed by all triplets of nodes in the network. Subsequently, graphlets occurring in a state of the network are compared to those formed by the same three nodes in another version of the network. Once the comparisons are performed, LoTo applies metrics from binary classification problems calculated on the existence and absence of graphlets to assess the topological similarity between both network states. Experiments performed on randomized networks demonstrate that GBMs are more sensitive to topological variation than the same metrics calculated on single edges. Additional comparisons with other common metrics demonstrate that our GBMs are capable to identify nodes whose local topology changes between different states of the network. Notably, due to the explicit use of graphlets, LoTo captures topological variations that are disregarded by other approaches. LoTo is freely available as an online web server at http://dlab.cl/loto.https://peerj.com/articles/3052.pdfDifferential analysisGene Regulatory NetworkMetricGraphlet |
| spellingShingle | Alberto J. Martin Sebastián Contreras-Riquelme Calixto Dominguez Tomas Perez-Acle LoTo: a graphlet based method for the comparison of local topology between gene regulatory networks PeerJ Differential analysis Gene Regulatory Network Metric Graphlet |
| title | LoTo: a graphlet based method for the comparison of local topology between gene regulatory networks |
| title_full | LoTo: a graphlet based method for the comparison of local topology between gene regulatory networks |
| title_fullStr | LoTo: a graphlet based method for the comparison of local topology between gene regulatory networks |
| title_full_unstemmed | LoTo: a graphlet based method for the comparison of local topology between gene regulatory networks |
| title_short | LoTo: a graphlet based method for the comparison of local topology between gene regulatory networks |
| title_sort | loto a graphlet based method for the comparison of local topology between gene regulatory networks |
| topic | Differential analysis Gene Regulatory Network Metric Graphlet |
| url | https://peerj.com/articles/3052.pdf |
| work_keys_str_mv | AT albertojmartin lotoagraphletbasedmethodforthecomparisonoflocaltopologybetweengeneregulatorynetworks AT sebastiancontrerasriquelme lotoagraphletbasedmethodforthecomparisonoflocaltopologybetweengeneregulatorynetworks AT calixtodominguez lotoagraphletbasedmethodforthecomparisonoflocaltopologybetweengeneregulatorynetworks AT tomasperezacle lotoagraphletbasedmethodforthecomparisonoflocaltopologybetweengeneregulatorynetworks |