Limits...
Comparison of Gene Coexpression Profiles and Construction of Conserved Gene Networks to Find Functional Modules.

Okamura Y, Obayashi T, Kinoshita K - PLoS ONE (2015)

Bottom Line: For this purpose, the similarities of gene expression patterns and gene sequences have been separately utilized, although the combined information will provide a better solution.Some of the tightly coupled genes (modules) showed clear functional enrichment, such as immune system and cell cycle, indicating that our method could identify functionally related genes without any prior knowledge.We also found a few functional modules without any annotations, which may be good candidates for novel functional modules.

View Article: PubMed Central - PubMed

Affiliation: Graduate School of Information Sciences, Tohoku University, Sendai, Miyagi, Japan.

ABSTRACT

Background: Computational approaches toward gene annotation are a formidable challenge, now that many genome sequences have been determined. Each gene has its own function, but complicated cellular functions are achieved by sets of genes. Therefore, sets of genes with strong functional relationships must be identified. For this purpose, the similarities of gene expression patterns and gene sequences have been separately utilized, although the combined information will provide a better solution.

Result & discussion: We propose a new method to find functional modules, by comparing gene coexpression profiles among species. A coexpression pattern is represented as a list of coexpressed genes with each guide gene. We compared two coexpression lists, one from a human guide gene and the other from a homologous mouse gene, and defined a measure to evaluate the similarity between the lists. Based on this coexpression similarity, we detected the highly conserved genes, and constructed human gene networks with conserved coexpression between human and mouse. Some of the tightly coupled genes (modules) showed clear functional enrichment, such as immune system and cell cycle, indicating that our method could identify functionally related genes without any prior knowledge. We also found a few functional modules without any annotations, which may be good candidates for novel functional modules. All of the comparisons are available at the http://v1.coxsimdb.info web database.

No MeSH data available.


Detected gene networks (A) Gene networks based on coexpression conservation.We generated networks with 3,776 genes. The largest gene network contained 2,717 genes. Genes (nodes) were colored when they were a member of the top 20 largest modules with SCS = 4. Gray nodes were parts of some smaller modules, and black nodes were not parts of any modules. We prepared this picture of the network with Cytoscape ([31]). (B) The largest gene modules with SCS = 3 and the large modules with SCS = 5 are colored. This module has the representative term “immune system process”, but not all of the sub-modules with SCS = 5 have immune-related GO terms, as discussed in the text. (C) The gene network without a turning point. Since some gene networks had high coexpression conservation, no flat region was found. We used 100 instead of a turning point, because turning points cannot be defined for these genes. This network was generated from these highly conserved genes.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4493118&req=5

pone.0132039.g002: Detected gene networks (A) Gene networks based on coexpression conservation.We generated networks with 3,776 genes. The largest gene network contained 2,717 genes. Genes (nodes) were colored when they were a member of the top 20 largest modules with SCS = 4. Gray nodes were parts of some smaller modules, and black nodes were not parts of any modules. We prepared this picture of the network with Cytoscape ([31]). (B) The largest gene modules with SCS = 3 and the large modules with SCS = 5 are colored. This module has the representative term “immune system process”, but not all of the sub-modules with SCS = 5 have immune-related GO terms, as discussed in the text. (C) The gene network without a turning point. Since some gene networks had high coexpression conservation, no flat region was found. We used 100 instead of a turning point, because turning points cannot be defined for these genes. This network was generated from these highly conserved genes.

Mentions: To visualize the relationships among all of the guide genes and their CC genes, we represented them in a network style, where each node corresponds to a gene and an edge is drawn from a guide gene to a CC gene, and removed all of the unidirectional edges. (Information about the nodes and edges is provided in the Cytoscape format in the S1 File.). The resulting networks are shown in Fig 2A. The networks consisted of one large and twenty small networks.


Comparison of Gene Coexpression Profiles and Construction of Conserved Gene Networks to Find Functional Modules.

Okamura Y, Obayashi T, Kinoshita K - PLoS ONE (2015)

Detected gene networks (A) Gene networks based on coexpression conservation.We generated networks with 3,776 genes. The largest gene network contained 2,717 genes. Genes (nodes) were colored when they were a member of the top 20 largest modules with SCS = 4. Gray nodes were parts of some smaller modules, and black nodes were not parts of any modules. We prepared this picture of the network with Cytoscape ([31]). (B) The largest gene modules with SCS = 3 and the large modules with SCS = 5 are colored. This module has the representative term “immune system process”, but not all of the sub-modules with SCS = 5 have immune-related GO terms, as discussed in the text. (C) The gene network without a turning point. Since some gene networks had high coexpression conservation, no flat region was found. We used 100 instead of a turning point, because turning points cannot be defined for these genes. This network was generated from these highly conserved genes.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4493118&req=5

pone.0132039.g002: Detected gene networks (A) Gene networks based on coexpression conservation.We generated networks with 3,776 genes. The largest gene network contained 2,717 genes. Genes (nodes) were colored when they were a member of the top 20 largest modules with SCS = 4. Gray nodes were parts of some smaller modules, and black nodes were not parts of any modules. We prepared this picture of the network with Cytoscape ([31]). (B) The largest gene modules with SCS = 3 and the large modules with SCS = 5 are colored. This module has the representative term “immune system process”, but not all of the sub-modules with SCS = 5 have immune-related GO terms, as discussed in the text. (C) The gene network without a turning point. Since some gene networks had high coexpression conservation, no flat region was found. We used 100 instead of a turning point, because turning points cannot be defined for these genes. This network was generated from these highly conserved genes.
Mentions: To visualize the relationships among all of the guide genes and their CC genes, we represented them in a network style, where each node corresponds to a gene and an edge is drawn from a guide gene to a CC gene, and removed all of the unidirectional edges. (Information about the nodes and edges is provided in the Cytoscape format in the S1 File.). The resulting networks are shown in Fig 2A. The networks consisted of one large and twenty small networks.

Bottom Line: For this purpose, the similarities of gene expression patterns and gene sequences have been separately utilized, although the combined information will provide a better solution.Some of the tightly coupled genes (modules) showed clear functional enrichment, such as immune system and cell cycle, indicating that our method could identify functionally related genes without any prior knowledge.We also found a few functional modules without any annotations, which may be good candidates for novel functional modules.

View Article: PubMed Central - PubMed

Affiliation: Graduate School of Information Sciences, Tohoku University, Sendai, Miyagi, Japan.

ABSTRACT

Background: Computational approaches toward gene annotation are a formidable challenge, now that many genome sequences have been determined. Each gene has its own function, but complicated cellular functions are achieved by sets of genes. Therefore, sets of genes with strong functional relationships must be identified. For this purpose, the similarities of gene expression patterns and gene sequences have been separately utilized, although the combined information will provide a better solution.

Result & discussion: We propose a new method to find functional modules, by comparing gene coexpression profiles among species. A coexpression pattern is represented as a list of coexpressed genes with each guide gene. We compared two coexpression lists, one from a human guide gene and the other from a homologous mouse gene, and defined a measure to evaluate the similarity between the lists. Based on this coexpression similarity, we detected the highly conserved genes, and constructed human gene networks with conserved coexpression between human and mouse. Some of the tightly coupled genes (modules) showed clear functional enrichment, such as immune system and cell cycle, indicating that our method could identify functionally related genes without any prior knowledge. We also found a few functional modules without any annotations, which may be good candidates for novel functional modules. All of the comparisons are available at the http://v1.coxsimdb.info web database.

No MeSH data available.