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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.


Comparison between the conserved coexpression-based modules and those based on coexpression without conservation.(A) The number of detected gene modules against MR for the coexpression-based method (left 6 bars) and the conservation-based method (right bar). The modules are colored according to whether a module had enriched GO terms. (B) The ratio of enriched gene modules. (C) A box plot of the gene module size distribution.
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pone.0132039.g003: Comparison between the conserved coexpression-based modules and those based on coexpression without conservation.(A) The number of detected gene modules against MR for the coexpression-based method (left 6 bars) and the conservation-based method (right bar). The modules are colored according to whether a module had enriched GO terms. (B) The ratio of enriched gene modules. (C) A box plot of the gene module size distribution.

Mentions: When we used MR = 3, 5, 10, 15, 20, and 30 as cutoffs, 22, 165, 458, 600, 667, and 622 modules were detected, respectively (shown in S6 Table). We calculated the GO enrichment of the modules for each MR threshold, and found that 5/22, 41/165, 76/458, 56/600, 56/667, and 33/622 modules were enriched with at least one GO term. However, the conservation filtering method proposed in this paper detected 45 enriched modules out of 70 modules (Fig 3A), and the ratio of enriched modules based on coexpression conservation is clearly better than the ratios of enriched modules based on the non-filtering method with COXPRESdb at any MR threshold (< 41/165 with MR = 5, see Fig 3B). This observation suggests that the conservation-based method may reduce false positives to identify functional modules.


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

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

Comparison between the conserved coexpression-based modules and those based on coexpression without conservation.(A) The number of detected gene modules against MR for the coexpression-based method (left 6 bars) and the conservation-based method (right bar). The modules are colored according to whether a module had enriched GO terms. (B) The ratio of enriched gene modules. (C) A box plot of the gene module size distribution.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4493118&req=5

pone.0132039.g003: Comparison between the conserved coexpression-based modules and those based on coexpression without conservation.(A) The number of detected gene modules against MR for the coexpression-based method (left 6 bars) and the conservation-based method (right bar). The modules are colored according to whether a module had enriched GO terms. (B) The ratio of enriched gene modules. (C) A box plot of the gene module size distribution.
Mentions: When we used MR = 3, 5, 10, 15, 20, and 30 as cutoffs, 22, 165, 458, 600, 667, and 622 modules were detected, respectively (shown in S6 Table). We calculated the GO enrichment of the modules for each MR threshold, and found that 5/22, 41/165, 76/458, 56/600, 56/667, and 33/622 modules were enriched with at least one GO term. However, the conservation filtering method proposed in this paper detected 45 enriched modules out of 70 modules (Fig 3A), and the ratio of enriched modules based on coexpression conservation is clearly better than the ratios of enriched modules based on the non-filtering method with COXPRESdb at any MR threshold (< 41/165 with MR = 5, see Fig 3B). This observation suggests that the conservation-based method may reduce false positives to identify functional modules.

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.