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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.


Example of the correspondence between the conservation-based method modules and the COXPRESdb-based modules.The three module pairs with the largest numbers of intersecting genes are shown. The list of all similar module pairs is provided in S7 Table.
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pone.0132039.g004: Example of the correspondence between the conservation-based method modules and the COXPRESdb-based modules.The three module pairs with the largest numbers of intersecting genes are shown. The list of all similar module pairs is provided in S7 Table.

Mentions: Some examples of similar module pairs are shown in Fig 4. The first module pair in Fig 4 and S7 Table has different representative GO terms with 25 common genes, one for “skin development” and the other has no significant term, where the size of the conservation-based module (97 = 72+25) is much larger than that of COXPRESdb (42 = 25 + 17). The larger size and the existence of the representative GO term indicate the enhanced enrichment of the related genes. The second module pair also has a larger number of genes with the representative term in the conservation-based module (35) than that of COXPRESdb (24). Since it shares the same representative GO terms, the larger number of genes with the representative GO term may indicate the presence of a smaller number of related genes outside of the module. However, the ratio of the genes with a representative GO term for the conservation-based module (0.52) is smaller than that of COXPRESdb (0.62), which indicates the inclusion of a larger number of unrelated genes in the conservation-based modules. Since the conservation charts of the large module member genes have few flat regions in a small N range, the turning points of these genes were found in a large N range. Therefore, genes that are not directly related to a representative term may be included in the detected gene module. As described above, the conservation-based modules have better ratios on average, as in the case of the third example. However, in some cases the COXPRESdb-based modules produce better modules from the viewpoint of the inclusion of falsely related genes, as in the second example. In short, coexpression conservation may reduce the number of false negatives and false positives, to detect the functionally related genes on average.


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

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

Example of the correspondence between the conservation-based method modules and the COXPRESdb-based modules.The three module pairs with the largest numbers of intersecting genes are shown. The list of all similar module pairs is provided in S7 Table.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0132039.g004: Example of the correspondence between the conservation-based method modules and the COXPRESdb-based modules.The three module pairs with the largest numbers of intersecting genes are shown. The list of all similar module pairs is provided in S7 Table.
Mentions: Some examples of similar module pairs are shown in Fig 4. The first module pair in Fig 4 and S7 Table has different representative GO terms with 25 common genes, one for “skin development” and the other has no significant term, where the size of the conservation-based module (97 = 72+25) is much larger than that of COXPRESdb (42 = 25 + 17). The larger size and the existence of the representative GO term indicate the enhanced enrichment of the related genes. The second module pair also has a larger number of genes with the representative term in the conservation-based module (35) than that of COXPRESdb (24). Since it shares the same representative GO terms, the larger number of genes with the representative GO term may indicate the presence of a smaller number of related genes outside of the module. However, the ratio of the genes with a representative GO term for the conservation-based module (0.52) is smaller than that of COXPRESdb (0.62), which indicates the inclusion of a larger number of unrelated genes in the conservation-based modules. Since the conservation charts of the large module member genes have few flat regions in a small N range, the turning points of these genes were found in a large N range. Therefore, genes that are not directly related to a representative term may be included in the detected gene module. As described above, the conservation-based modules have better ratios on average, as in the case of the third example. However, in some cases the COXPRESdb-based modules produce better modules from the viewpoint of the inclusion of falsely related genes, as in the second example. In short, coexpression conservation may reduce the number of false negatives and false positives, to detect the functionally related genes on average.

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.