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Genes2FANs: connecting genes through functional association networks.

Dannenfelser R, Clark NR, Ma'ayan A - BMC Bioinformatics (2012)

Bottom Line: However, besides physical or co-expression interactions there are many ways in which pairs of genes, or their protein products, can be associated.In addition, users can enter any PubMed search term and then the system automatically converts the returned results to gene lists using GeneRIF.Combining functional association interactions with physical PPIs can be useful for revealing new biology and help form hypotheses for further experimentation.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Pharmacology and Systems Therapeutics, Systems Biology Center of New York, Mount Sinai School of Medicine, New York, NY 10029, USA.

ABSTRACT

Background: Protein-protein, cell signaling, metabolic, and transcriptional interaction networks are useful for identifying connections between lists of experimentally identified genes/proteins. However, besides physical or co-expression interactions there are many ways in which pairs of genes, or their protein products, can be associated. By systematically incorporating knowledge on shared properties of genes from diverse sources to build functional association networks (FANs), researchers may be able to identify additional functional interactions between groups of genes that are not readily apparent.

Results: Genes2FANs is a web based tool and a database that utilizes 14 carefully constructed FANs and a large-scale protein-protein interaction (PPI) network to build subnetworks that connect lists of human and mouse genes. The FANs are created from mammalian gene set libraries where mouse genes are converted to their human orthologs. The tool takes as input a list of human or mouse Entrez gene symbols to produce a subnetwork and a ranked list of intermediate genes that are used to connect the query input list. In addition, users can enter any PubMed search term and then the system automatically converts the returned results to gene lists using GeneRIF. This gene list is then used as input to generate a subnetwork from the user's PubMed query. As a case study, we applied Genes2FANs to connect disease genes from 90 well-studied disorders. We find an inverse correlation between the counts of links connecting disease genes through PPI and links connecting diseases genes through FANs, separating diseases into two categories.

Conclusions: Genes2FANs is a useful tool for interpreting the relationships between gene/protein lists in the context of their various functions and networks. Combining functional association interactions with physical PPIs can be useful for revealing new biology and help form hypotheses for further experimentation. Our finding that disease genes in many cancers are mostly connected through PPIs whereas other complex diseases, such as autism and type-2 diabetes, are mostly connected through FANs without PPIs, can guide better strategies for disease gene discovery. Genes2FANs is available at: http://actin.pharm.mssm.edu/genes2FANs.

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Related in: MedlinePlus

Converting PubMed queries to lists of Entrez gene symbols. PubMed queries are first converted into a list of PubMed IDs using NCBI’s e-utilities. For each PubMed ID a list of genes is obtained using GeneRIF. Genes are tallied and sorted by their occurrence and the top N genes are uploaded automatically into Genes2FANs.
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Figure 5: Converting PubMed queries to lists of Entrez gene symbols. PubMed queries are first converted into a list of PubMed IDs using NCBI’s e-utilities. For each PubMed ID a list of genes is obtained using GeneRIF. Genes are tallied and sorted by their occurrence and the top N genes are uploaded automatically into Genes2FANs.

Mentions: The Genes2FANs web interface was developed using PHP, JavaScript, AJAX, and Perl. The core code for building subnetworks is implemented in C with a custom built hash function for fast access of network nodes and links. FNV, the subnetwork viewer, was implemented using Adobe Action Script 3.0 [19]. Currently, the application resides on a Linux server running Apache. To begin an analysis, users can enter a gene list by adding Entrez gene symbols one at a time or by pasting a list for upload. Results are presented to the user as an interactive subnetwork diagram and a table containing intermediate genes with z-scores indicating how significant the intermediates are for the input gene list. The interactive resultant subnetwork allows users to reposition nodes, hover over edges to reveal the gene sets that contributed to the edge, as well as pan and zoom. Users are presented with a choice of FANs to include and several options to control the size and aesthetics of the resulting subnetworks. Intermediate genes are displayed in a table ordered by their z-score computed using a Binomial Proportion test. There are also various export options allowing users to save the network for offline analysis. Figure 5 shows a screenshot of the web interface.


Genes2FANs: connecting genes through functional association networks.

Dannenfelser R, Clark NR, Ma'ayan A - BMC Bioinformatics (2012)

Converting PubMed queries to lists of Entrez gene symbols. PubMed queries are first converted into a list of PubMed IDs using NCBI’s e-utilities. For each PubMed ID a list of genes is obtained using GeneRIF. Genes are tallied and sorted by their occurrence and the top N genes are uploaded automatically into Genes2FANs.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3472228&req=5

Figure 5: Converting PubMed queries to lists of Entrez gene symbols. PubMed queries are first converted into a list of PubMed IDs using NCBI’s e-utilities. For each PubMed ID a list of genes is obtained using GeneRIF. Genes are tallied and sorted by their occurrence and the top N genes are uploaded automatically into Genes2FANs.
Mentions: The Genes2FANs web interface was developed using PHP, JavaScript, AJAX, and Perl. The core code for building subnetworks is implemented in C with a custom built hash function for fast access of network nodes and links. FNV, the subnetwork viewer, was implemented using Adobe Action Script 3.0 [19]. Currently, the application resides on a Linux server running Apache. To begin an analysis, users can enter a gene list by adding Entrez gene symbols one at a time or by pasting a list for upload. Results are presented to the user as an interactive subnetwork diagram and a table containing intermediate genes with z-scores indicating how significant the intermediates are for the input gene list. The interactive resultant subnetwork allows users to reposition nodes, hover over edges to reveal the gene sets that contributed to the edge, as well as pan and zoom. Users are presented with a choice of FANs to include and several options to control the size and aesthetics of the resulting subnetworks. Intermediate genes are displayed in a table ordered by their z-score computed using a Binomial Proportion test. There are also various export options allowing users to save the network for offline analysis. Figure 5 shows a screenshot of the web interface.

Bottom Line: However, besides physical or co-expression interactions there are many ways in which pairs of genes, or their protein products, can be associated.In addition, users can enter any PubMed search term and then the system automatically converts the returned results to gene lists using GeneRIF.Combining functional association interactions with physical PPIs can be useful for revealing new biology and help form hypotheses for further experimentation.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Pharmacology and Systems Therapeutics, Systems Biology Center of New York, Mount Sinai School of Medicine, New York, NY 10029, USA.

ABSTRACT

Background: Protein-protein, cell signaling, metabolic, and transcriptional interaction networks are useful for identifying connections between lists of experimentally identified genes/proteins. However, besides physical or co-expression interactions there are many ways in which pairs of genes, or their protein products, can be associated. By systematically incorporating knowledge on shared properties of genes from diverse sources to build functional association networks (FANs), researchers may be able to identify additional functional interactions between groups of genes that are not readily apparent.

Results: Genes2FANs is a web based tool and a database that utilizes 14 carefully constructed FANs and a large-scale protein-protein interaction (PPI) network to build subnetworks that connect lists of human and mouse genes. The FANs are created from mammalian gene set libraries where mouse genes are converted to their human orthologs. The tool takes as input a list of human or mouse Entrez gene symbols to produce a subnetwork and a ranked list of intermediate genes that are used to connect the query input list. In addition, users can enter any PubMed search term and then the system automatically converts the returned results to gene lists using GeneRIF. This gene list is then used as input to generate a subnetwork from the user's PubMed query. As a case study, we applied Genes2FANs to connect disease genes from 90 well-studied disorders. We find an inverse correlation between the counts of links connecting disease genes through PPI and links connecting diseases genes through FANs, separating diseases into two categories.

Conclusions: Genes2FANs is a useful tool for interpreting the relationships between gene/protein lists in the context of their various functions and networks. Combining functional association interactions with physical PPIs can be useful for revealing new biology and help form hypotheses for further experimentation. Our finding that disease genes in many cancers are mostly connected through PPIs whereas other complex diseases, such as autism and type-2 diabetes, are mostly connected through FANs without PPIs, can guide better strategies for disease gene discovery. Genes2FANs is available at: http://actin.pharm.mssm.edu/genes2FANs.

Show MeSH
Related in: MedlinePlus