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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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Process of creating FANs. The process of creating FANs involves gathering datasets and processing them into GMT files. Using these GMT files, networks are created using either the Jaccard index or a Binomial Proportion test. Large and dense networks are filtered using a declustering method and a cutoff is applied to produce the final FANs.
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Figure 1: Process of creating FANs. The process of creating FANs involves gathering datasets and processing them into GMT files. Using these GMT files, networks are created using either the Jaccard index or a Binomial Proportion test. Large and dense networks are filtered using a declustering method and a cutoff is applied to produce the final FANs.

Mentions: The first step in assembling the FANs was to gather data spread across a wide variety of databases and online sources. Besides collecting a comprehensive list of available protein-protein and cell signaling networks (see below), we also collected and generated gene set libraries that we later converted to FANs. Gene set libraries store sets of genes in a gene matrix transposed (GMT) file with rows containing a set of genes symbols associated with a given functional term. Using this format we were able to quantify the relationships between pairs of genes based on their co-occurrence membership in sets of the same gene set library using two different similarity measures: the Jaccard index and a Binomial Proportion test. The process of creating FANs from GMT files is outlined (Figure‚ÄČ1).


Genes2FANs: connecting genes through functional association networks.

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

Process of creating FANs. The process of creating FANs involves gathering datasets and processing them into GMT files. Using these GMT files, networks are created using either the Jaccard index or a Binomial Proportion test. Large and dense networks are filtered using a declustering method and a cutoff is applied to produce the final FANs.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: Process of creating FANs. The process of creating FANs involves gathering datasets and processing them into GMT files. Using these GMT files, networks are created using either the Jaccard index or a Binomial Proportion test. Large and dense networks are filtered using a declustering method and a cutoff is applied to produce the final FANs.
Mentions: The first step in assembling the FANs was to gather data spread across a wide variety of databases and online sources. Besides collecting a comprehensive list of available protein-protein and cell signaling networks (see below), we also collected and generated gene set libraries that we later converted to FANs. Gene set libraries store sets of genes in a gene matrix transposed (GMT) file with rows containing a set of genes symbols associated with a given functional term. Using this format we were able to quantify the relationships between pairs of genes based on their co-occurrence membership in sets of the same gene set library using two different similarity measures: the Jaccard index and a Binomial Proportion test. The process of creating FANs from GMT files is outlined (Figure‚ÄČ1).

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