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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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Top diseases. The top 10 diseases with the greatest difference in edge counts for the PPI vs. FANs disease subnetworks made from the OMIM disease gene lists (A) and the top 20 diseases for the subnetworks made using the query PubMed function (B).
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Figure 9: Top diseases. The top 10 diseases with the greatest difference in edge counts for the PPI vs. FANs disease subnetworks made from the OMIM disease gene lists (A) and the top 20 diseases for the subnetworks made using the query PubMed function (B).

Mentions: With both methods, directly from OMIM or through PubMed queries, diseases show an inverse correlation between protein-protein interaction (PPI) links and other types of functional annotation links, segregating diseases with many known genes into two broad categories: those with gene products that physically interact, and those that interact functionally but not physically (Figures 7 and 8). This trend is statistically significant based on a Spearman rank correlation of 0.73 which has a p-value of 2.97×10-10 for the PubMed queried lists, and 0.27 for the lists directly from OMIM (p = 0.0065). The diseases that show high level of PPI and low level of functional associations include breast, ovarian, pancreatic, colorectal, thyroid, gastric, lung, and prostate cancers, as well as ataxia and leukemia (Figure 9); whereas diseases that display high level of functional interactions and low level of PPI are: deafness, type-2 diabetes mellitus, asthma, schizophrenia, autism and epilepsy. To ensure that this is not an artifact of the declustering algorithm on the FANs we ran the same process using the nine FANs before declustering. The declustering process had little effect on these results (Additional file 2: Figure S2 and Additional file 3: Figure S3) with Spearman rank correlation of 0.38 which has a p-value of 0.00026 for the PubMed queried lists, and 0.57 for the lists directly from OMIM (p = 1.99×10-7). The finding that some diseases have disease genes that are linked mostly through PPI, while other disease genes are mostly connected through FANs, is important because many investigations attempt to use protein interactions for novel disease gene discovery, for example, prioritizing mutations in genes detected by exome sequencing. This suggests that disease gene discovery using a PPI approach would work well for diseases such as cancers where many PPIs connect the disease gene products; however, for other complex diseases such as autism and type-2 diabetes, FANs would potentially be better for disease gene discovery.


Genes2FANs: connecting genes through functional association networks.

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

Top diseases. The top 10 diseases with the greatest difference in edge counts for the PPI vs. FANs disease subnetworks made from the OMIM disease gene lists (A) and the top 20 diseases for the subnetworks made using the query PubMed function (B).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 9: Top diseases. The top 10 diseases with the greatest difference in edge counts for the PPI vs. FANs disease subnetworks made from the OMIM disease gene lists (A) and the top 20 diseases for the subnetworks made using the query PubMed function (B).
Mentions: With both methods, directly from OMIM or through PubMed queries, diseases show an inverse correlation between protein-protein interaction (PPI) links and other types of functional annotation links, segregating diseases with many known genes into two broad categories: those with gene products that physically interact, and those that interact functionally but not physically (Figures 7 and 8). This trend is statistically significant based on a Spearman rank correlation of 0.73 which has a p-value of 2.97×10-10 for the PubMed queried lists, and 0.27 for the lists directly from OMIM (p = 0.0065). The diseases that show high level of PPI and low level of functional associations include breast, ovarian, pancreatic, colorectal, thyroid, gastric, lung, and prostate cancers, as well as ataxia and leukemia (Figure 9); whereas diseases that display high level of functional interactions and low level of PPI are: deafness, type-2 diabetes mellitus, asthma, schizophrenia, autism and epilepsy. To ensure that this is not an artifact of the declustering algorithm on the FANs we ran the same process using the nine FANs before declustering. The declustering process had little effect on these results (Additional file 2: Figure S2 and Additional file 3: Figure S3) with Spearman rank correlation of 0.38 which has a p-value of 0.00026 for the PubMed queried lists, and 0.57 for the lists directly from OMIM (p = 1.99×10-7). The finding that some diseases have disease genes that are linked mostly through PPI, while other disease genes are mostly connected through FANs, is important because many investigations attempt to use protein interactions for novel disease gene discovery, for example, prioritizing mutations in genes detected by exome sequencing. This suggests that disease gene discovery using a PPI approach would work well for diseases such as cancers where many PPIs connect the disease gene products; however, for other complex diseases such as autism and type-2 diabetes, FANs would potentially be better for disease gene discovery.

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