[This is an old blog written on Monday, January 30, 2017]
The protein-protein interaction (PPI) network analysis was introduced into Metascape (http://www.metascape.org) on Nov 2, 2016. We initially relied on BioGRID [1] as the proxy of all public-domain physical protein-protein interaction data sources. BioGRID contains over 200k unique human PPI interactions, it is well maintained and frequently updated. Its coverage of all organisms fit very well with our goal to support key model organisms.
The quality of the PPI network analysis certainly depends on the underlying PPI database, therefore, we have been keeping an eye on new PPI data sources. Two recent members caught our attention: OmniPath [2] and InWeb_IM [3], published in Nature Method at the end of 2016. OminPath focuses on human signal-interactions from literature-curated signal pathways, while InWeb_IM focuses on integrating and scoring physical PPI pairs from eight resources (BIND, BioGRID, DIP, IntAct, MatrixDB, NetPath, Reactome, WikiPathways). In addition, InWeb_IM also uses a conservative ortholog mapping approach to “transfer” some interactions from non-human to human.
The following is the Venn diagram showing the overlap of unique human physical PPI pairs among the three databases. Metascape now uses the combined database (~600k) that triples the number of human interactions provided by BioGRID alone. Readers might notice BioGRID is one of the eight sources for InWeb_IM, then an immediate question is why there are still a portion of BioGRID not covered by InWeb_IM? Communications with the authors clarified the puzzle, InWeb_IM only retains data for the proteins that have been reviewed by UniProtKB. E.g., R9QTR3 [4] is “unreviewed” at this time. It interacts with SSX2 and SSX3 according to BioGRID, however, it has no data in InWeb_IM.
Out of curiosity, we compared the public-domain data to a commercial literature-based human PPI database. There remains a large discrepancy. Although protein-protein interactions have many dependencies, such as post-translational modifications, the time dimension – complexes are not formed until key proteins are available, etc., since the commercial database is also literature-based, the weak overlap deserves some attention. We have not done detailed analysis on this topic, nevertheless, a quick search using TLR7 as an example identified unique PPI interactions found by either sources. TLR7-MMP9 interaction was found in the commercial source supported by a co-immuno-precipitation study [5], this is a valid link missed by the public sources. Most of the InWeb_IM-only links were orginated from interactions inferred through ortholog data, understandably missed by the commercial database. TLR7-MLF1 interaction was included in the InWeb_IM release file (through UniProt ID: P58340 and Q9NYK1), indicating there is experimental support missed by the commercial source. However, this interaction pair has a confidence score of 0.148, which is considered lower than the threshold used in the InWeb_IM web tool. However, no threshold was mentioned in the InWeb_IM publication and private communications with the authors confirmed that the analyses presented in the paper were largely based on all interaction pairs; we retain all interaction pairs for Metascape analysis. We also need to point out that commercial database also contains non-PPI interactions (not included in the Venn diagram), such as protein-gene regulation, which is still meaningful for network analysis. Our initial check indicates commercial sources contains many literature-based PPI data that is missed by the public sources, while public sources provide some additional experimental data and some inferred interactions. They remain complementary.
A comprehensive PPI database is only one side of a coin for an informative PPI analysis. The PPI analysis in Metascape is rather unique in the way that we automatically apply Molecular Complex Detection (MCODE) algorithm [6] to the resultant networks to identify tightly connected network cores. This is extremely helpful when the larger network is hard to read. Metascape also automatically analyzes each network components for pathway enrichment, therefore assign them biological functions for easy interpretation. All networks identified are available in PNG, PDF and Cytoscape [7] formats. These are rather unique features compared to other online PPI analysis tools.
In summary, Metascape currently contains comprehensive public-domain PPI data sources, combined with its broad-spectrum algorithms, protein network analyses have never been better.
Protein-protein Network Analysis
[This is an old blog written on Monday, January 30, 2017]
The protein-protein interaction (PPI) network analysis was introduced into Metascape (http://www.metascape.org) on Nov 2, 2016. We initially relied on BioGRID [1] as the proxy of all public-domain physical protein-protein interaction data sources. BioGRID contains over 200k unique human PPI interactions, it is well maintained and frequently updated. Its coverage of all organisms fit very well with our goal to support key model organisms.
The quality of the PPI network analysis certainly depends on the underlying PPI database, therefore, we have been keeping an eye on new PPI data sources. Two recent members caught our attention: OmniPath [2] and InWeb_IM [3], published in Nature Method at the end of 2016. OminPath focuses on human signal-interactions from literature-curated signal pathways, while InWeb_IM focuses on integrating and scoring physical PPI pairs from eight resources (BIND, BioGRID, DIP, IntAct, MatrixDB, NetPath, Reactome, WikiPathways). In addition, InWeb_IM also uses a conservative ortholog mapping approach to “transfer” some interactions from non-human to human.
The following is the Venn diagram showing the overlap of unique human physical PPI pairs among the three databases. Metascape now uses the combined database (~600k) that triples the number of human interactions provided by BioGRID alone. Readers might notice BioGRID is one of the eight sources for InWeb_IM, then an immediate question is why there are still a portion of BioGRID not covered by InWeb_IM? Communications with the authors clarified the puzzle, InWeb_IM only retains data for the proteins that have been reviewed by UniProtKB. E.g., R9QTR3 [4] is “unreviewed” at this time. It interacts with SSX2 and SSX3 according to BioGRID, however, it has no data in InWeb_IM.
Out of curiosity, we compared the public-domain data to a commercial literature-based human PPI database. There remains a large discrepancy. Although protein-protein interactions have many dependencies, such as post-translational modifications, the time dimension – complexes are not formed until key proteins are available, etc., since the commercial database is also literature-based, the weak overlap deserves some attention. We have not done detailed analysis on this topic, nevertheless, a quick search using TLR7 as an example identified unique PPI interactions found by either sources. TLR7-MMP9 interaction was found in the commercial source supported by a co-immuno-precipitation study [5], this is a valid link missed by the public sources. Most of the InWeb_IM-only links were orginated from interactions inferred through ortholog data, understandably missed by the commercial database. TLR7-MLF1 interaction was included in the InWeb_IM release file (through UniProt ID: P58340 and Q9NYK1), indicating there is experimental support missed by the commercial source. However, this interaction pair has a confidence score of 0.148, which is considered lower than the threshold used in the InWeb_IM web tool. However, no threshold was mentioned in the InWeb_IM publication and private communications with the authors confirmed that the analyses presented in the paper were largely based on all interaction pairs; we retain all interaction pairs for Metascape analysis. We also need to point out that commercial database also contains non-PPI interactions (not included in the Venn diagram), such as protein-gene regulation, which is still meaningful for network analysis. Our initial check indicates commercial sources contains many literature-based PPI data that is missed by the public sources, while public sources provide some additional experimental data and some inferred interactions. They remain complementary.
A comprehensive PPI database is only one side of a coin for an informative PPI analysis. The PPI analysis in Metascape is rather unique in the way that we automatically apply Molecular Complex Detection (MCODE) algorithm [6] to the resultant networks to identify tightly connected network cores. This is extremely helpful when the larger network is hard to read. Metascape also automatically analyzes each network components for pathway enrichment, therefore assign them biological functions for easy interpretation. All networks identified are available in PNG, PDF and Cytoscape [7] formats. These are rather unique features compared to other online PPI analysis tools.
In summary, Metascape currently contains comprehensive public-domain PPI data sources, combined with its broad-spectrum algorithms, protein network analyses have never been better.
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