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MyProteinNet: build up-to-date protein interaction networks for organisms, tissues and user-defined contexts.

Basha O, Flom D, Barshir R, Smoly I, Tirman S, Yeger-Lotem E - Nucleic Acids Res. (2015)

Bottom Line: They can score the interactions based on reliability and filter them by user-defined contexts including molecular expression and protein annotation.The output of MyProteinNet includes the generic and filtered interactome files, together with a summary of their network attributes.MyProteinNet is particularly geared toward building human tissue interactomes, by maintaining tissue expression profiles from multiple resources.

View Article: PubMed Central - PubMed

Affiliation: Department of Clinical Biochemistry & Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel.

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Related in: MedlinePlus

A flow chart of MyProteinNet analysis. The user selects databases, PPI scoring scheme and cellular context. Based on this selection, MyProteinNet constructs a generic interactome containing all mapped PPIs, scores each PPI based on the detection method(s) and then filters PPIs. This results in a weighted, context-sensitive interactome that is accompanied by its characteristic network measurements.
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Figure 1: A flow chart of MyProteinNet analysis. The user selects databases, PPI scoring scheme and cellular context. Based on this selection, MyProteinNet constructs a generic interactome containing all mapped PPIs, scores each PPI based on the detection method(s) and then filters PPIs. This results in a weighted, context-sensitive interactome that is accompanied by its characteristic network measurements.

Mentions: MyProteinNet uses a Bayesian scoring scheme published previously (22,35). This scheme works in two steps. In the first step, given a specific subset of GO terms (33), e.g. response pathways, the scheme creates positive and negative sets of PPIs as follows. The positive set includes PPIs between proteins annotated to these GO terms and the negative set includes PPIs between proteins that were not annotated to these GO terms. The score of each detection method is then computed based on the ratios of positive to negative PPIs that the detection method identified. In the second step the weight of each PPI is calculated according to the detection methods that identified it using a Bayesian computation (21). Since some detection methods identify only few PPIs, these methods and the corresponding PPIs cannot be scored reliably. To overcome this limitation, we manually combined detection methods that differ only slightly. For example, anti bait coimmunopercipitation (MI:0006) and anti tag coimmunopercipitation (MI:0007) were both grouped as coimmunopercipitation methods. MyProteinNet thus scores 37 combined detection methods (Supplementary Table S2). A diagram describing the distribution of PPI weights is provided as part of the output (FigureĀ 1).


MyProteinNet: build up-to-date protein interaction networks for organisms, tissues and user-defined contexts.

Basha O, Flom D, Barshir R, Smoly I, Tirman S, Yeger-Lotem E - Nucleic Acids Res. (2015)

A flow chart of MyProteinNet analysis. The user selects databases, PPI scoring scheme and cellular context. Based on this selection, MyProteinNet constructs a generic interactome containing all mapped PPIs, scores each PPI based on the detection method(s) and then filters PPIs. This results in a weighted, context-sensitive interactome that is accompanied by its characteristic network measurements.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4489290&req=5

Figure 1: A flow chart of MyProteinNet analysis. The user selects databases, PPI scoring scheme and cellular context. Based on this selection, MyProteinNet constructs a generic interactome containing all mapped PPIs, scores each PPI based on the detection method(s) and then filters PPIs. This results in a weighted, context-sensitive interactome that is accompanied by its characteristic network measurements.
Mentions: MyProteinNet uses a Bayesian scoring scheme published previously (22,35). This scheme works in two steps. In the first step, given a specific subset of GO terms (33), e.g. response pathways, the scheme creates positive and negative sets of PPIs as follows. The positive set includes PPIs between proteins annotated to these GO terms and the negative set includes PPIs between proteins that were not annotated to these GO terms. The score of each detection method is then computed based on the ratios of positive to negative PPIs that the detection method identified. In the second step the weight of each PPI is calculated according to the detection methods that identified it using a Bayesian computation (21). Since some detection methods identify only few PPIs, these methods and the corresponding PPIs cannot be scored reliably. To overcome this limitation, we manually combined detection methods that differ only slightly. For example, anti bait coimmunopercipitation (MI:0006) and anti tag coimmunopercipitation (MI:0007) were both grouped as coimmunopercipitation methods. MyProteinNet thus scores 37 combined detection methods (Supplementary Table S2). A diagram describing the distribution of PPI weights is provided as part of the output (FigureĀ 1).

Bottom Line: They can score the interactions based on reliability and filter them by user-defined contexts including molecular expression and protein annotation.The output of MyProteinNet includes the generic and filtered interactome files, together with a summary of their network attributes.MyProteinNet is particularly geared toward building human tissue interactomes, by maintaining tissue expression profiles from multiple resources.

View Article: PubMed Central - PubMed

Affiliation: Department of Clinical Biochemistry & Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel.

Show MeSH
Related in: MedlinePlus