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InPrePPI: an integrated evaluation method based on genomic context for predicting protein-protein interactions in prokaryotic genomes.

Sun J, Sun Y, Ding G, Liu Q, Wang C, He Y, Shi T, Li Y, Zhao Z - BMC Bioinformatics (2007)

Bottom Line: After realizing the limited power in the prediction using only one genomic feature, investigators are now moving toward integration.So far, there have been few integration studies for PPI prediction; one failed to yield appreciable improvement of prediction and the others did not conduct performance comparison.It remains unclear whether an integration of multiple genomic features can improve the PPI prediction and, if it can, how to integrate these features.

View Article: PubMed Central - HTML - PubMed

Affiliation: Virginia Institute for Psychiatric and Behavioral Genetics and Department of Psychiatry, Virginia Commonwealth University, Richmond, VA 23298, USA. jsun@vcu.edu

ABSTRACT

Background: Although many genomic features have been used in the prediction of protein-protein interactions (PPIs), frequently only one is used in a computational method. After realizing the limited power in the prediction using only one genomic feature, investigators are now moving toward integration. So far, there have been few integration studies for PPI prediction; one failed to yield appreciable improvement of prediction and the others did not conduct performance comparison. It remains unclear whether an integration of multiple genomic features can improve the PPI prediction and, if it can, how to integrate these features.

Results: In this study, we first performed a systematic evaluation on the PPI prediction in Escherichia coli (E. coli) by four genomic context based methods: the phylogenetic profile method, the gene cluster method, the gene fusion method, and the gene neighbor method. The number of predicted PPIs and the average degree in the predicted PPI networks varied greatly among the four methods. Further, no method outperformed the others when we tested using three well-defined positive datasets from the KEGG, EcoCyc, and DIP databases. Based on these comparisons, we developed a novel integrated method, named InPrePPI. InPrePPI first normalizes the AC value (an integrated value of the accuracy and coverage) of each method using three positive datasets, then calculates a weight for each method, and finally uses the weight to calculate an integrated score for each protein pair predicted by the four genomic context based methods. We demonstrate that InPrePPI outperforms each of the four individual methods and, in general, the other two existing integrated methods: the joint observation method and the integrated prediction method in STRING. These four methods and InPrePPI are implemented in a user-friendly web interface.

Conclusion: This study evaluated the PPI prediction by four genomic context based methods, and presents an integrated evaluation method that shows better performance in E. coli.

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Comparison of PPI prediction by InPrePPI and STRING using the COG annotation data. A predicted pair is treated as a true positive when its two proteins are within the same COG well-characterized category.
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Figure 5: Comparison of PPI prediction by InPrePPI and STRING using the COG annotation data. A predicted pair is treated as a true positive when its two proteins are within the same COG well-characterized category.

Mentions: Protein annotations of Clusters of Orthologous Groups (COG) have been used in the assessment of PPI prediction [33,34]. Here we used COG annotations for E. coli K12 proteins to assess the prediction performance by InPrePPI and STRING. There are 25 COG functional categories, including 22 well-characterized and 3 poorly characterized or unknown categories. A predicted pair is counted as a true positive when its two proteins are within the same COG well-characterized category and as a false positive otherwise. The fractions of true positives were 0.408 (487 true positives over the 1,194 predicted pairs, 487/1,194) for InPrePPI_high, 0.356 (1,926/5,403) for InPrePPI_medium, and 0.139 (6,722/48,314) for InPrePPI_low, respectively, while the corresponding fractions in STRING were 0.280 (639/2,279) for STRING_high, 0.091 (407/4,458) for STRING_medium, and 0.065 (644/9,970) for STRING_low. Based on this metric, InPrePPI had better prediction performance than STRING (Figure 5).


InPrePPI: an integrated evaluation method based on genomic context for predicting protein-protein interactions in prokaryotic genomes.

Sun J, Sun Y, Ding G, Liu Q, Wang C, He Y, Shi T, Li Y, Zhao Z - BMC Bioinformatics (2007)

Comparison of PPI prediction by InPrePPI and STRING using the COG annotation data. A predicted pair is treated as a true positive when its two proteins are within the same COG well-characterized category.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2238723&req=5

Figure 5: Comparison of PPI prediction by InPrePPI and STRING using the COG annotation data. A predicted pair is treated as a true positive when its two proteins are within the same COG well-characterized category.
Mentions: Protein annotations of Clusters of Orthologous Groups (COG) have been used in the assessment of PPI prediction [33,34]. Here we used COG annotations for E. coli K12 proteins to assess the prediction performance by InPrePPI and STRING. There are 25 COG functional categories, including 22 well-characterized and 3 poorly characterized or unknown categories. A predicted pair is counted as a true positive when its two proteins are within the same COG well-characterized category and as a false positive otherwise. The fractions of true positives were 0.408 (487 true positives over the 1,194 predicted pairs, 487/1,194) for InPrePPI_high, 0.356 (1,926/5,403) for InPrePPI_medium, and 0.139 (6,722/48,314) for InPrePPI_low, respectively, while the corresponding fractions in STRING were 0.280 (639/2,279) for STRING_high, 0.091 (407/4,458) for STRING_medium, and 0.065 (644/9,970) for STRING_low. Based on this metric, InPrePPI had better prediction performance than STRING (Figure 5).

Bottom Line: After realizing the limited power in the prediction using only one genomic feature, investigators are now moving toward integration.So far, there have been few integration studies for PPI prediction; one failed to yield appreciable improvement of prediction and the others did not conduct performance comparison.It remains unclear whether an integration of multiple genomic features can improve the PPI prediction and, if it can, how to integrate these features.

View Article: PubMed Central - HTML - PubMed

Affiliation: Virginia Institute for Psychiatric and Behavioral Genetics and Department of Psychiatry, Virginia Commonwealth University, Richmond, VA 23298, USA. jsun@vcu.edu

ABSTRACT

Background: Although many genomic features have been used in the prediction of protein-protein interactions (PPIs), frequently only one is used in a computational method. After realizing the limited power in the prediction using only one genomic feature, investigators are now moving toward integration. So far, there have been few integration studies for PPI prediction; one failed to yield appreciable improvement of prediction and the others did not conduct performance comparison. It remains unclear whether an integration of multiple genomic features can improve the PPI prediction and, if it can, how to integrate these features.

Results: In this study, we first performed a systematic evaluation on the PPI prediction in Escherichia coli (E. coli) by four genomic context based methods: the phylogenetic profile method, the gene cluster method, the gene fusion method, and the gene neighbor method. The number of predicted PPIs and the average degree in the predicted PPI networks varied greatly among the four methods. Further, no method outperformed the others when we tested using three well-defined positive datasets from the KEGG, EcoCyc, and DIP databases. Based on these comparisons, we developed a novel integrated method, named InPrePPI. InPrePPI first normalizes the AC value (an integrated value of the accuracy and coverage) of each method using three positive datasets, then calculates a weight for each method, and finally uses the weight to calculate an integrated score for each protein pair predicted by the four genomic context based methods. We demonstrate that InPrePPI outperforms each of the four individual methods and, in general, the other two existing integrated methods: the joint observation method and the integrated prediction method in STRING. These four methods and InPrePPI are implemented in a user-friendly web interface.

Conclusion: This study evaluated the PPI prediction by four genomic context based methods, and presents an integrated evaluation method that shows better performance in E. coli.

Show MeSH
Related in: MedlinePlus