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Improved functional overview of protein complexes using inferred epistatic relationships.

Ryan C, Greene D, Guénolé A, van Attikum H, Krogan NJ, Cunningham P, Cagney G - BMC Syst Biol (2011)

Bottom Line: Epistatic Miniarray Profiling(E-MAP) quantifies the net effect on growth rate of disrupting pairs of genes, often producing phenotypes that may be more (negative epistasis) or less (positive epistasis) severe than the phenotype predicted based on single gene disruptions.We constructed an expanded epistasis map for yeast cell protein complexes and show that our new interactions increase the evidence for previously proposed inter-complex connections, and predict many new links.We validated a number of these in the laboratory, including new interactions linking the SWR-C chromatin modifying complex and the nuclear transport apparatus.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Computer Science and Informatics, University College Dublin, Ireland. colm.ryan@ucd.ie.

ABSTRACT

Background: Epistatic Miniarray Profiling(E-MAP) quantifies the net effect on growth rate of disrupting pairs of genes, often producing phenotypes that may be more (negative epistasis) or less (positive epistasis) severe than the phenotype predicted based on single gene disruptions. Epistatic interactions are important for understanding cell biology because they define relationships between individual genes, and between sets of genes involved in biochemical pathways and protein complexes. Each E-MAP screen quantifies the interactions between a logically selected subset of genes (e.g. genes whose products share a common function). Interactions that occur between genes involved in different cellular processes are not as frequently measured, yet these interactions are important for providing an overview of cellular organization.

Results: We introduce a method for combining overlapping E-MAP screens and inferring new interactions between them. We use this method to infer with high confidence 2,240 new strongly epistatic interactions and 34,469 weakly epistatic or neutral interactions. We show that accuracy of the predicted interactions approaches that of replicate experiments and that, like measured interactions, they are enriched for features such as shared biochemical pathways and knockout phenotypes. We constructed an expanded epistasis map for yeast cell protein complexes and show that our new interactions increase the evidence for previously proposed inter-complex connections, and predict many new links. We validated a number of these in the laboratory, including new interactions linking the SWR-C chromatin modifying complex and the nuclear transport apparatus.

Conclusion: Overall, our data support a modular model of yeast cell protein network organization and show how prediction methods can considerably extend the information that can be extracted from overlapping E-MAP screens.

Show MeSH
Similarity threshold vs accuracy: the impact of the similarity threshold on the accuracy of the predicted S-scores, as measured by correlation between predicted and experimentally observed values (A) and NRMSE (B). (C) is a density plot showing agreement between independent E-MAP experiments [32,34] and agreement between observed and predicted interactions at thresholds 0.8 (D), 0.6 (E) and 0.4 (F). Lines are drawn at the thresholds which have previously been used to identify 'significantly negative' and 'significantly positive' interactions [33]. Interactions in the light green boxes indicate values which should be positive or negative, which have been predicted as neutral(and vice versa). Interactions in the dark green boxes indicate values whose polarity has been switched -- significant negatives predicted as positives and vice versa.
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Figure 2: Similarity threshold vs accuracy: the impact of the similarity threshold on the accuracy of the predicted S-scores, as measured by correlation between predicted and experimentally observed values (A) and NRMSE (B). (C) is a density plot showing agreement between independent E-MAP experiments [32,34] and agreement between observed and predicted interactions at thresholds 0.8 (D), 0.6 (E) and 0.4 (F). Lines are drawn at the thresholds which have previously been used to identify 'significantly negative' and 'significantly positive' interactions [33]. Interactions in the light green boxes indicate values which should be positive or negative, which have been predicted as neutral(and vice versa). Interactions in the dark green boxes indicate values whose polarity has been switched -- significant negatives predicted as positives and vice versa.

Mentions: Both measures show a similar trend (Figure 2). Beyond a minimum similarity threshold of ~0.4, there appears to be an almost linear relationship between the threshold used and the measured accuracy. Scatter plots constructed to compare predicted and experimentally observed epistasis scores show that our predictions have similar variance to independent E-MAP experiments at a correlation threshold of 0.6. Importantly, at a threshold of 0.6 the number of gene pairs misclassified into incorrect epistasis categories (positive classed as negative etc.) is very low (~1%). Thus, for our analysis we used a threshold of 0.6, preferring a smaller number of more accurate predictions to a significantly larger number of less accurate predictions (~35,000 vs ~160,000). However, all predictions made with a threshold of 0.4 and above are given in additional files 1, 2 and 3.


Improved functional overview of protein complexes using inferred epistatic relationships.

Ryan C, Greene D, Guénolé A, van Attikum H, Krogan NJ, Cunningham P, Cagney G - BMC Syst Biol (2011)

Similarity threshold vs accuracy: the impact of the similarity threshold on the accuracy of the predicted S-scores, as measured by correlation between predicted and experimentally observed values (A) and NRMSE (B). (C) is a density plot showing agreement between independent E-MAP experiments [32,34] and agreement between observed and predicted interactions at thresholds 0.8 (D), 0.6 (E) and 0.4 (F). Lines are drawn at the thresholds which have previously been used to identify 'significantly negative' and 'significantly positive' interactions [33]. Interactions in the light green boxes indicate values which should be positive or negative, which have been predicted as neutral(and vice versa). Interactions in the dark green boxes indicate values whose polarity has been switched -- significant negatives predicted as positives and vice versa.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3117733&req=5

Figure 2: Similarity threshold vs accuracy: the impact of the similarity threshold on the accuracy of the predicted S-scores, as measured by correlation between predicted and experimentally observed values (A) and NRMSE (B). (C) is a density plot showing agreement between independent E-MAP experiments [32,34] and agreement between observed and predicted interactions at thresholds 0.8 (D), 0.6 (E) and 0.4 (F). Lines are drawn at the thresholds which have previously been used to identify 'significantly negative' and 'significantly positive' interactions [33]. Interactions in the light green boxes indicate values which should be positive or negative, which have been predicted as neutral(and vice versa). Interactions in the dark green boxes indicate values whose polarity has been switched -- significant negatives predicted as positives and vice versa.
Mentions: Both measures show a similar trend (Figure 2). Beyond a minimum similarity threshold of ~0.4, there appears to be an almost linear relationship between the threshold used and the measured accuracy. Scatter plots constructed to compare predicted and experimentally observed epistasis scores show that our predictions have similar variance to independent E-MAP experiments at a correlation threshold of 0.6. Importantly, at a threshold of 0.6 the number of gene pairs misclassified into incorrect epistasis categories (positive classed as negative etc.) is very low (~1%). Thus, for our analysis we used a threshold of 0.6, preferring a smaller number of more accurate predictions to a significantly larger number of less accurate predictions (~35,000 vs ~160,000). However, all predictions made with a threshold of 0.4 and above are given in additional files 1, 2 and 3.

Bottom Line: Epistatic Miniarray Profiling(E-MAP) quantifies the net effect on growth rate of disrupting pairs of genes, often producing phenotypes that may be more (negative epistasis) or less (positive epistasis) severe than the phenotype predicted based on single gene disruptions.We constructed an expanded epistasis map for yeast cell protein complexes and show that our new interactions increase the evidence for previously proposed inter-complex connections, and predict many new links.We validated a number of these in the laboratory, including new interactions linking the SWR-C chromatin modifying complex and the nuclear transport apparatus.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Computer Science and Informatics, University College Dublin, Ireland. colm.ryan@ucd.ie.

ABSTRACT

Background: Epistatic Miniarray Profiling(E-MAP) quantifies the net effect on growth rate of disrupting pairs of genes, often producing phenotypes that may be more (negative epistasis) or less (positive epistasis) severe than the phenotype predicted based on single gene disruptions. Epistatic interactions are important for understanding cell biology because they define relationships between individual genes, and between sets of genes involved in biochemical pathways and protein complexes. Each E-MAP screen quantifies the interactions between a logically selected subset of genes (e.g. genes whose products share a common function). Interactions that occur between genes involved in different cellular processes are not as frequently measured, yet these interactions are important for providing an overview of cellular organization.

Results: We introduce a method for combining overlapping E-MAP screens and inferring new interactions between them. We use this method to infer with high confidence 2,240 new strongly epistatic interactions and 34,469 weakly epistatic or neutral interactions. We show that accuracy of the predicted interactions approaches that of replicate experiments and that, like measured interactions, they are enriched for features such as shared biochemical pathways and knockout phenotypes. We constructed an expanded epistasis map for yeast cell protein complexes and show that our new interactions increase the evidence for previously proposed inter-complex connections, and predict many new links. We validated a number of these in the laboratory, including new interactions linking the SWR-C chromatin modifying complex and the nuclear transport apparatus.

Conclusion: Overall, our data support a modular model of yeast cell protein network organization and show how prediction methods can considerably extend the information that can be extracted from overlapping E-MAP screens.

Show MeSH