The effects of incomplete protein interaction data on structural and evolutionary inferences

Silva, Eric de; Thorne, Thomas; Ingram, Piers J.; Agrafioti, Ino; Swire, Jonathan; Wiuf, Carsten; Stumpf, Michael

doi:10.1186/1741-7007-4-39

Cited by 60 publications

(43 citation statements)

References 46 publications

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“…The correlations between phenomic variables are typically positive (for example, highly expressed proteins also tend to interact with many other proteins and have many paralogues), whereas the correlations between phenomic and evolutionary variables are generally negative (for example, highly expressed genes on average evolve more slowly than those expressed at a low level). Most of these correlations are statistically significant but relatively weak, so caution is required (experimental biases should be investigated as potential causes 98-100 ), but the overall pattern of positive and negative correlations seems to be undeniable 97,101 . Thus, constraints on the ranges of phenomic variables partly seem to constrain the evolution of gene sequences, gene repertoires and genome architectures, as shown by the model of protein evolution discussed above.…”

Section: Constraints On Molecular Phenotypesmentioning

confidence: 99%

Constraints and plasticity in genome and molecular-phenome evolution

2010

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Multiple constraints variously affect different parts of the genomes of diverse life forms. The selective pressures that shape the evolution of viral, archaeal, bacterial and eukaryotic genomes differ markedly, even among relatively closely related animal and bacterial lineages; by contrast, constraints affecting protein evolution seem to be more universal. The constraints that shape the evolution of genomes and phenomes are complemented by the plasticity and robustness of genome architecture, expression and regulation. Taken together, these findings are starting to reveal complex networks of evolutionary processes that must be integrated to attain a new synthesis of evolutionary biology.

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Section: Constraints On Molecular Phenotypesmentioning

confidence: 99%

Constraints and plasticity in genome and molecular-phenome evolution

2010

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“…(iv) PPI detection methods are associated with technical biases and the choice of proteins tested for interaction partners introduces a study bias (10,12–13). The noisy and biased nature of the PPI networks can severely impact the biological hypotheses generated from these data (14–16). …”

Section: Introductionmentioning

confidence: 99%

HIPPIE v2.0: enhancing meaningfulness and reliability of protein–protein interaction networks

2016

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The increasing number of experimentally detected interactions between proteins makes it difficult for researchers to extract the interactions relevant for specific biological processes or diseases. This makes it necessary to accompany the large-scale detection of protein–protein interactions (PPIs) with strategies and tools to generate meaningful PPI subnetworks. To this end, we generated the Human Integrated Protein–Protein Interaction rEference or HIPPIE (http://cbdm.uni-mainz.de/hippie/). HIPPIE is a one-stop resource for the generation and interpretation of PPI networks relevant to a specific research question. We provide means to generate highly reliable, context-specific PPI networks and to make sense out of them. We just released the second major update of HIPPIE, implementing various new features. HIPPIE grew substantially over the last years and now contains more than 270 000 confidence scored and annotated PPIs. We integrated different types of experimental information for the confidence scoring and the construction of context-specific networks. We implemented basic graph algorithms that highlight important proteins and interactions. HIPPIE's graphical interface implements several ways for wet lab and computational scientists alike to access the PPI data.

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“…falciparum is displayed in Figure 5. De Silva et al [22] proposed a simple estimator of the network size based on the sampling fraction ρ of proteins that are present in the dataset. Applied to H.…”

Section: Resultsmentioning

confidence: 99%

Using Likelihood-Free Inference to Compare Evolutionary Dynamics of the Protein Networks of H. pylori and P. falciparum

et al. 2007

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Gene duplication with subsequent interaction divergence is one of the primary driving forces in the evolution of genetic systems. Yet little is known about the precise mechanisms and the role of duplication divergence in the evolution of protein networks from the prokaryote and eukaryote domains. We developed a novel, model-based approach for Bayesian inference on biological network data that centres on approximate Bayesian computation, or likelihood-free inference. Instead of computing the intractable likelihood of the protein network topology, our method summarizes key features of the network and, based on these, uses a MCMC algorithm to approximate the posterior distribution of the model parameters. This allowed us to reliably fit a flexible mixture model that captures hallmarks of evolution by gene duplication and subfunctionalization to protein interaction network data of Helicobacter pylori and Plasmodium falciparum. The 80% credible intervals for the duplication–divergence component are [0.64, 0.98] for H. pylori and [0.87, 0.99] for P. falciparum. The remaining parameter estimates are not inconsistent with sequence data. An extensive sensitivity analysis showed that incompleteness of PIN data does not largely affect the analysis of models of protein network evolution, and that the degree sequence alone barely captures the evolutionary footprints of protein networks relative to other statistics. Our likelihood-free inference approach enables a fully Bayesian analysis of a complex and highly stochastic system that is otherwise intractable at present. Modelling the evolutionary history of PIN data, it transpires that only the simultaneous analysis of several global aspects of protein networks enables credible and consistent inference to be made from available datasets. Our results indicate that gene duplication has played a larger part in the network evolution of the eukaryote than in the prokaryote, and suggests that single gene duplications with immediate divergence alone may explain more than 60% of biological network data in both domains.

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The effects of incomplete protein interaction data on structural and evolutionary inferences

Cited by 60 publications

References 46 publications

Constraints and plasticity in genome and molecular-phenome evolution

Constraints and plasticity in genome and molecular-phenome evolution

HIPPIE v2.0: enhancing meaningfulness and reliability of protein–protein interaction networks

Using Likelihood-Free Inference to Compare Evolutionary Dynamics of the Protein Networks of H. pylori and P. falciparum

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