Dataset Information

Phylogenetic inference from homologous sequence data: minimum topological assumption, strict mutational compatibility consensus tree as the ultimate solution.

ABSTRACT:

Background

For the purposes of phylogenetic inference from molecular data sets many different methods are currently offered as alternatives for researchers in phylogenetic systematics. The vast majority of these methods are based on specific topological assumptions relating to the resultant genealogical tree. Each of these has been shown to perform effectively in special conditions and for specific data sets while yielding less reliable results in other instances. Moreover, the majority of the methods include information from homoplastic characters in spite of a universally accepted agreement in their ineffectiveness for phylogenetic inference, which may often lead to inaccuracy and inconsistency. As an alternative to such methods, a strict mutational compatibility consensus tree building method as a universally applicable and reliable method is reported.

Results

The analysis of a data set from a previously published experimental phylogeny demonstrates the accuracy of the strict mutational compatibility consensus tree building method and illustrates its potential for obtaining unambiguous and precise results with full resolution.

Conclusion

The universal applicability of a simplified compatibility method in its algorithmic form for phylogenetic inference is described. Firstly, dismissal of topological assumptions creates a general potential for agreement of inferred with true phylogeny. Second, exclusion of irregular characters from analysis repeatably enables construction of consistent phylogeny. Third, a direct calculation of bootstrap proportion values for individual nodes of the resulting tree is possible rather than their empirical estimation. Finally, guidance is given for empirical assessment of the sample size necessary for full genealogical resolution and significant bootstrap proportions.

Reviewers

This article was reviewed by Yuri I. Wolf (nominated by Eugene Koonin), Arcady Mushegian and Martijn Huynen.

SUBMITTER: Stankov SV

PROVIDER: S-EPMC1409768 | biostudies-literature | 2006 Feb

REPOSITORIES: biostudies-literature

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Publications

Phylogenetic inference from homologous sequence data: minimum topological assumption, strict mutational compatibility consensus tree as the ultimate solution.

Stankov Srdan V SV

Biology direct 20060215

<h4>Background</h4>For the purposes of phylogenetic inference from molecular data sets many different methods are currently offered as alternatives for researchers in phylogenetic systematics. The vast majority of these methods are based on specific topological assumptions relating to the resultant genealogical tree. Each of these has been shown to perform effectively in special conditions and for specific data sets while yielding less reliable results in other instances. Moreover, the majority ...[more]

PMID: 16542008

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Project description:MotivationMost functions within the cell emerge thanks to protein-protein interactions (PPIs), yet experimental determination of PPIs is both expensive and time-consuming. PPI networks present significant levels of noise and incompleteness. Predicting interactions using only PPI-network topology (topological prediction) is difficult but essential when prior biological knowledge is absent or unreliable.MethodsNetwork embedding emphasizes the relations between network proteins embedded in a low-dimensional space, in which protein pairs that are closer to each other represent good candidate interactions. To achieve network denoising, which boosts prediction performance, we first applied minimum curvilinear embedding (MCE), and then adopted shortest path (SP) in the reduced space to assign likelihood scores to candidate interactions. Furthermore, we introduce (i) a new valid variation of MCE, named non-centred MCE (ncMCE); (ii) two automatic strategies for selecting the appropriate embedding dimension; and (iii) two new randomized procedures for evaluating predictions.ResultsWe compared our method against several unsupervised and supervisedly tuned embedding approaches and node neighbourhood techniques. Despite its computational simplicity, ncMCE-SP was the overall leader, outperforming the current methods in topological link prediction.ConclusionMinimum curvilinearity is a valuable non-linear framework that we successfully applied to the embedding of protein networks for the unsupervised prediction of novel PPIs. The rationale for our approach is that biological and evolutionary information is imprinted in the non-linear patterns hidden behind the protein network topology, and can be exploited for predicting new protein links. The predicted PPIs represent good candidates for testing in high-throughput experiments or for exploitation in systems biology tools such as those used for network-based inference and prediction of disease-related functional modules.Availabilityhttps://sites.google.com/site/carlovittoriocannistraci/home.Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

Phylogenetic inference from homologous sequence data: minimum topological assumption, strict mutational compatibility consensus tree as the ultimate solution.

Background

Results

Conclusion

Reviewers

Publications

Phylogenetic inference from homologous sequence data: minimum topological assumption, strict mutational compatibility consensus tree as the ultimate solution.

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