scholarly journals Polynomial Supertree Methods Revisited

2011 ◽  
Vol 2011 ◽  
pp. 1-21 ◽  
Author(s):  
Malte Brinkmeyer ◽  
Thasso Griebel ◽  
Sebastian Böcker
Keyword(s):  
Simulation Study ◽  
Phylogenetic Trees ◽  
Optimization Problem ◽  
Matrix Representation ◽  
Distance Matrix ◽  
Extensive Simulation ◽  
Supertree Method ◽  
Pros And Cons ◽  
To Come ◽  
Supertree Methods

Supertree methods allow to reconstruct large phylogenetic trees by combining smaller trees with overlapping leaf sets into one, more comprehensive supertree. The most commonly used supertree method, matrix representation with parsimony (MRP), produces accurate supertrees but is rather slow due to the underlying hard optimization problem. In this paper, we present an extensive simulation study comparing the performance of MRP and the polynomial supertree methods MinCut Supertree, Modified MinCut Supertree, Build-with-distances, PhySIC, PhySIC_IST, and super distance matrix. We consider both quality and resolution of the reconstructed supertrees. Our findings illustrate the tradeoff between accuracy and running time in supertree construction, as well as the pros and cons of voting- and veto-based supertree approaches. Based on our results, we make some general suggestions for supertree methods yet to come.

Molecular and morphometric characterisation of Xiphinema globosum Sturhan, 1978 (Nematoda: Longidoridae) from Spain

Nematology ◽  
2011 ◽  
Vol 13 (1) ◽  
pp. 17-28 ◽  
Author(s):  
Blanca Landa ◽  
Carolina Cantalapiedra-Navarrete ◽  
Juan Palomares-Rius ◽  
Pablo Castillo ◽  
Carlos Gutiérrez-Gutiérrez
Keyword(s):  
Phylogenetic Trees ◽  
18S Rrna ◽  
Matrix Representation ◽  
Natural Environments ◽  
28S Rrna ◽  
Parsimony Method ◽  
Rdna Genes ◽  
Supertree Method ◽  
The Matrix ◽  
Relationship Of

AbstractDuring a recent nematode survey in natural environments of the Los Alcornocales Regional Park narrow valleys, viz., the renowned 'canutos' excavated in the mountains that maintain a humid microclimate, in southern Spain, an amphimictic population of Xiphinema globosum was identified. Morphological and morphometric studies on this population fit the original and previous descriptions and represent the first report from Spain and southern Europe. Molecular characterisation of X. globosum from Spain using D2-D3 expansion regions of 28S rRNA, 18S rRNA and ITS1-rRNA is provided and maximum likelihood and Bayesian inference analysis were used to reconstruct phylogenetic relationships within X. globosum and other Xiphinema species. A supertree solution of the different phylogenetic trees obtained in this study and in other published studies using rDNA genes are presented using the matrix representation parsimony method (MRP) and the most similar supertree method (MSSA). The results revealed a closer phylogenetic relationship of X. globosum with X. diversicaudatum, X. bakeri and with some sequences of unidentified Xiphinema spp. deposited in GenBank.

scholarly journals Optimizing phylogenetic supertrees using answer set programming

2015 ◽  
Vol 15 (4-5) ◽  
pp. 604-619 ◽  
Author(s):  
LAURA KOPONEN ◽  
EMILIA OIKARINEN ◽  
TOMI JANHUNEN ◽  
LAURA SÄILÄ
Keyword(s):  
Phylogenetic Trees ◽  
Optimization Problem ◽  
Matrix Representation ◽  
Answer Set Programming ◽  
Heuristic Methods ◽  
Construction Problem ◽  
Single Tree ◽  
Conflicting Information ◽  
The Family ◽  
Answer Set

AbstractThe supertree construction problem is about combining several phylogenetic trees with possibly conflicting information into a single tree that has all the leaves of the source trees as its leaves and the relationships between the leaves are as consistent with the source trees as possible. This leads to an optimization problem that is computationally challenging and typically heuristic methods, such as matrix representation with parsimony (MRP), are used. In this paper we consider the use of answer set programming to solve the supertree construction problem in terms of two alternative encodings. The first is based on an existing encoding of trees using substructures known as quartets, while the other novel encoding captures the relationships present in trees through direct projections. We use these encodings to compute a genus-level supertree for the family of cats (Felidae). Furthermore, we compare our results to recent supertrees obtained by the MRP method.

scholarly journals Collecting reliable clades using the Greedy Strict Consensus Merger

2015 ◽  
Author(s):  
Markus Fleischauer ◽  
Sebastian Böcker
Keyword(s):  
Computational Complexity ◽  
Phylogenetic Trees ◽  
Optimization Problems ◽  
Matrix Representation ◽  
Phylogenetic Inference ◽  
Scoring Functions ◽  
True Positive ◽  
Worst Case ◽  
Inference Methods ◽  
Supertree Methods

Supertree methods combine a set of phylogenetic trees into a single supertree. Similar to supermatrix methods, these methods provide a way to reconstruct larger parts of the Tree of Life, potentially evading the computational complexity of phylogenetic inference methods such as maximum likelihood. The supertree problem can be formalized in different ways, to cope with contradictory information in the input. Many supertree methods have been developed. Some of them solve NP-hard optimization problems like the well known Matrix Representation with Parsimony, others have polynomial worst-case running time but work in a greedy fashion (FlipCut). Both can profit from a set of clades that are already known to be part of the supertree. The Superfine approach shows how the Greedy Strict Consensus Merger (GSCM) can be used as preprocessing to find these clades. We introduce different scoring functions for the GSCM, a randomization, as well as a combination thereof to improve the GSCM to find more clades. This helps, in turn, to improve the resolution of the final supertree. We find this modifications to increase the number of true positive clades by 16% while decreasing the number of false positive clades by 3% compared to the currently used Overlap scoring.

scholarly journals Collecting reliable clades using the Greedy Strict Consensus Merger

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e2172 ◽  
Author(s):  
Markus Fleischauer ◽  
Sebastian Böcker
Keyword(s):  
Computational Complexity ◽  
Phylogenetic Trees ◽  
Optimization Problems ◽  
Matrix Representation ◽  
Phylogenetic Inference ◽  
Scoring Functions ◽  
True Positive ◽  
Worst Case ◽  
Inference Methods ◽  
Supertree Methods

Supertree methods combine a set of phylogenetic trees into a single supertree. Similar to supermatrix methods, these methods provide a way to reconstruct larger parts of the Tree of Life, potentially evading the computational complexity of phylogenetic inference methods such as maximum likelihood. The supertree problem can be formalized in different ways, to cope with contradictory information in the input. Many supertree methods have been developed. Some of them solve NP-hard optimization problems like the well-known Matrix Representation with Parsimony, while others have polynomial worst-case running time but work in a greedy fashion (FlipCut). Both can profit from a set of clades that are already known to be part of the supertree. The Superfine approach shows how the Greedy Strict Consensus Merger (GSCM) can be used as preprocessing to find these clades. We introduce different scoring functions for the GSCM, a randomization, as well as a combination thereof to improve the GSCM to find more clades. This helps, in turn, to improve the resolution of the GSCM supertree. We find this modifications to increase the number of true positive clades by 18% compared to the currently used Overlap scoring.

scholarly journals Collecting reliable clades using the Greedy Strict Consensus Merger

2015 ◽  
Author(s):  
Markus Fleischauer ◽  
Sebastian Böcker
Keyword(s):  
Computational Complexity ◽  
Phylogenetic Trees ◽  
Optimization Problems ◽  
Matrix Representation ◽  
Phylogenetic Inference ◽  
Scoring Functions ◽  
True Positive ◽  
Worst Case ◽  
Inference Methods ◽  
Supertree Methods

Supertree methods combine a set of phylogenetic trees into a single supertree. Similar to supermatrix methods, these methods provide a way to reconstruct larger parts of the Tree of Life, potentially evading the computational complexity of phylogenetic inference methods such as maximum likelihood. The supertree problem can be formalized in different ways, to cope with contradictory information in the input. Many supertree methods have been developed. Some of them solve NP-hard optimization problems like the well known Matrix Representation with Parsimony, others have polynomial worst-case running time but work in a greedy fashion (FlipCut). Both can profit from a set of clades that are already known to be part of the supertree. The Superfine approach shows how the Greedy Strict Consensus Merger (GSCM) can be used as preprocessing to find these clades. We introduce different scoring functions for the GSCM, a randomization, as well as a combination thereof to improve the GSCM to find more clades. This helps, in turn, to improve the resolution of the final supertree. We find this modifications to increase the number of true positive clades by 16% while decreasing the number of false positive clades by 3% compared to the currently used Overlap scoring.

scholarly journals Collecting reliable clades using the Greedy Strict Consensus Merger

2015 ◽  
Author(s):  
Markus Fleischauer ◽  
Sebastian Böcker
Keyword(s):  
Computational Complexity ◽  
Phylogenetic Trees ◽  
Optimization Problems ◽  
Matrix Representation ◽  
Phylogenetic Inference ◽  
Scoring Functions ◽  
True Positive ◽  
Worst Case ◽  
Inference Methods ◽  
Supertree Methods

Supertree methods combine a set of phylogenetic trees into a single supertree. Similar to supermatrix methods, these methods provide a way to reconstruct larger parts of the Tree of Life, potentially evading the computational complexity of phylogenetic inference methods such as maximum likelihood. The supertree problem can be formalized in different ways, to cope with contradictory information in the input. Many supertree methods have been developed. Some of them solve NP-hard optimization problems like the well known Matrix Representation with Parsimony, others have polynomial worst-case running time but work in a greedy fashion (FlipCut). Both can profit from a set of clades that are already known to be part of the supertree. The Superfine approach shows how the Greedy Strict Consensus Merger (GSCM) can be used as preprocessing to find these clades. We introduce different scoring functions for the GSCM, a randomization, as well as a combination thereof to improve the GSCM to find more clades. This helps, in turn, to improve the resolution of the final supertree. We find this modifications to increase the number of true positive clades by 16% while decreasing the number of false positive clades by 3% compared to the currently used Overlap scoring.

scholarly journals Collecting reliable clades using the Greedy Strict Consensus Merger

2015 ◽  
Author(s):  
Markus Fleischauer ◽  
Sebastian Böcker
Keyword(s):  
Computational Complexity ◽  
Phylogenetic Trees ◽  
Optimization Problems ◽  
Matrix Representation ◽  
Phylogenetic Inference ◽  
Scoring Functions ◽  
True Positive ◽  
Worst Case ◽  
Inference Methods ◽  
Supertree Methods

Supertree methods combine a set of phylogenetic trees into a single supertree. Similar to supermatrix methods, these methods provide a way to reconstruct larger parts of the Tree of Life, potentially evading the computational complexity of phylogenetic inference methods such as maximum likelihood. The supertree problem can be formalized in different ways, to cope with contradictory information in the input. Many supertree methods have been developed. Some of them solve NP-hard optimization problems like the well known Matrix Representation with Parsimony, others have polynomial worst-case running time but work in a greedy fashion (FlipCut). Both can profit from a set of clades that are already known to be part of the supertree. The Superfine approach shows how the Greedy Strict Consensus Merger (GSCM) can be used as preprocessing to find these clades. We introduce different scoring functions for the GSCM, a randomization, as well as a combination thereof to improve the GSCM to find more clades. This helps, in turn, to improve the resolution of the final supertree. We find this modifications to increase the number of true positive clades by 16% while decreasing the number of false positive clades by 3% compared to the currently used Overlap scoring.

scholarly journals Transforming variables to central normality

2021 ◽  
Author(s):  
Jakob Raymaekers ◽  
Peter J. Rousseeuw
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimator ◽  
Simulation Study ◽  
Real Data ◽  
Data Sets ◽  
Transformation Parameter ◽  
Likelihood Estimator ◽  
Extensive Simulation ◽  
Highly Sensitive

AbstractMany real data sets contain numerical features (variables) whose distribution is far from normal (Gaussian). Instead, their distribution is often skewed. In order to handle such data it is customary to preprocess the variables to make them more normal. The Box–Cox and Yeo–Johnson transformations are well-known tools for this. However, the standard maximum likelihood estimator of their transformation parameter is highly sensitive to outliers, and will often try to move outliers inward at the expense of the normality of the central part of the data. We propose a modification of these transformations as well as an estimator of the transformation parameter that is robust to outliers, so the transformed data can be approximately normal in the center and a few outliers may deviate from it. It compares favorably to existing techniques in an extensive simulation study and on real data.

Salvianolic acid B noncovalently interacts with disordered c-Myc: a computational and spectroscopic-based study

2021 ◽  
Author(s):  
Ashutosh Singh ◽  
Ankur Kumar ◽  
Prateek Kumar ◽  
Taniya Bhardwaj ◽  
Rajanish Giri ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Small Molecule ◽  
Fluorescence Lifetime ◽  
Simulation Study ◽  
Salvianolic Acid B ◽  
Binding Potential ◽  
Extensive Simulation ◽  
Salvianolic Acid ◽  
Therapeutic Properties ◽  
Biophysical Techniques

Aims: c-Myc, along with its partner MAX, regulates the expression of several genes, leading to an oncogenic phenotype. The MAX interacting interface of c-Myc is disordered and uncharacterized for small molecule binding. Salvianolic acid B possesses numerous therapeutic properties, including anticancer activity. The current study was designed to elucidate the interaction of the Sal_Ac_B with the disordered bHLH domain of c-Myc using computational and biophysical techniques. Materials & methods: The binding of Sal_Ac_B with Myc was studied using computational and biophysical techniques, including molecular docking and simulation, fluorescence lifetime, circular dichroism and anisotropy. Results & conclusions: The study demonstrated a high binding potential of Sal_Ac_B against the disordered Myc peptide. The binding of the compounds leads to an overall conformational change in Myc. Moreover, an extensive simulation study showed a stable Sal_Ac_B/Myc binding.

A DIMENSIONLESS FIT MEASURE FOR PHYLOGENETIC DISTANCE TREES

2005 ◽  
Vol 03 (06) ◽  
pp. 1429-1440 ◽  
Author(s):  
MANUEL GIL ◽  
CHRISTOPHE DESSIMOZ ◽  
GASTON H. GONNET
Keyword(s):  
Phylogenetic Trees ◽  
Distance Matrix ◽  
Phylogenetic Distance ◽  
Linear Transformations ◽  
Relative Measure ◽  
Absolute Measure ◽  
Distance Matrices ◽  
Fit Index

We present a dimensionless fit index for phylogenetic trees that have been constructed from distance matrices. It is designed to measure the quality of the fit of the data to a tree in absolute terms, independent of linear transformations on the distance matrix. The index can be used as an absolute measure to evaluate how well a set of data fits to a tree, or as a relative measure to compare different methods that are expected to produce the same tree. The usefulness of the index is demonstrated in three examples.

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