Uncovering Hidden Phylogenetic Consensus

Pattengale, Nicholas D.; Swenson, Krister M.; Moret, Bernard M. E.

doi:10.1007/978-3-642-13078-6_16

Cited by 20 publications

(24 citation statements)

References 18 publications

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“…(A similar measure was used before by Pattengale et al [61].) The denominator in (1) is simply the maximum number of internal edges a tree can have, and is used to normalize the degree of resolution across trees of different sizes.…”

Section: Maximum Agreement Subtrees and Majority Rule Treesmentioning

confidence: 99%

“…Datasets D3 and D4 were taken from published phylogenetic analyses. D3 is from the Bayesian analysis of [52], while D4 consists of bootstrap trees from [61]. Bayesian analyses and bootstrap trees are typical candidates for consensus tree algorithms [76,61]; the trees in these datasets have a very high commonality.…”

Section: Performance Of Evominermentioning

confidence: 99%

“…D3 is from the Bayesian analysis of [52], while D4 consists of bootstrap trees from [61]. Bayesian analyses and bootstrap trees are typical candidates for consensus tree algorithms [76,61]; the trees in these datasets have a very high commonality. We extracted datasets of different sizes (in terms of the number of leaves and the number of trees) from D3 and D4 by randomly selecting the required number of trees and restricting them on a random set of leaves of the required size.…”

Section: Performance Of Evominermentioning

confidence: 99%

“…Our datasets were derived from bootstrapped trees used in a previous study [61] on majority rule trees. Trees were constructed using 17 DNA alignments containing 125 up to 2,554 sequences.…”

Section: Experiments On Biological Datamentioning

confidence: 99%

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EvoMiner: frequent subtree mining in phylogenetic databases

et al. 2013

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The problem of mining collections of trees to identify common patterns, called frequent subtrees (FSTs), arises often when trying to interpret the results of phylogenetic analysis. FST mining generalizes the wellknown maximum agreement subtree problem. Here we present EvoMiner, a new algorithm for mining frequent subtrees in collections of phylogenetic trees. EvoMiner is an Apriori-like level-wise method, which uses a novel phylogeny-specific constant-time candidate generation scheme, an efficient fingerprinting-based technique for downward closure, and a lowest common ancestor based support counting step that requires neither costly subtree operations nor database traversal. Our algorithm achieves speed-ups of up to 100 times or more over Phylominer, the current state-of-the-art algorithm for mining phylogenetic trees. EvoMiner can also work in depth first enumeration mode, to use less memory at the expense of speed. We demonstrate the utility of FST mining as a way to extract meaningful phylogenetic information from collections of trees when compared to maximum agreement subtrees and majority rule trees ---two commonly used approaches in phylogenetic analysis for extracting consensus information from a collection of trees over a common leaf set. Disciplines Theory and AlgorithmsThis article is available at Iowa State University Digital Repository: http://lib.dr.iastate.edu/cs_techreports/11Noname manuscript No. The final publication is available at http://link.springer.com Abstract The problem of mining collections of trees to identify common patterns, called frequent subtrees (FSTs), arises often when trying to interpret the results of phylogenetic analysis. FST mining generalizes the well-known maximum agreement subtree problem. Here we present EvoMiner, a new algorithm for mining frequent subtrees in collections of phylogenetic trees. EvoMiner is an Apriori-like level-wise method, which uses a novel phylogeny-specific constant-time candidate generation scheme, an efficient fingerprinting-based technique for downward closure, and a lowest common ancestor based support counting step that requires neither costly subtree operations nor database traversal. Our algorithm achieves speed-ups of up to 100 times or more over Phylominer, the current state-of-the-art algorithm for mining phylogenetic trees. EvoMiner can also work in depth first enumeration mode, to use less memory at the expense of speed. We demonstrate the utility of FST mining as a way to extract meaningful phylogenetic information from collections of trees when compared to maximum agreement subtrees and majority rule trees -two commonly used approaches in phylogenetic analysis for extracting consensus information from a collection of trees over a common leaf set.

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Section: Maximum Agreement Subtrees and Majority Rule Treesmentioning

confidence: 99%

Section: Performance Of Evominermentioning

confidence: 99%

Section: Performance Of Evominermentioning

confidence: 99%

Section: Experiments On Biological Datamentioning

confidence: 99%

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EvoMiner: frequent subtree mining in phylogenetic databases

et al. 2013

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“…Since then, a large body of work by Wilkinson and others has grown on the subjects of finding a single representative tree [32,33,34,31,11,24] or something other than a tree (forest, network, etc.) [2,17,9,15,26].…”

Section: Introductionmentioning

confidence: 99%

The Kernel of Maximum Agreement Subtrees

Swenson

Chen

Pattengale

et al. 2012

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Abstract.A Maximum Agreement SubTree (MAST) is a largest subtree common to a set of trees and serves as a summary of common substructure in the trees. A single MAST can be misleading, however, since there can be an exponential number of MASTs, and two MASTs for the same tree set do not even necessarily share any leaves. In this paper we introduce the notion of the Kernel Agreement SubTree (KAST), which is the summary of the common substructure in all MASTs, and show that it can be calculated in polynomial time (for trees with bounded degree). Suppose the input trees represent competing hypotheses for a particular phylogeny. We show the utility of the KAST as a method to discern the common structure of confidence, and as a measure of how confident we are in a given tree set.

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Automated Plausibility Analysis of Large Phylogenies

Dao

Flouri

Stamatakis

2015

Pattern Recognition in Computational Molecular Biology

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Our ability to infer large phylogenetic trees that contain up to tens of thousands of species, gives rise to novel challenges in computational phylogenetics. Phylogenetic accuracy decreases as we add more and more taxa while keeping the number of sites in an alignment fixed. In addition, it is difficult, if not impossible to visually inspect such large phylogenies in order to determine whether they "make sense" or not. So, how can we assess the biological plausibility of a huge phylogeny? Here, we develop an automated plausibility assessment approach and introduce the algorithm Plausibility-Check. Our algorithm tries to determine whether a comprehensive phylogenetic tree T is plausible or not by comparing it to a set of substantially smaller and hence, more trustworthy, reference trees that contain a proper subset of the taxa in T . This is done by calculating and averaging over all topological distances between the small reference trees and the large tree. The most compute-intensive operation is the extracting of the subtrees that are induced by the taxon (sub-)set of a small reference tree in the large tree T . This is a prerequisite for calculating the topological distance between the (Title, Edition).

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Uncovering Hidden Phylogenetic Consensus

Cited by 20 publications

References 18 publications

EvoMiner: frequent subtree mining in phylogenetic databases

EvoMiner: frequent subtree mining in phylogenetic databases

The Kernel of Maximum Agreement Subtrees

Automated Plausibility Analysis of Large Phylogenies

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