The Kernel of Maximum Agreement Subtrees

Swenson, Krister M.; Chen, Eric; Pattengale, Nicholas D.; Sankoff, David

doi:10.1007/978-3-642-21260-4_15

Cited by 6 publications

(11 citation statements)

References 29 publications

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“…4 neither the MRT nor any of the MASTs gives information about relationships among species s1 − s4, but the FST does. In fact, by itself, an MAST can be misleading [77]. -FSTs are useful for solving MAST-related problems.…”

Section: Maximum Agreement Subtrees and Majority Rule Treesmentioning

confidence: 99%

“…-FSTs are useful for solving MAST-related problems. These include finding a maximum compatible subtree [31], finding a maximum agreement supertree [35], and computing the kernel of maximum agreement subtrees [77]. -FSTs can be used to mine subtree patterns on collections of trees having leaf sets that are partially overlapping but not identical.…”

Section: Maximum Agreement Subtrees and Majority Rule Treesmentioning

confidence: 99%

“…Since, D3 and D4 have highly similar trees, the minSup value was 99% to single out the highly frequent subtrees among the frequent subtrees. The range of leaf set sizes and the number of trees reflects the typical inputs on which phylogenetic analyses involving MAST and related problems are done; see, e.g., [77,88]. In each of the datasets, EvoMiner is faster than Phylominer by a factor of up to 100 (sometimes more).…”

Section: Performance Of Evominermentioning

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: Maximum Agreement Subtrees and Majority Rule Treesmentioning

confidence: 99%

Section: Performance Of Evominermentioning

confidence: 99%

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EvoMiner: frequent subtree mining in phylogenetic databases

et al. 2013

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“…(Note the parallels between the use of majority clusters and the choice of

f \in ((], \frac{1}{2}, 1)

for MFSTs.) The MRT, though linear-time computable, is very sensitive to the presence of “rogue” taxa; that is, taxa whose positions vary widely within the input collection [25,26]. MFSTs are less sensitive to this phenomenon, because the MRT by definition must contain the entire leaf set (including the rogue taxa), whereas MFSTs have no such restriction (see Figure 3).…”

Section: Introductionmentioning

confidence: 99%

“…MFSTs are less sensitive to this phenomenon, because the MRT by definition must contain the entire leaf set (including the rogue taxa), whereas MFSTs have no such restriction (see Figure 3). The fact that MASTs are less sensitive to rogue taxa than MRTs has been well-acknowledged in the literature [25,27,28]. MFSTs, which include MASTs as a special case, are even more likely to reveal informative common substructures in the presence of rogue taxa.…”

Section: Introductionmentioning

confidence: 99%

Enumerating all maximal frequent subtrees in collections of phylogenetic trees

Deepak

Fernández-Baca

2014

Algorithms Mol Biol

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BackgroundA common problem in phylogenetic analysis is to identify frequent patterns in a collection of phylogenetic trees. The goal is, roughly, to find a subset of the species (taxa) on which all or some significant subset of the trees agree. One popular method to do so is through maximum agreement subtrees (MASTs). MASTs are also used, among other things, as a metric for comparing phylogenetic trees, computing congruence indices and to identify horizontal gene transfer events.ResultsWe give algorithms and experimental results for two approaches to identify common patterns in a collection of phylogenetic trees, one based on agreement subtrees, called maximal agreement subtrees, the other on frequent subtrees, called maximal frequent subtrees. These approaches can return subtrees on larger sets of taxa than MASTs, and can reveal new common phylogenetic relationships not present in either MASTs or the majority rule tree (a popular consensus method). Our current implementation is available on the web at https://code.google.com/p/mfst-miner/.ConclusionsOur computational results confirm that maximal agreement subtrees and all maximal frequent subtrees can reveal a more complete phylogenetic picture of the common patterns in collections of phylogenetic trees than maximum agreement subtrees; they are also often more resolved than the majority rule tree. Further, our experiments show that enumerating maximal frequent subtrees is considerably more practical than enumerating ordinary (not necessarily maximal) frequent subtrees.

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