Spectral Partitioning of Phylogenetic Data Sets Based on Compatibility

Chen, Duhong; Burleigh, Gordon; Fernández-Baca, David

doi:10.1080/10635150701499571

Cited by 11 publications

(9 citation statements)

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“…Spectral partitioning subdivides characters in an alignment into a prespecified number of clusters based on character compatibility [46]. Characters in the same cluster are more phylogenetically compatible with each other than they are to characters in different clusters.…”

Section: Resultsmentioning

confidence: 99%

“…Spectral partitioning is a technique that partitions alignments based on character compatibility. More specifically, it clusters the characters with the highest average pairwise compatibility, so that characters in each cluster are more compatible with each other than they are with characters in the other clusters [46]. If the relative contribution of spectral partitions differs strongly between gene types, genomes or loci, this can be taken as evidence for conflict between them.…”

Section: Methodsmentioning

confidence: 99%

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Data mining approach identifies research priorities and data requirements for resolving the red algal tree of life

et al. 2010

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BackgroundThe assembly of the tree of life has seen significant progress in recent years but algae and protists have been largely overlooked in this effort. Many groups of algae and protists have ancient roots and it is unclear how much data will be required to resolve their phylogenetic relationships for incorporation in the tree of life. The red algae, a group of primary photosynthetic eukaryotes of more than a billion years old, provide the earliest fossil evidence for eukaryotic multicellularity and sexual reproduction. Despite this evolutionary significance, their phylogenetic relationships are understudied. This study aims to infer a comprehensive red algal tree of life at the family level from a supermatrix containing data mined from GenBank. We aim to locate remaining regions of low support in the topology, evaluate their causes and estimate the amount of data required to resolve them.ResultsPhylogenetic analysis of a supermatrix of 14 loci and 98 red algal families yielded the most complete red algal tree of life to date. Visualization of statistical support showed the presence of five poorly supported regions. Causes for low support were identified with statistics about the age of the region, data availability and node density, showing that poor support has different origins in different parts of the tree. Parametric simulation experiments yielded optimistic estimates of how much data will be needed to resolve the poorly supported regions (ca. 103 to ca. 104 nucleotides for the different regions). Nonparametric simulations gave a markedly more pessimistic image, some regions requiring more than 2.8 105 nucleotides or not achieving the desired level of support at all. The discrepancies between parametric and nonparametric simulations are discussed in light of our dataset and known attributes of both approaches.ConclusionsOur study takes the red algae one step closer to meaningful inclusion in the tree of life. In addition to the recovery of stable relationships, the recognition of five regions in need of further study is a significant outcome of this work. Based on our analyses of current availability and future requirements of data, we make clear recommendations for forthcoming research.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Data mining approach identifies research priorities and data requirements for resolving the red algal tree of life

et al. 2010

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“…In various configurations, graph theory has been successful in understanding the organizing principles behind biological phenomena at every scale, including the regulation of gene-expression (22), protein-protein interactions (23), metabolic networks (24) and ecological food webs (25). Graph theory and associated spectral analyses have also been useful in phylogenetics, particularly in developing approaches for tree inference (26) or for comparing the phylogenetic composition of microbial samples (27). Metrics like the Robinson-Foulds distance (28) and nearest neighbour interchange (29), too, for example, are used to compare different trees representing the same set of organisms, by counting the number of steps needed to transform one into the other (or both into a third); while others take a geometric approach to define polytopic contours around a reconstructed tree in order to define 'confidence regions' in the tree (30).…”

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confidence: 99%

Characterizing and comparing phylogenies from their Laplacian spectrum

Lewitus

Morlon

2015

Preprint

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Abstract.-. Phylogenetic trees are central to many areas of biology, ranging from population genetics and epidemiology to microbiology, ecology, and macroevolution. The ability to summarize properties of trees, compare different trees, and identify distinct modes of division within trees is essential to all these research areas. But despite wide-ranging applications, there currently exists no common, comprehensive framework for such analyses. Here we present a graph-theoretical approach that provides such a framework. We show how to construct the spectral density profiles of phylogenetic trees from their Laplacian graphs. Using ultrametric simulated trees as well as non-ultrametric empirical trees, we demonstrate that the spectral density successfully identifies various properties of the trees and clusters them into meaningful groups. Finally, we illustrate how the eigengap can identify modes of division within a given tree. As phylogenetic data continue to accumulate and to be integrated into various areas of the life sciences, we expect that this spectral graph-theoretical framework to phylogenetics will have powerful and long-lasting applications.phylogenetics -diversification -Influenza -phylodynamics -biodiversitymicrobiology -Laplacian . CC-BY-NC-ND 4.0 International license peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/026476 doi: bioRxiv preprint first posted online Sep. 9, 2015; Phylogenies are essential to many areas of the life sciences. In population genetics and phylogeography, they are used to infer past demography and historical migration events (1). In epidemiology, they are key to understanding how best to control the spread of infectious disease (2). In microbiology, they provide one of the most natural and powerful measures of diversity (3). Phylogenies are also increasingly effective in ecology, where they can inform our understanding of community assembly (4), interspecific interactions (5), and species responses to environmental change (6), as well as guide conservation efforts (7; 8). Finally, phylogenies are essential to comparative phylogenetics (9) and comparative genomics (10) and therefore to our understanding of diversification (11), trait evolution (12), and the genetic underpinnings of both (e.g., (13; 14)).Despite the importance of phylogenetics in the life sciences, the current techniques aimed at extracting information from phylogenies are limited. One of these techniques is built on summary statistics. In microbiology, ecology, and conservation biology, summary statistics based on measures of phylogenetic diversity, such as total phylogenetic branch length, (7; 15) are often used. In diversification analyses, traditional summary statistics quantify either the stem-to-tip (e.g. γ (16) and Lineage-Through-Time plots (17)) or lineage-to-lineage (e.g. β and Colless' index (18)) distribution of branching events across trees. These summary statistics disregard much of the data -an...

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“…Additionally, FUGA implements a greedy algorithm for community detection that uses network modularity as a measure of community structure [18], as well as several functions for spectral graph partitioning [19]. Such unsupervised algorithms are well adapted to large biological networks and may uncover previously undetected interactions.…”

Section: Resultsmentioning

confidence: 99%

Functional Genomics Assistant (FUGA): a toolbox for the analysis of complex biological networks

et al. 2011

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BackgroundCellular constituents such as proteins, DNA, and RNA form a complex web of interactions that regulate biochemical homeostasis and determine the dynamic cellular response to external stimuli. It follows that detailed understanding of these patterns is critical for the assessment of fundamental processes in cell biology and pathology. Representation and analysis of cellular constituents through network principles is a promising and popular analytical avenue towards a deeper understanding of molecular mechanisms in a system-wide context.FindingsWe present Functional Genomics Assistant (FUGA) - an extensible and portable MATLAB toolbox for the inference of biological relationships, graph topology analysis, random network simulation, network clustering, and functional enrichment statistics. In contrast to conventional differential expression analysis of individual genes, FUGA offers a framework for the study of system-wide properties of biological networks and highlights putative molecular targets using concepts of systems biology.ConclusionFUGA offers a simple and customizable framework for network analysis in a variety of systems biology applications. It is freely available for individual or academic use at http://code.google.com/p/fuga.

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Spectral Partitioning of Phylogenetic Data Sets Based on Compatibility

Cited by 11 publications

References 57 publications

Data mining approach identifies research priorities and data requirements for resolving the red algal tree of life

Data mining approach identifies research priorities and data requirements for resolving the red algal tree of life

Characterizing and comparing phylogenies from their Laplacian spectrum

Functional Genomics Assistant (FUGA): a toolbox for the analysis of complex biological networks

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