Constrained incremental tree building: New absolute fast converging phylogeny estimation methods with improved scalability and accuracy

Qiuyi Zhang, Satish Rao, Tandy Warnow

Research output: Contribution to journalArticle

Abstract

Background: Absolute fast converging (AFC) phylogeny estimation methods are ones that have been proven to recover the true tree with high probability given sequences whose lengths are polynomial in the number of number of leaves in the tree (once the shortest and longest branch weights are fixed). While there has been a large literature on AFC methods, the best in terms of empirical performance was DCMNJ, D C M NJ, published in SODA 2001. The main empirical advantage of DCMNJ DCM NJ over other AFC methods is its use of neighbor joining (NJ) to construct trees on smaller taxon subsets, which are then combined into a tree on the full set of species using a supertree method; in contrast, the other AFC methods in essence depend on quartet trees that are computed independently of each other, which reduces accuracy compared to neighbor joining. However, DCMNJ DCM NJ is unlikely to scale to large datasets due to its reliance on supertree methods, as no current supertree methods are able to scale to large datasets with high accuracy. Results: In this study we present a new approach to large-scale phylogeny estimation that shares some of the features of DCMNJ DCM NJ but bypasses the use of supertree methods. We prove that this new approach is AFC and uses polynomial time and space. Furthermore, we describe variations on this basic approach that can be used with leaf-disjoint constraint trees (computed using methods such as maximum likelihood) to produce other methods that are likely to provide even better accuracy. Thus, we present a new generalizable technique for large-scale tree estimation that is designed to improve scalability for phylogeny estimation methods to ultra-large datasets, and that can be used in a variety of settings (including tree estimation from unaligned sequences, and species tree estimation from gene trees).

Original languageEnglish (US)
Article number2
JournalAlgorithms for Molecular Biology
Volume14
Issue number1
DOIs
StatePublished - Feb 6 2019

Fingerprint

Phylogeny
Joining
Scalability
Large Data Sets
Polynomials
Trees (mathematics)
Maximum likelihood
Leaves
Genes
Dilatation and Curettage
Maximum Likelihood
Polynomial time
Disjoint
High Accuracy
Branch
Likely
Gene
Weights and Measures

Keywords

  • Absolute fast converging methods
  • Maximum likelihood
  • Neighbor joining
  • Phylogeny estimation
  • Sample complexity
  • Short quartets

ASJC Scopus subject areas

  • Structural Biology
  • Molecular Biology
  • Computational Theory and Mathematics
  • Applied Mathematics

Cite this

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title = "Constrained incremental tree building: New absolute fast converging phylogeny estimation methods with improved scalability and accuracy",
abstract = "Background: Absolute fast converging (AFC) phylogeny estimation methods are ones that have been proven to recover the true tree with high probability given sequences whose lengths are polynomial in the number of number of leaves in the tree (once the shortest and longest branch weights are fixed). While there has been a large literature on AFC methods, the best in terms of empirical performance was DCMNJ, D C M NJ, published in SODA 2001. The main empirical advantage of DCMNJ DCM NJ over other AFC methods is its use of neighbor joining (NJ) to construct trees on smaller taxon subsets, which are then combined into a tree on the full set of species using a supertree method; in contrast, the other AFC methods in essence depend on quartet trees that are computed independently of each other, which reduces accuracy compared to neighbor joining. However, DCMNJ DCM NJ is unlikely to scale to large datasets due to its reliance on supertree methods, as no current supertree methods are able to scale to large datasets with high accuracy. Results: In this study we present a new approach to large-scale phylogeny estimation that shares some of the features of DCMNJ DCM NJ but bypasses the use of supertree methods. We prove that this new approach is AFC and uses polynomial time and space. Furthermore, we describe variations on this basic approach that can be used with leaf-disjoint constraint trees (computed using methods such as maximum likelihood) to produce other methods that are likely to provide even better accuracy. Thus, we present a new generalizable technique for large-scale tree estimation that is designed to improve scalability for phylogeny estimation methods to ultra-large datasets, and that can be used in a variety of settings (including tree estimation from unaligned sequences, and species tree estimation from gene trees).",
keywords = "Absolute fast converging methods, Maximum likelihood, Neighbor joining, Phylogeny estimation, Sample complexity, Short quartets",
author = "Qiuyi Zhang and Satish Rao and Tandy Warnow",
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doi = "10.1186/s13015-019-0136-9",
language = "English (US)",
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T2 - New absolute fast converging phylogeny estimation methods with improved scalability and accuracy

AU - Zhang, Qiuyi

AU - Rao, Satish

AU - Warnow, Tandy

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N2 - Background: Absolute fast converging (AFC) phylogeny estimation methods are ones that have been proven to recover the true tree with high probability given sequences whose lengths are polynomial in the number of number of leaves in the tree (once the shortest and longest branch weights are fixed). While there has been a large literature on AFC methods, the best in terms of empirical performance was DCMNJ, D C M NJ, published in SODA 2001. The main empirical advantage of DCMNJ DCM NJ over other AFC methods is its use of neighbor joining (NJ) to construct trees on smaller taxon subsets, which are then combined into a tree on the full set of species using a supertree method; in contrast, the other AFC methods in essence depend on quartet trees that are computed independently of each other, which reduces accuracy compared to neighbor joining. However, DCMNJ DCM NJ is unlikely to scale to large datasets due to its reliance on supertree methods, as no current supertree methods are able to scale to large datasets with high accuracy. Results: In this study we present a new approach to large-scale phylogeny estimation that shares some of the features of DCMNJ DCM NJ but bypasses the use of supertree methods. We prove that this new approach is AFC and uses polynomial time and space. Furthermore, we describe variations on this basic approach that can be used with leaf-disjoint constraint trees (computed using methods such as maximum likelihood) to produce other methods that are likely to provide even better accuracy. Thus, we present a new generalizable technique for large-scale tree estimation that is designed to improve scalability for phylogeny estimation methods to ultra-large datasets, and that can be used in a variety of settings (including tree estimation from unaligned sequences, and species tree estimation from gene trees).

AB - Background: Absolute fast converging (AFC) phylogeny estimation methods are ones that have been proven to recover the true tree with high probability given sequences whose lengths are polynomial in the number of number of leaves in the tree (once the shortest and longest branch weights are fixed). While there has been a large literature on AFC methods, the best in terms of empirical performance was DCMNJ, D C M NJ, published in SODA 2001. The main empirical advantage of DCMNJ DCM NJ over other AFC methods is its use of neighbor joining (NJ) to construct trees on smaller taxon subsets, which are then combined into a tree on the full set of species using a supertree method; in contrast, the other AFC methods in essence depend on quartet trees that are computed independently of each other, which reduces accuracy compared to neighbor joining. However, DCMNJ DCM NJ is unlikely to scale to large datasets due to its reliance on supertree methods, as no current supertree methods are able to scale to large datasets with high accuracy. Results: In this study we present a new approach to large-scale phylogeny estimation that shares some of the features of DCMNJ DCM NJ but bypasses the use of supertree methods. We prove that this new approach is AFC and uses polynomial time and space. Furthermore, we describe variations on this basic approach that can be used with leaf-disjoint constraint trees (computed using methods such as maximum likelihood) to produce other methods that are likely to provide even better accuracy. Thus, we present a new generalizable technique for large-scale tree estimation that is designed to improve scalability for phylogeny estimation methods to ultra-large datasets, and that can be used in a variety of settings (including tree estimation from unaligned sequences, and species tree estimation from gene trees).

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