Abstract
Background: Divide-and-conquer methods, which divide the species set into overlapping subsets, construct a tree on each subset, and then combine the subset trees using a supertree method, provide a key algorithmic framework for boosting the scalability of phylogeny estimation methods to large datasets. Yet the use of supertree methods, which typically attempt to solve NP-hard optimization problems, limits the scalability of such approaches. Results: In this paper, we introduce a divide-and-conquer approach that does not require supertree estimation: we divide the species set into pairwise disjoint subsets, construct a tree on each subset using a base method, and then combine the subset trees using a distance matrix. For this merger step, we present a new method, called NJMerge, which is a polynomial-time extension of Neighbor Joining (NJ); thus, NJMerge can be viewed either as a method for improving traditional NJ or as a method for scaling the base method to larger datasets. We prove that NJMerge can be used to create divide-and-conquer pipelines that are statistically consistent under some models of evolution. We also report the results of an extensive simulation study evaluating NJMerge on multi-locus datasets with up to 1000 species. We found that NJMerge sometimes improved the accuracy of traditional NJ and substantially reduced the running time of three popular species tree methods (ASTRAL-III, SVDquartets, and "concatenation" using RAxML) without sacrificing accuracy. Finally, although NJMerge can fail to return a tree, in our experiments, NJMerge failed on only 11 out of 2560 test cases. Conclusions: Theoretical and empirical results suggest that NJMerge is a valuable technique for large-scale phylogeny estimation, especially when computational resources are limited. NJMerge is freely available on Github (http://github.com/ekmolloy/njmerge).
| Original language | English (US) |
|---|---|
| Article number | 14 |
| Number of pages | 30 |
| Journal | Algorithms for Molecular Biology |
| Volume | 14 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jul 19 2019 |
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Keywords
- Divide-and-conquer
- Incomplete lineage sorting
- Neighbor Joining
- Phylogenomics
- Species trees
ASJC Scopus subject areas
- Structural Biology
- Molecular Biology
- Computational Theory and Mathematics
- Applied Mathematics
Cite this
Statistically consistent divide-and-conquer pipelines for phylogeny estimation using NJMerge. / Molloy, Erin K.; Warnow, Tandy.
In: Algorithms for Molecular Biology, Vol. 14, No. 1, 14, 19.07.2019.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Statistically consistent divide-and-conquer pipelines for phylogeny estimation using NJMerge
AU - Molloy, Erin K.
AU - Warnow, Tandy
PY - 2019/7/19
Y1 - 2019/7/19
N2 - Background: Divide-and-conquer methods, which divide the species set into overlapping subsets, construct a tree on each subset, and then combine the subset trees using a supertree method, provide a key algorithmic framework for boosting the scalability of phylogeny estimation methods to large datasets. Yet the use of supertree methods, which typically attempt to solve NP-hard optimization problems, limits the scalability of such approaches. Results: In this paper, we introduce a divide-and-conquer approach that does not require supertree estimation: we divide the species set into pairwise disjoint subsets, construct a tree on each subset using a base method, and then combine the subset trees using a distance matrix. For this merger step, we present a new method, called NJMerge, which is a polynomial-time extension of Neighbor Joining (NJ); thus, NJMerge can be viewed either as a method for improving traditional NJ or as a method for scaling the base method to larger datasets. We prove that NJMerge can be used to create divide-and-conquer pipelines that are statistically consistent under some models of evolution. We also report the results of an extensive simulation study evaluating NJMerge on multi-locus datasets with up to 1000 species. We found that NJMerge sometimes improved the accuracy of traditional NJ and substantially reduced the running time of three popular species tree methods (ASTRAL-III, SVDquartets, and "concatenation" using RAxML) without sacrificing accuracy. Finally, although NJMerge can fail to return a tree, in our experiments, NJMerge failed on only 11 out of 2560 test cases. Conclusions: Theoretical and empirical results suggest that NJMerge is a valuable technique for large-scale phylogeny estimation, especially when computational resources are limited. NJMerge is freely available on Github (http://github.com/ekmolloy/njmerge).
AB - Background: Divide-and-conquer methods, which divide the species set into overlapping subsets, construct a tree on each subset, and then combine the subset trees using a supertree method, provide a key algorithmic framework for boosting the scalability of phylogeny estimation methods to large datasets. Yet the use of supertree methods, which typically attempt to solve NP-hard optimization problems, limits the scalability of such approaches. Results: In this paper, we introduce a divide-and-conquer approach that does not require supertree estimation: we divide the species set into pairwise disjoint subsets, construct a tree on each subset using a base method, and then combine the subset trees using a distance matrix. For this merger step, we present a new method, called NJMerge, which is a polynomial-time extension of Neighbor Joining (NJ); thus, NJMerge can be viewed either as a method for improving traditional NJ or as a method for scaling the base method to larger datasets. We prove that NJMerge can be used to create divide-and-conquer pipelines that are statistically consistent under some models of evolution. We also report the results of an extensive simulation study evaluating NJMerge on multi-locus datasets with up to 1000 species. We found that NJMerge sometimes improved the accuracy of traditional NJ and substantially reduced the running time of three popular species tree methods (ASTRAL-III, SVDquartets, and "concatenation" using RAxML) without sacrificing accuracy. Finally, although NJMerge can fail to return a tree, in our experiments, NJMerge failed on only 11 out of 2560 test cases. Conclusions: Theoretical and empirical results suggest that NJMerge is a valuable technique for large-scale phylogeny estimation, especially when computational resources are limited. NJMerge is freely available on Github (http://github.com/ekmolloy/njmerge).
KW - Divide-and-conquer
KW - Incomplete lineage sorting
KW - Neighbor Joining
KW - Phylogenomics
KW - Species trees
UR - http://www.scopus.com/inward/record.url?scp=85069429732&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85069429732&partnerID=8YFLogxK
U2 - 10.1186/s13015-019-0151-x
DO - 10.1186/s13015-019-0151-x
M3 - Article
AN - SCOPUS:85069429732
VL - 14
JO - Algorithms for Molecular Biology
JF - Algorithms for Molecular Biology
SN - 1748-7188
IS - 1
M1 - 14
ER -