Evidence map›Paper›PMID 24180377›Full record

ArticleAlgorithms for molecular biology : AMB2013

Inferring species trees from incongruent multi-copy gene trees using the Robinson-Foulds distance.

Ruchi Chaudhary, John Gordon Burleigh, David Fernández-Baca

Abstract read
In one paragraph

Article in Algorithms for molecular biology : AMB, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

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17citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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3 · Its place in the literature

Who cites it

17 citing papers in PubMed.

  1. Article
  2. Article
  3. Phylogenetic reconciliation.PLoS computational biology · 2022
    Article
  4. Article
  5. Article
  6. Phylogenetic tree building in the genomic age.Nature reviews. Genetics · 2020
    Review
  7. Non-parametric correction of estimated gene trees using TRACTION.Algorithms for molecular biology : AMB · 2020
    Article
  8. Article
  9. Article
  10. Article
  11. Comparing Phylogenetic Trees by Matching Nodes Using the Transfer Distance Between Partitions.Journal of computational biology : a journal of computational molecular cell biology · 2017
    Article
  12. Multilocus inference of species trees and DNA barcoding.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2016
    Review
  13. Article
  14. Article
  15. Article
  16. Are the duplication cost and Robinson-Foulds distance equivalent?Journal of computational biology : a journal of computational molecular cell biology · 2014
    Article
  17. Article
4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Ruchi ChaudharyDepartment of Computer Science, Iowa State University, Ames, IA 50011, USA. ruchic@ufl.edu.
John Gordon Burleigh
David Fernández-Baca

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundConstructing species trees from multi-copy gene trees remains a challenging problem in phylogenetics. One difficulty is that the underlying genes can be incongruent due to evolutionary processes such as gene duplication and loss, deep coalescence, or lateral gene transfer. Gene tree estimation errors may further exacerbate the difficulties of species tree estimation.

resultsWe present a new approach for inferring species trees from incongruent multi-copy gene trees that is based on a generalization of the Robinson-Foulds (RF) distance measure to multi-labeled trees (mul-trees). We prove that it is NP-hard to compute the RF distance between two mul-trees; however, it is easy to calculate this distance between a mul-tree and a singly-labeled species tree. Motivated by this, we formulate the RF problem for mul-trees (MulRF) as follows: Given a collection of multi-copy gene trees, find a singly-labeled species tree that minimizes the total RF distance from the input mul-trees. We develop and implement a fast SPR-based heuristic algorithm for the NP-hard MulRF problem.We compare the performance of the MulRF method (available at http://genome.cs.iastate.edu/CBL/MulRF/) with several gene tree parsimony approaches using gene tree simulations that incorporate gene tree error, gene duplications and losses, and/or lateral transfer. The MulRF method produces more accurate species trees than gene tree parsimony approaches. We also demonstrate that the MulRF method infers in minutes a credible plant species tree from a collection of nearly 2,000 gene trees.

conclusionsOur new phylogenetic inference method, based on a generalized RF distance, makes it possible to quickly estimate species trees from large genomic data sets. Since the MulRF method, unlike gene tree parsimony, is based on a generic tree distance measure, it is appealing for analyses of genomic data sets, in which many processes such as deep coalescence, recombination, gene duplication and losses as well as phylogenetic error may contribute to gene tree discord. In experiments, the MulRF method estimated species trees accurately and quickly, demonstrating MulRF as an efficient alternative approach for phylogenetic inference from large-scale genomic data sets.

Identifiers

PMID24180377
PMCPMC3874668

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.