skip to main content


Title: FastRFS: fast and accurate Robinson-Foulds Supertrees using constrained exact optimization
Abstract Motivation

The estimation of phylogenetic trees is a major part of many biological dataset analyses, but maximum likelihood approaches are NP-hard and Bayesian MCMC methods do not scale well to even moderate-sized datasets. Supertree methods, which are used to construct trees from trees computed on subsets, are critically important tools for enabling the statistical estimation of phylogenies for large and potentially heterogeneous datasets. Supertree estimation is itself NP-hard, and no current supertree method has sufficient accuracy and scalability to provide good accuracy on the large datasets that supertree methods were designed for, containing thousands of species and many subset trees.

Results

We present FastRFS, a new method based on a dynamic programming method we have developed to find an exact solution to the Robinson-Foulds Supertree problem within a constrained search space. FastRFS has excellent accuracy in terms of criterion scores and topological accuracy of the resultant trees, substantially improving on competing methods on a large collection of biological and simulated data. In addition, FastRFS is extremely fast, finishing in minutes on even very large datasets, and in under an hour on a biological dataset with 2228 species.

Availability and Implementation

FastRFS is available on github at https://github.com/pranjalv123/FastRFS

Supplementary information

Supplementary data are available at Bioinformatics online.

 
more » « less
PAR ID:
10394742
Author(s) / Creator(s):
; ;
Publisher / Repository:
Oxford University Press
Date Published:
Journal Name:
Bioinformatics
Volume:
33
Issue:
5
ISSN:
1367-4803
Page Range / eLocation ID:
p. 631-639
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract

    One of the Grand Challenges in Science is the construction of theTree of Life, an evolutionary tree containing several million species, spanning all life on earth. However, the construction of the Tree of Life is enormously computationally challenging, as all the current most accurate methods are either heuristics forNP-hard optimization problems or Bayesian MCMC methods that sample from tree space. One of the most promising approaches for improving scalability and accuracy for phylogeny estimation uses divide-and-conquer: a set of species is divided into overlapping subsets, trees are constructed on the subsets, and then merged together using a “supertree method”. Here, we present Exact-RFS-2, the first polynomial-time algorithm to find an optimal supertree of two trees, using the Robinson-Foulds Supertree (RFS) criterion (a major approach in supertree estimation that is related to maximum likelihood supertrees), and we prove that finding the RFS of three input trees isNP-hard. Exact-RFS-2 is available in open source form on Github athttps://github.com/yuxilin51/GreedyRFS.

     
    more » « less
  2. Abstract Motivation

    Phylogenomics faces a dilemma: on the one hand, most accurate species and gene tree estimation methods are those that co-estimate them; on the other hand, these co-estimation methods do not scale to moderately large numbers of species. The summary-based methods, which first infer gene trees independently and then combine them, are much more scalable but are prone to gene tree estimation error, which is inevitable when inferring trees from limited-length data. Gene tree estimation error is not just random noise and can create biases such as long-branch attraction.

    Results

    We introduce a scalable likelihood-based approach to co-estimation under the multi-species coalescent model. The method, called quartet co-estimation (QuCo), takes as input independently inferred distributions over gene trees and computes the most likely species tree topology and internal branch length for each quartet, marginalizing over gene tree topologies and ignoring branch lengths by making several simplifying assumptions. It then updates the gene tree posterior probabilities based on the species tree. The focus on gene tree topologies and the heuristic division to quartets enables fast likelihood calculations. We benchmark our method with extensive simulations for quartet trees in zones known to produce biased species trees and further with larger trees. We also run QuCo on a biological dataset of bees. Our results show better accuracy than the summary-based approach ASTRAL run on estimated gene trees.

    Availability and implementation

    QuCo is available on https://github.com/maryamrabiee/quco.

    Supplementary information

    Supplementary data are available at Bioinformatics online.

     
    more » « less
  3. Ponty, Yann (Ed.)
    Abstract Motivation Species delimitation, the process of deciding how to group a set of organisms into units called species, is one of the most challenging problems in computational evolutionary biology. While many methods exist for species delimitation, most based on the coalescent theory, few are scalable to very large datasets, and methods that scale tend to be not accurate. Species delimitation is closely related to species tree inference from discordant gene trees, a problem that has enjoyed rapid advances in recent years. Results In this article, we build on the accuracy and scalability of recent quartet-based methods for species tree estimation and propose a new method called SODA for species delimitation. SODA relies heavily on a recently developed method for testing zero branch length in species trees. In extensive simulations, we show that SODA can easily scale to very large datasets while maintaining high accuracy. Availability and implementation The code and data presented here are available on https://github.com/maryamrabiee/SODA. Supplementary information Supplementary data are available at Bioinformatics online. 
    more » « less
  4. Abstract Motivation

    As genome-wide reconstruction of phylogenetic trees becomes more widespread, limitations of available data are being appreciated more than ever before. One issue is that phylogenomic datasets are riddled with missing data, and gene trees, in particular, almost always lack representatives from some species otherwise available in the dataset. Since many downstream applications of gene trees require or can benefit from access to complete gene trees, it will be beneficial to algorithmically complete gene trees. Also, gene trees are often unrooted, and rooting them is useful for downstream applications. While completing and rooting a gene tree with respect to a given species tree has been studied, those problems are not studied in depth when we lack such a reference species tree.

    Results

    We study completion of gene trees without a need for a reference species tree. We formulate an optimization problem to complete the gene trees while minimizing their quartet distance to the given set of gene trees. We extend a seminal algorithm by Brodal et al. to solve this problem in quasi-linear time. In simulated studies and on a large empirical data, we show that completion of gene trees using other gene trees is relatively accurate and, unlike the case where a species tree is available, is unbiased.

    Availability and implementation

    Our method, tripVote, is available at https://github.com/uym2/tripVote.

    Supplementary information

    Supplementary data are available at Bioinformatics online.

     
    more » « less
  5. Abstract Summary

    Multiple sequence alignment is a basic part of many bioinformatics pipelines, including in phylogeny estimation, prediction of structure for both RNAs and proteins, and metagenomic sequence analysis. Yet many sequence datasets exhibit substantial sequence length heterogeneity, both because of large insertions and deletions in the evolutionary history of the sequences and the inclusion of unassembled reads or incompletely assembled sequences in the input. A few methods have been developed that can be highly accurate in aligning datasets with sequence length heterogeneity, with UPP one of the first methods to achieve good accuracy, and WITCH a recent improvement on UPP for accuracy. In this article, we show how we can speed up WITCH. Our improvement includes replacing a critical step in WITCH (currently performed using a heuristic search) by a polynomial time exact algorithm using Smith–Waterman. Our new method, WITCH-NG (i.e. ‘next generation WITCH’) achieves the same accuracy but is substantially faster. WITCH-NG is available at https://github.com/RuneBlaze/WITCH-NG.

    Availability and implementation

    The datasets used in this study are from prior publications and are freely available in public repositories, as indicated in the Supplementary Materials.

    Supplementary information

    Supplementary data are available at Bioinformatics Advances online.

     
    more » « less