DOI: 10.6076/d1cp4r
title: QuCo: quartet-based co-estimation of species trees and gene trees

description: Motivation: Phylogenomics faces a dilemma: on the one hand, the 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.
description: Data are simulated by us and provided here for
 reproducibility. 
description: # Quco Dataset --- Date belonging
 to the following paper: Rabiee, Maryam, and Siavash Mirarab. “QuCo:
 Quartet-Based Co-Estimation of Species Trees and Gene Trees.”
 Bioinformatics 38, no. Supplement1 (June 24, 2022): i413–21.
 . ## Description of
 the data and file structure There are several files: ###
 `Quartet-simulation-sequences.tar.gz` The main simulations presented in
 the paper, which involve Felsenstein’s zone quartets. Here, we provide the
 simulated sequences. Files are of the form:
 `rep.[CU]d/R[long]l-[short]s/[rep]/seq[seqlength]/sequences.tar.gz` where
 * `[seqlength]` is the sequence length and is either 1600, 800, 400, 200 *
 `[rep]` is the replicate number, which is between 1 and 20 * `[short]` is
 the length of the short branch, and varies between 0.01, 0.02, 0.04, 0.08.
 * `[long]` is the length of the long branch and varies between 0.1, 0.2,
 0.3, and 0.4. * `[CU]` is the length of the internal branch length in
 coalescent units (CU) and varies between 0.1, 0.2, and 0.3. Each file
 includes the simulated sequences in the fasta format for all genes of each
 repliate. ### `Anomaly-simulations-mrbayes-outputs.tar.gz` This includes
 the results of the anomaly zone simulations, and specifically the output
 of MrBayes. For each of the 50 replicated simulations, we include: *
 `[id]/mrbayes-outputs.tar.gz` Inside each archive, we have MrBayes MCMC
 sample from 200 loci. The files are named as follows, where `locus id` is
 the name of the locus and we have results of four chains (runs 1--4). Each
 .t file includes the MCMC samples in nexus format, as generated by
 MrBayes. * `seq600/[locus id]/[locus id].nex.run[1/2/3/4].t` ###
 `BiologicalDataset_rj_tree_distribution.tar.gz` Biological dataset of
 Bossert et al. (2021) with 32 species and 1291 UCEs
 (). Here, for
 reproducabililty, we provide MrBayes tree distributions. For each of the
 1291 loci, you can find the following files: * `uce-[ucid].run[1/2].t`:
 The MCM sample in nexus format from MrBayes, for chain (run) 1 or 2. ##
 Sharing/Access information The rest of the data is available on *
 [GitLab](https://gitlab.com/mrabiee/quo-data) ## Code/Software The
 simulated data are generated using Simphy