DOI: 10.5061/dryad.ht76hdrp0
title: Data from: ASTRAL: genome-scale coalescent-based species tree estimation

description: Species trees provide insight into basic biology, including the mechanisms
 of evolution and how it modifies biomolecular function and structure,
 biodiversity and co-evolution between genes and species. Yet, gene trees
 often differ from species trees, creating challenges to species tree
 estimation. One of the most frequent causes for conflicting topologies
 between gene trees and species trees is incomplete lineage sorting (ILS),
 which is modelled by the multi-species coalescent. While many methods have
 been developed to estimate species trees from multiple genes, some which
 have statistical guarantees under the multi-species coalescent model,
 existing methods are too computationally intensive for use with
 genome-scale analyses or have been shown to have poor accuracy under some
 realistic conditions. Results: We present ASTRAL, a fast method for
 estimating species trees from multiple genes. ASTRAL is statistically
 consistent, can run on datasets with thousands of genes and has
 outstanding accuracy—improving on MP-EST and the population tree from
 BUCKy, two statistically consistent leading coalescent-based methods.
 ASTRAL is often more accurate than concatenation using maximum likelihood,
 except when ILS levels are low or there are too few gene trees.
description: Availability and implementation: ASTRAL is available in open
 source form at https://github.com/smirarab/ASTRAL/. Datasets studied in
 this article are available at
 http://www.cs.utexas.edu/users/phylo/datasets/astral. Contact:
  warnow@illinois.edu Supplementary information:  Supplementary data are
 available at Bioinformatics online.
description: # ASTRAL: genome-scale coalescent-based species tree estimation
 This repository includes
 both simulated and biological dataset. ## Description of the data and file
 structure The following datasets are used in the ASTRAL paper shown above.
 All these archive files include README files that describe their content.
 ### biological.zip: This file includes: 1\. our estimated gene trees on
 alignments provided to us by authors of Song et al, 2012, PNAS, 2\. our
 estimated species trees on the same dataset. We have re-analyses of two
 biological datasets in our paper. #### Song et al dataset We obtained gene
 alignments from the Song et al and re-estimated gene trees and species
 trees. The following files are included in mammals.zip *
 mammals-alignments.zip contains all the alignments that we obtained from
 Song et al. * mammals-genetreess.zip contains gene trees that we
 estimated. For each gene, we include 3 files *
 RAxML_bipartitions.final.f200 is the bestML tree with support values drawn
 on it based on 200 bootstrap replicates. * RAxML_bootstrap.all includes
 200 replicates of bootstrapping using RAxML * RAxML_bootstrap.all.extra is
 related to the gene resampling procedure. When gene resampling
 bootstrapping was used, some genes needed more than 200 bootstrap
 replicates. Those are included in RAxML_bootstrap.all.extra files (thus
 the first 200 replicates are same as RAxML_bootstrap.all, but some genes
 have more replicates). * `424.[mpest/astral].mlbs`: the species trees
 estimated based on these 424 gene trees. Note that the original Song et al
 dataset has 447 genes, but we removed 23 genes for reasons described in
 the paper. #### Chiari et al. dataset All the gene data related to this
 dataset are already available on the
 [Dyrad](http//datadryad.org/resource/doi/dryad.87b01fq0) ###
 truetrees.zip: The model species tree and the true gene trees simulated
 based on the mammalian dataset of Song et al, 2012, PNAS. The following
 files are available (all in newick format): * model-species-tree: The
 model species tree used for simulation. * The following are the true gene
 trees simulated using the coalescence process based on the model tree.
 Branches in the model species tree are multiplied by 2, 5, or divided by 2
 and 5, to create alternative levels of ILS. * true-trees-1X *
 true-trees-scaled2down * true-trees-scaled2up * true-trees-scaled5down *
 true-trees-scaled5up ### sequencedata.zip Sequence data simulated on the
 true gene trees (mammalian dataset). All the simulated alignments for
 various levels of ILS are given here. Each zip file is a collection of
 alignments (`.fasta`), which make up the content of replicates. Only full
 alignments are given here. Alignments are trimmed into their first 500bp
 or 1000bp to create various model conditions with varying phylogenetic
 signals. ### estimatedgenetrees.zip This file gives gene trees estimated
 using RAxML on alignments of length 1000 and 500 (mammalian dataset). *
 All Gene trees, including their bootstrap replicates are provided in this
 file. The zip file contains a set of other zip files, each corresponding
 to a different ILS level. Each of these zip files consists of a collection
 of files with the following format: * `[gene id]`.`[alignment
 length]`-BestML.tre * `[gene id]`.`[alignment length]`-bp.MLBS.gz `BestML`
 is a newick tree file that contains the maximum likelihood tree returned
 by RAxMML (best of 10 runs). `MLBS.gz` includes the set of 200 bootstrap
 replicates for each gene (note these files are compressed using gzip and
 need to be uncompressed using gunzip). * `gene_ids.txt`: Note that model
 conditions in the paper are defined by the number of genes in addition to
 the ILS level and the alignment length. The gene trees provided here are
 the same for model conditions that differ only in the number of genes.
 Thus, a particular gene id can be used in model conditions with 25 genes,
 50 genes, and so on. The gene ids assigned to each model condition are
 shown in `gene_ids.txt`. For example, gene id 2342 is used in replicate 12
 of 200 genes model conditions, replicate 6 of 400 genes model conditions,
 and replicate 3 of 800 genes model condition. ## Sharing/Access
 information We acknowledge the help of [Bastien
 Boussau](https://sites.google.com/site/bastienboussau/) who performed
 these simulations for another study and made them available to us for this
 paper.  ## Code/Software We used the script given here in the mammalian
 dataset: