Run astral with outsider in R

Accurate Species TRee ALgorithm: estimating an unrooted species tree given a set of unrooted gene trees.

Install and look up help

library(outsider)
#> ----------------
#> outsider v 0.1.0
#> ----------------
#> - Security notice: be sure of which modules you install
module_install(repo = 'dombennett/om..astral')
#> -----------------------------------------------------
#> Warning: You are about to install an outsider module!
#> -----------------------------------------------------
#> Outsider modules install and run external programs
#> via Docker <https://www.docker.com>. These external
#> programs may communicate with the internet and could
#> potentially be malicious.
#> 
#> Be sure to know the module you are about to install:
#> Is it from a trusted developer? Are colleagues using
#> it? Is it supposed to download lots of data? Is it
#> well used (e.g. check number of stars on GitHub)?
#> -----------------------------------------------------
#>  Module information
#> -----------------------------------------------------
#> program: astral
#> details: Accurate Species TRee ALgorithm
#> docker: dombennett
#> github: dombennett
#> url: https://github.com/DomBennett/om..astral
#> image: dombennett/om_astral
#> container: om_astral
#> package: om..astral
#> Travis CI: Failing/Erroring
#> -----------------------------------------------------
#> Enter any key to continue or press Esc to quit
#module_help(repo = 'dombennett/om..astral')

A detailed example

# import
library(outsider)
astral <- module_import(fname = 'astral', repo = "dombennett/om..astral")

# data
# test dataset of song mammals
url <- 'https://raw.githubusercontent.com/DomBennett/om..astral/master/test_data/song_mammals.424.gene.tre'
input_file <- file.path(tempdir(), 'song_mammals.424.gene.tre')
download.file(url = url, destfile = input_file)

# run with test file and 1GB memmory
output_file <- file.path(tempdir(), 'song_mammals.tre')
astral(input_file = input_file, output_file = output_file,
       java_args = '-Xmx1000M')
#> 
#> ================== ASTRAL ===================== 
#> 
#> This is ASTRAL version 5.6.3
#> Gene trees are treated as unrooted
#> 424 trees read from song_mammals.424.gene.tre
#> All output trees will be *arbitrarily* rooted at Chicken
#> 
#> ======== Running the main analysis
#> Number of taxa: 37 (37 species)
#> Taxa: [Chicken, Marmoset, Orangutan, Human, Chimpanzee, Gorilla, Macaque, Galagos, Mouse_Lemur, Tree_Shrew, Mouse, Rat, Kangaroo_Rat, Guinea_Pig, Squirrel, Tarsier, Rabbit, Pika, Microbat, Megabat, Horse, Dolphin, Cow, Alpaca, Pig, Dog, Cat, Shrew, Hedgehog, Lesser_Hedgehog_Tenrec, Hyrax, Elephant, Sloth, Armadillos, Platypus, Opossum, Wallaby]
#> Taxon occupancy: {Rat=424, Tarsier=424, Dolphin=424, Rabbit=424, Macaque=424, Pika=424, Alpaca=424, Shrew=424, Sloth=424, Tree_Shrew=424, Kangaroo_Rat=424, Armadillos=424, Chimpanzee=424, Horse=424, Dog=424, Human=424, Lesser_Hedgehog_Tenrec=424, Microbat=424, Platypus=424, Wallaby=424, Cow=424, Pig=424, Marmoset=424, Megabat=424, Hedgehog=424, Mouse=424, Guinea_Pig=424, Mouse_Lemur=424, Cat=424, Hyrax=424, Elephant=424, Chicken=424, Orangutan=424, Opossum=424, Galagos=424, Squirrel=424, Gorilla=424}
#> Number of gene trees: 424
#> 0 trees have missing taxa
#> Calculating quartet distance matrix (for completion of X)
#> Species tree distances calculated ...
#> Building set of clusters (X) from gene trees 
#> ------------------------------
#> gradient0: 1933
#> Number of Clusters after addition by distance: 1933
#> calculating extra bipartitions to be added at level 1 ...
#> Adding to X using resolutions of greedy consensus ...
#> Limit for sigma of degrees:975
#> polytomy size limit : 4
#> discarded polytomies:  [3, 3, 4, 4]
#> Threshold 0.0:
#> Threshold 0.01:
#> Threshold 0.02:
#> Threshold 0.05:
#> Threshold 0.1:
#> Threshold 0.2:
#> polytomy of size 4; rounds with additions with at least 5 support: 0; clusters: 1933
#> Threshold 0.3333333333333333:
#> polytomy of size 3; rounds with additions with at least 5 support: 0; clusters: 1933
#> polytomy of size 3; rounds with additions with at least 5 support: 0; clusters: 1933
#> polytomy of size 4; rounds with additions with at least 5 support: 0; clusters: 1933
#> max k is :0
#> Number of Clusters after addition by greedy: 1933
#> gradient0 in heuristiic: 1933
#> partitions formed in 0.953 secs
#> Dynamic Programming starting after 0.953 secs
#> Using tree-based weight calculation.
#> Using polytree-based weight calculation.
#> Polytree max score: 28003080
#> Polytree building time: 0.238 seconds.
#> Number of quartet trees in the gene trees: 28003080
#> Size of largest cluster: 37
#> Greedy score: 24862814
#> estimationFactor: 1.1263037241078182
#> Sub-optimal score: 25489533
#> Total Number of elements weighted: 3372
#> Normalized score (portion of input quartet trees satisfied before correcting for multiple individuals): 0.9115752624354179
#> Optimization score: 25526915
#> Optimal tree inferred in 1.849 secs.
#> (Chimpanzee,(Human,(Gorilla,(Orangutan,(Macaque,(Marmoset,(Tarsier,((Galagos,Mouse_Lemur),((Tree_Shrew,((Rabbit,Pika),(Squirrel,(Guinea_Pig,(Kangaroo_Rat,(Mouse,Rat)))))),((((Chicken,Platypus),(Opossum,Wallaby)),((Sloth,Armadillos),(Lesser_Hedgehog_Tenrec,(Hyrax,Elephant)))),((Shrew,Hedgehog),((Microbat,Megabat),((Horse,(Dog,Cat)),(Alpaca,(Pig,(Dolphin,Cow))))))))))))))));
#> Final quartet score is: 25526915
#> Final normalized quartet score is: 0.9115752624354179
#> (Chicken,(Platypus,((Opossum,Wallaby)1:4.9534768802562965,(((Sloth,Armadillos)1:4.3332364705044455,(Lesser_Hedgehog_Tenrec,(Hyrax,Elephant)1:1.5072311535707152)1:3.0324267685203896)1:0.11752571582479492,(((Shrew,Hedgehog)1:1.0128082767511968,((Microbat,Megabat)1:1.5529600021930556,((Horse,(Dog,Cat)1:2.9057840368910477)0.9:0.0641150813890718,(Alpaca,(Pig,(Dolphin,Cow)1:1.3549566469016843)1:0.6392263251934307)1:3.5727535641858488)1:0.11521870556469156)1:0.43083761402314513)1:1.9714179086025256,((Tree_Shrew,((Rabbit,Pika)1:3.449399483480025,(Squirrel,(Guinea_Pig,(Kangaroo_Rat,(Mouse,Rat)1:5.045850200387328)1:0.6153942169908233)1:0.14915704136865612)1:1.6583346999346553)1:0.843253312273408)0.91:0.08610422555073367,((Galagos,Mouse_Lemur)1:2.4173938646853443,(Tarsier,(Marmoset,(Macaque,(Orangutan,(Gorilla,(Human,Chimpanzee)1:0.6434676443273446)1:2.3363022618905545)1:2.5809965452562977)1:2.687856981495734)1:4.347341076686)1:0.6511829814609134)1:2.1216694610241857)1:2.3363739622649566)1:1.5631449093243042)1:3.3544501191225256)1:0.9262330413691204));
#> Weight calculation took 0.470036888 secs
#> ASTRAL finished in 2.473 secs

# plot tree
pkgs <- installed.packages()
if (!'ape' %in% pkgs) {
  install.packages('ape')
}
tree <- ape::read.tree(file = output_file)
ape::plot.phylo(tree, type = 'fan', no.margin = TRUE)
#> Warning in ape::plot.phylo(tree, type = "fan", no.margin = TRUE): 37 branch
#> length(s) NA(s): branch lengths ignored in the plot

Key arguments

There are three arguments for calling astral through R:

• input_file: List of Newick gene trees, equivalent to -i in command-line version
• output_file: Newick species tree, equivalent to -o in command-line version
• log_file: Output log.

Additionally, there are separate Java arguments, e.g. -Xmx to specify maximum memory usage.

Links

Find out more by visiting the ASTRAL’s homepage

Please cite

• Zhang, Chao, Maryam Rabiee, Erfan Sayyari, and Siavash Mirarab. 2018. “ASTRAL-III: Polynomial Time Species Tree Reconstruction from Partially Resolved Gene Trees.” BMC Bioinformatics 19 (S6): 153. doi:10.1186/s12859-018-2129-y.
• Rabiee, Maryam, Erfan Sayyari, and Siavash Mirarab. 2019. “Multi-Allele Species Reconstruction Using ASTRAL.” Molecular Phylogenetics and Evolution 130 (January). 286–96. doi:10.1016/j.ympev.2018.10.033.
• Sayyari, Erfan, and Siavash Mirarab. 2016. “Fast Coalescent-Based Computation of Local Branch Support from Quartet Frequencies.” Molecular Biology and Evolution 33 (7): 1654–68. doi:10.1093/molbev/msw079
• Sayyari, Erfan, and Siavash Mirarab. 2018. “Testing for Polytomies in Phylogenetic Species Trees Using Quartet Frequencies.” Genes 9 (3): 132. doi:10.3390/genes9030132.
• Bennett et al. (2020). outsider: Install and run programs, outside of R, inside of R. Journal of Open Source Software, In review

An outsider module

Learn more at outsider website. Want to build your own module? Check out outsider.devtools website.