DOI: 10.5061/dryad.3r2280ggv
title: Data for: PickMe: Sample selection for species tree reconstruction using coalescent weighted quartets

description: After collecting large data sets for phylogenomics studies, researchers
 must decide which genes or samples to include when reconstructing a
 species tree. Incomplete or unreliable data sets make the
 empiricist's decision more difficult. Researchers rely on ad hoc
 strategies to maximize sampling while ensuring sufficient data for
 accurate inferences. An algorithm called PickMe formalizes the sample
 selection process, assuming that the samples evolved under the Tree
 Multispecies Coalescent model. We propose a Bayesian framework for
 selecting samples for species tree analysis. Given a collection of gene
 trees, we compute a posterior probability for each quartet, describing the
 likelihood that the species tree displays this topology. From this, we
 assign individual samples reliability scores computed as the average of a
 scaled version of the posterior probabilities. PickMe uses these weights
 to recommend which samples to include in a species tree analysis. Analysis
 of simulated data showed that including the samples suggested by
 \textit{Pickme} produced species trees closer to the true species trees
 than both unfiltered data sets and data sets with ad hoc gene occupancy
 cut-offs applied.  To further illustrate the efficacy of this
 tool, we apply PickMe to gene trees generated from target capture data
 from milkweeds. PickMe indicates more samples could have reliably been
 included in a previous milkweed phylogenomic analysis than the authors
 analyzed without access to a formal methodology for sample selection.
 Using simulated and empirical data, we also compare \emph{PickMe} to
 existing sample selection methods. Inclusion of PickMe will enhance
 phylogenomics data analysis pipelines by providing a formal structure for
 sample selection.
description: We obtained targeted sequence data for 763 putatively single-copy
 nuclear loci for samples of 59 North American milkweed species, three
 African outgroup species, \textit{Asclepias physocarpa}, \textit{A.
 fruticosa}, and \textit{A. fornicata}, and one additional outgroup,
 \textit{Pergularia daemia} using the target enrichment baits of Weitemier
 et al. (2014) (Supplemental Material~\protect\ref{app:milkweed}). Data for
 32 of these samples and orthologs from the genome sequence of
 \textit{Asclepias syriaca} \citep{weitemier2019draft} were included in the
 analyses of \cite{BOUTTE2019106534}, and nuclear sequence data for the
 additional 30 samples were generated using the DNA sequencing and assembly
 methods described therein. \cite{BOUTTE2019106534} had excluded the 30
 newly analyzed samples based on an ad hoc minimum gene recovery criterion
 of 600 genes (79\%) with the goal of high gene occupancy for all samples
 for species tree analyses. For the analyses conducted here, we masked
 assembled sequences with Ns for very low read depth ($\le 2$ reads) and at
 heterozygous sites (i.e., intra-individual SNPs). For each gene, we
 aligned masked sequences using Mafft version 7.245 with default parameters
 \citep{katoh2013mafft}, and removed sequences with less than 50\% of the
 total alignment length \citep[i.e. Type II missing
 data;][]{hosner2016avoiding} to reduce gene tree error following
 \cite{sayyari2017fragmentary} and \cite{mirarab2019species}.
 For further analysis, we selected a subset of 703 genes, which
 had been identified by \cite{BOUTTE2019106534} as producing the
 best-resolved milkweed phylogenies based on bootstrap support across the
 gene trees. For the complete data set of 63 species, we first estimated
 the 703 gene trees using Neighbor-Joining on uncorrected distances (the
 proportion of observed differences in the aligned sequences) as
 implemented in the ape package \citep{paradis2018ape} in R v. 3.5.1
 \citep{R}. Using these estimated gene trees, we identified the samples to
 be included in species tree analyses using \emph{PickMe}. To determine
 whether the gene tree inference method affected the sample selection
 results, we also used the GTR+Gamma model in RAxML v. 8.2.12;
  \citep{stamatakis2014raxml} to estimate the initial gene trees. For the
 set of samples identified as reliable by \emph{PickMe}, we realigned the
 sequences and then removed small alignments ($&lt; 100$ bp) following
 \cite{BOUTTE2019106534}. We then used IQ-Tree v. 1.5.4
 \citep{nguyen2014iqtree,chernomor2016terrace} to select the best model of
 molecular evolution for the retained alignments and inferred the gene tree
 for each locus using the same parameters as \cite{BOUTTE2019106534}. Using
 ASTRAL-II v. 4.10.12 \citep{mirarab2015astral}  with default parameters,
 we inferred a species tree and calculated local posterior probability
 support \citep{sayyari2016fast}. We calculated gene concordance factors
 using the method of \cite{Minh2020new}, implemented in IQ-Tree v. 2.1.2
 \citep{nguyen2014iqtree,chernomor2016terrace}. For comparison, we repeated
 the gene and species tree analyses done for the subset of \textit{PickMe}
 reliable samples for the full data set using identical methods.
description: # Data for: PickMe: sample selection for species tree reconstruction using
 coalescent weighted quartets
 [https://doi.org/10.5061/dryad.3r2280ggv](https://doi.org/10.5061/dryad.3r2280ggv) ## Description of the data and file structure Data was collected for the analysis of the evolutionary relationships among milkweeds.  The remaining data was used to test the PickMe algorithm for sample selection in the context of phylogenomic analysis. **Supplemental Materials:**  -**SupplA1A7.pdf:** Contains PDF of supplemental appendices A1-A7 as referenced in the published article. **Data Descriptions** \- **Milkweed-Sequence-Files.zip**: Contains sequence data for the analysis. All sequences have been referenced on GenBank. \- **estimated-gene-trees-NJ-Uncorrected** and **estimated-gene-trees-RAxML ** **estimated-gene-trees-NJ-Uncorrected**: Contain all estimated Milkweed gene trees as described in the associated article.  Sample names were cleaned up for the main manuscript.  A log for matching is listed in a text file. \- **OldSpeciesTree.cf.tree**: The species tree referenced in the paper, based on the dataset used in Boutte et al. (2019). \- **new_species_Tree.cf.tree**: The new Milkweed phylogeny, including the additional samples selected by PickMe, as described in the article. -**Asclepias_sp_R1.fq.gz and Asclepias_sp_R2.fq.gz** Illumina sequence data for the sample "Asclepias sp." that was determined to have been involved in a lab error. ### Files and variables #### Data Descriptions \- **Milkweed-Sequence-Files.zip**: Contains sequence data for the analysis. By the time of publication, all sequences will be referenced on GenBank. \- **estimated-gene-trees-NJ-Uncorrected** and **estimated-gene-trees-RAxML**: Contain all estimated Milkweed gene trees as described in the associated article. \- **gates_newPickMeOut.csv**: The output from running PickMe on the `Estimated-gene-tree-Gates.tre`, which contains estimated trees provided by Dan Gates for the method comparison. \- **OldSpeciesTree.cf.tree**: The species tree referenced in the paper, based on the dataset used in Boutte et al. (2019). -**PickMe_all_sp_ML_trees_species_tree.tre** The species tree referenced in the paper which included all available samples with exclusion using PickMe. **PickMe_all_sp_ML_trees** The species tree referenced in the paper which included all available samples without exclusion using PickMe. \- **new_species_Tree.cf.tree**: The new Milkweed phylogeny, including the additional samples selected by PickMe, as described in the article. -**Asclepias_sp_R1.fq.gz and Asclepias_sp_R2.fq.gz** Illumina sequence data for the sample "Asclepias sp." that was determined to have been involved in a lab error. -**species_names_in_trees_differing_from_the_manuscript_text.txt** Indexing file to match the species names published in the manuscripts with the specific sample names used in the analysis. ## Code/software Software Descriptions \- The sample selection software **PickMe** is available at [PhyloPickMe.jl on GitHub](https://github.com/jrusinko/PhyloPickMe.jl) and can also be installed through the Julia package **PhyloPickMe**. \- **OccupancyDistribution.R**: Contains the code used to sample occupancy rates in the Species Tree Analysis simulation. \- **MissingSamplesForSim.R**: The script used to drop samples from the DNA data, based on supplemental data from Sayyari et al. (2017), for the comparison between PickMe and occupancy-based sample selection. \- **DropTipsFromPickMe.R**: Takes a set of DNA files and the results of a PickMe run as input, writing out revised DNA sequences that exclude all samples identified by PickMe as "VeryBad" or "Bad" (for a single dataset). \- **PickMeSimDropTipsPickMe.R**: Used to generate DNA sequence data by removing samples classified as "Bad" or "VeryBad" in the simulation. \- **PickMeSimDropTipsOccupancy.R**: Used to generate DNA sequence data by removing samples with a gene occupancy rate below a certain threshold. \- **OccAstral.sh**: Script to run ASTRAL on the gene trees after samples were removed based on occupancy rates. A similar script was used to compute ASTRAL for the PickMe-dropped trees and for the estimated trees before any samples were dropped. \- **PickMeRFAnalysis.R**: Contains code to compare the results of PickMe to dropping samples based on occupancy rate. For ease of use, the script reads stored data from `OccDropData.RData` instead of recomputing distances. \- **Kappa.jl**: Runs PickMe on collections of estimated trees for the Kappa simulation. \- **CreateMissingTaxaforKappa.R**: Drops samples from gene trees according to a uniform gene occupancy distribution. \- **CreateTaxaErrorsKappa.R**: Generates "random" taxa that appear arbitrarily on gene trees for the Kappa simulation. \- **KappaAnalysisforResubmission.R**: Tests the efficacy of PickMe in identifying random taxa at a range of cutoff values. -**Slurm*arrayJob.txt **** and** RAxML*****_occupancy script** are representative scripts used for estimating gene trees with RAxML on a cluster. ## Access information Data was derived from the following source: [https://datadryad.org/stash/dataset/doi:10.6076/D14599](https://datadryad.org/stash/dataset/doi:10.6076/D14599) author={Sayyari, Erfan and Whitfield, James B and Mirarab, Siavash}, ``` title = {Fragmentary gene sequences negatively impact gene tree and species tree reconstruction [dataset]}, year = {2017}, doi = {DOI of the Dataset}, publisher = {Dryad Digital Repository}, url = {https://doi.org/10.6076/D14599}, ``` Additional Iochroma data was provided by Dan Gates. ### Change Log 14 October, 2024 \- Added gatesnew_PickMeOut . No changes in the results. Just included output file for reference. \- Added Estimated-gene-tree-Gates.tre and PicKMeSimData.tar.zst Made data avaialble for reviewers to check the simulation study, and empiritical comparison in the article. Both of these datasets contain data which the authors of this paper are not the primary owners. This material will be removed or replaced with references upon acceptance. \- added a variaety of R, bash and julia scripts which were used in the simulation and results for the paper. Primarily provided for transparency. 30 January. 2025 Removed  Estimated-gene-tree-Gates.tre and PicKMeSimData.tar.zst  Both of these datasets contained data which the authors of this paper are not the primary owners. They have been removed. 17 June, 2-025 Added: SupplementalMaterialsA1-A7.pdf.  Contains supplemental appendices referenced in the published article.