DOI: 10.5061/dryad.3tx95x6jd
title: Dataset from: Incomplete lineage sorting and reticulate evolution mask species relationships in Brunelliaceae, an Andean family with rapid, recent diversification

description: Premise: To date, phylogenetic relationships within the monogeneric
 Brunelliaceae have been based on morphological evidence, which does not
 provide sufficient phylogenetic resolution. Here we use target-enriched
 nuclear data to improve our understanding of phylogenetic relationships in
 the family.  Methods: We used the Angiosperms353 toolkit for
 targeted recovery of exonic regions and supercontigs (exons + introns)
 from low copy nuclear genes from 53 of 70 species in Brunellia, and
 several outgroup taxa. We removed loci that indicated biased relationships
 and applied concatenated and coalescent methods to infer Brunellia
 phylogeny. We identified conflicts among gene trees that may reflect
 hybridization or incomplete lineage-sorting events and assessed their
 impact on the phylogeny. Finally, we performed ancestral-state
 reconstructions of morphological traits and assessed the homology of
 character states used to define sections and subsections in Brunellia.
 Results: Brunellia comprises two major clades and several subclades. Most
 of these clades/subclades do not correspond to previous infrageneric taxa.
 There is high topological incongruence among the subclades across
 analyses. Conclusions: Phylogenetic reconstructions point to rapid species
 diversification in Brunelliaceae, reflected in very short branches between
 successive species splits. The removal of putatively biased loci slightly
 improves phylogenetic support for individual clades. Reticulate evolution
 due to hybridization and/or incomplete lineage sorting likely both
 contribute to gene-tree discordance. Morphological characters used to
 define taxa in current classification schemes are homoplastic in the
 ancestral character state reconstructions. While target enrichment data
 allows us to broaden our understanding of diversification in Brunellia,
 the relationships among subclades remain incompletely understood.
description: We used Hyb-Seq data generated for 62 samples of Brunellia and
 some outgroups. The sequence data was composed by 150-bp, paired-end reads
 obtained using an Illumina HiSeqX. Raw reads were quality trimmed using
 Trimmomatic 0.39 (Bolger et al., 2014) to remove low-quality bases at the
 end and beginning of each read (when 4 bp windows had a quality score
 &lt;Q20), and to remove reads shorter than 30 bp. 
 After trimming, paired reads were processed using HybPiper 1.3.1
 (Johnson et al., 2016; available at
 https://github.com/mossmatters/Angiosperms353) with BWA mapper (Li et al.,
 2009) for aligning the reads to the DNA targets, and SPAdes (Bankevich et
 al., 2012) for de novo assembly of reads. We performed the first capture
 using the “Angiosperms353_targetSequences” fasta file available on the
 HybPiper website. We recovered exonic regions with the “exonerate” script.
 The sample “Brunellia_inermis_Orozco 4085” was selected due to its high
 coverage to create a new customized set of exon data targets to maximize
 the data recovery in the sampling. Reads of each sample that assembled to
 these references were saved by default. We recovered exon data and
 supercontigs (exons + introns) of the 353 genes using the “reads_first.py”
 and “exonerate_hits.py” scripts. We added to the
 “exons” and “supercontigs” datasets the DNA sequences of Cephalotus
 follicularis (GenBank PRJDB4484) retrieved from NCBI for the 353 protein
 targets. We inspected putative paralogs with the “paralog_investigator.py”
 script, and genes with paralogs in the ingroup were removed from the
 following analyses. The genes were evaluated using ten parameters to
 minimize potential bias produced by a strong, but misleading signal, such
 as sequence saturation, long-branch attraction, and potential hidden
 paralogy due to polyploidization. Genes that were over or under the 1.5
 interquartile range for any of the considered parameters were excluded
 from all downstream analyses. We aligned the curated
 “exons” and “supercontigs” datasets using MAFFT (Katoh and Toh, 2014) and
 trimmed the alignments using TrimAl v1.4 (Capella-Gutierrez et al., 2009)
 with the "automated1" command. We analyzed concatenated
 alignments using maximum likelihood (ML) in IQ-TREE v2.0.6 (Minh et al.,
 2020). We also used IQ-TREE to build the trees based on individual loci
 that were subsequently included in ASTRAL analyses (Zhang et al., 2018).
 Here, we included the phylogenetic trees of the curated
 "supercontig" dataset, which is the one without paralogs or loci
 that can produce bias, called in the paper "SCWE".
description: This Dataset contains five folders with the following:
 <strong>README file</strong>
 <strong>Folder#1</strong> Includes
 the Brunellia_inermis_Orozco4085 gene list used as references for
 capturing reads on all samples through the HybPiper 1.3.1 pipeline
 (Johnson et al., 2016; available at
 https://github.com/mossmatters/Angiosperms353).
 <strong>Folder#2</strong> Includes
 the reads captured using HybPiper on each sample. Reads were previously
 trimmed for quality using Trimmomatic 0.39 (Bolger et al.,
 2014). <strong>Folder#3</strong>
 Includes sequences of each gene obtained using HybPiper in two
 forms: (1) only exonic regions, and (2) exonic regions, introns, and
 5' and 3' flanks of the gene (also called
 supercontigs). 
 <strong>Folder#4</strong> Includes
 alignments of ¨exons¨ and ¨supercontigs¨ datasets (after being trimmed
 with TrimAl and paralogous genes removed). 
 <strong>Folder#5</strong> Includes
 the ML and ASTRAL trees of the "supercontig-with-exclusions"
 dataset (SCWE).