DOI: 10.5061/dryad.sm6jb0p
title: Data from: Interrogating genomic-scale data for Squamata (lizards, snakes, and amphisbaenians) shows no support for key traditional morphological relationships

description: Genomics is narrowing uncertainty in the phylogenetic structure for many
 amniote groups. For one of the most diverse and species-rich groups, the
 squamate reptiles (lizards and snakes, amphisbaenians), an inverse
 correlation between the number of taxa and loci sampled still persists
 across all publications using DNA sequence data and reaching a consensus
 on the relationships among them has been highly problematic. Here, we use
 high-throughput sequence data from 289 samples covering 75 families of
 squamates to address phylogenetic affinities, estimate divergence times,
 and characterize residual topological uncertainty in the presence of
 genome scale data. Importantly, we address genomic support for the
 traditional taxonomic groupings Scleroglossa and Macrostomata using novel
 machine-learning techniques. We interrogate genes using various metrics
 inherent to these loci, including parsimony-informative sites,
 phylogenetic informativeness, length, gaps, number of substitutions, and
 site concordance to understand why certain loci fail to find previously
 well-supported molecular clades and how they fail to support species-tree
 estimates. We show that both incomplete lineage sorting and poor gene-tree
 estimation (due to a few undesirable gene properties, such as an
 insufficient number of parsimony informative sites), may account for most
 gene and species-tree discordance. We find overwhelming signal for
 Toxicofera, and also show that none of the loci included in this study
 supports Scleroglossa or Macrostomata. We comment on the origins and
 diversification of Squamata throughout the Mesozoic and underscore
 remaining uncertainties that persist in both deeper parts of the tree
 (e.g., relationships between Dibamia, Gekkota, and remaining squamates;
 and between the three toxiferan clades Iguania, Serpentes, and
 Anguiformes) and within specific clades (e.g., affinities among gekkotan,
 pleurodont iguanians, and colubroid families).
description: Data_S1_Aligned_AHA
 genomic_data_for tree_orderAnchored hybrid
 enrichment genomic-scale data for squamataData_S2_Concatenated_Squam_ConcatenatedConcatenated lociData_S3_Concatenated_partitioned_modelsPartitions and models for the concatenated dataData_S4_Tree_1_Squamate_ASTRAL_IQ_BS_TreeASTRAL III phylogeny using IQTree gene trees with bootstrap supportData_S4_Tree_1_Squamate_Astral_IQ_BS_TreeData_S5_Tree_2_SQ_IQ_ASTRAL_Full_AnnotationsASTRAL III Phylogeny using IQ gene trees with full ASTRAL annotations on quadripartitionsData_S6_Tree_3_Squamata_IQ_ASTRAL_ASTRAL_SupportASTRAL III phylogeny with ASTRAL quadripartion supportData_S6_Tree_3_Squamata_IQ_Astral_Astral_SupportData_S7_Tree_4_Squam_IQ_Concat_Part_BS_SHConcatenated and partitioned IQTree with bootstrap and SH support (BS/SH)Data_S8_Dated_ASTRAL_IQ_Point_TreeDated phylogeny where concatenated sequence data were fit to the topology of ASTRAL III tree and dates were estimated using TreePLData_S8_Dated_Astral_IQ_Point_TreeData_S9_Dated_IQ_UF_Bootstraps_with_errorDated phylogeny with error using 1000 IQ trees and UFBoots (Fast bootstrap) function and estimated with TreePLData_S10_Dated_Combined_Slow_bootstraps_RAXML_with_errorDated phylogeny with error using 100 RAxML bootstraps and phylogenies estimates using IQTree and estimated with TreePL (slow bootstraps)Data_S11_Tree_PL_controlControl file for running TreePLData_S12_All_Code_Squamate_Monophyly-Multicolinearity_STATSCode for running Neural Network for gene interrogation and other functionsData_S13_gene_support_species_tree_by_dateDated nodes from the species tree showing the number of genes that support or do not support that nodeData_S14_Support_by_node_tableNodes from species tree showing if each locus supports (1) or does not support (0) that nodeData_S15. NN_Table_All_StatsTable of all stats for each locus for testing predictions of RFgt-st differences using NN code for regression predictions.Data_S16_Gene_Trees_Dib_Gekkota_not_mono_ASTRAL_treeASTRAL III tree generated from IQ Trees not showing Dibamidae and Gekkota as monophyleticData_S16_Gene_Trees_Dib_Gekkota_not_mono_Astral_treeData_S17_Gene_Trees_Not_Supporting_Anilius_ASTRAL_treeASTRAL III tree generated from IQ Trees not showing Dibamidae and Gekkota as monophyleticData_S17_Gene_Trees_Not_Supporting_Anilius_Astral_treeData_S18_Gene_Trees_Not_Supporting_Toxicofera_ASTRAL_TreeASTRAL III tree generated from IQ Trees not showing Toxicofera as monophyleticData_S18_Gene_Trees_Not_Supporting_Toxicofera_Astral_TreeData_S19_NN_Test_Data_Anil_Dib_ToxData table for use with NN code (Data_S12) to make classification prediction for why genes do or do not find Toxicofera, Amerophidia, Dibamia/Gekkota.Fig_S1_Phylogenetic InformativenessPhylogenetic informativeness for each locus ranked by the length of time. The length of time colored for each locus is associated with strong phylogenetic informativeness over that time period. Loci at the top of the graph are informative over a larger range of time than those at the bottomFig_S2_NN_ModelAn artificial NN model showing input neurons characterizing each AHE locus, the single hidden neuron layer (H1), and the output variable predicting RF distances between each gene tree and the species tree (RFgtst). This diagram shows both intercept terms (bias), B1 and B2, which increases NN efficiency, and synaptic weights indicated by the thickness and bolding of lines connecting the neuronsTable_S1_SquamateAssemblySummaryExcel list of Squamata species sequenced and corresponding summary statistics of capture and sequencing success.Table_S2_Calibration_points_for_Squamata_phylogenyCalibration points for Squamata phylogeny