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[…] DNA was extracted from lice using either the Qiagen DNeasy micro-kit (Valencia, California, USA) following the manufacturer׳s protocol as described by Valim and Weckstein , or the Qiagen DNeasy tissue kit (Valencia, California, USA) following the manufacture׳s protocol as described by Johnson et al. . After DNA was extracted from individual lice, the exoskeletons were retained and mounted on microscope slides . These voucher slides were used to identify each specimen to genus using the keys in Price et al. . Specific-level identifications were based on original descriptions, specific keys if possible, and comparison with identified slide mounted material. Voucher slides are deposited in the Illinois Natural History Survey Insect Collection (INHS), Price Institute for Parasite Research at the University of Utah (PIPeR), and Field Museum of Natural History (FMNH) (Supplemental ).Portions of one mitochondrial (COI) and one nuclear gene (EF-1α) were selected because these genes have successfully resolved phylogenies of closely related groups of parasitic lice and more distantly related “bark lice” , , , , . We used PCR to amplify and sequence portions of the mitochondrial cytochrome oxidase I (COI; 379 bp) and the nuclear gene elongation factor 1a (EF1 α; 347 bp) using published amplification and sequencing protocols , . Purified PCR products were cycle sequenced using ABI Big Dye (Applied Biosystems, Foster City, California) and run on an ABI Prism 3730 DNA sequencer (Applied Biosystems). Raw sequence data were trimmed, edited, and reconciled using Sequencher 5.0.1 (Genecodes CO., Ann Arbor, Michigan) or Geneious (version 7.0.3, Biomatters LTD). Both genes are protein coding and therefore we were able to easily align them by eye according to codons. These aligned gene sequences were then concatenated for phylogenetic analysis. [...] The final sequence alignment was analyzed using PartitionFinder (v1.1.1; , ), an open source python script that selects the best-fit partitioning schemes and models of molecular evolution for phylogenetic analysis. We tested whether the two genes (COI, EF1 α) should be analyzed together under the same model and parameters or as two separate partitions. We tested only these two partitions because separating each of these genes by codon would only provide 100 bps for each partition, a very small amount of sequence for estimating parameters and would likely result in over-parameterization. The PartitionFinder analysis found that a single partition and GTR+I+G model of molecular evolution best fit the data, using both AICc and BIC criterion. Using these parameters, which were estimated as part of the analysis, and a flat Dirichlet prior for state frequencies, we ran a Bayesian analysis in MrBayes 3.2.2 , , for 10,000,000 generations. Each Bayesian analysis included two parallel runs, each with four Markov chains, to ensure that our analyses were not stuck at local optima . Markov chains were sampled every 500 generations, yielding 20,000 parameter point-estimates. We used these 20,000 point-estimates minus the burn-in generations (500 point-estimates, 250,000 generations) to create a 50% majority-rule consensus tree and calculated Bayesian posterior probabilities to assess nodal support. We rooted the Bayesian tree using a nested set of sister taxa within the family Philopteridae , , , .A consensus tree from the Bayesian analysis of combined COI and EF-1a sequences for Brueelia-complex is shown in . A cladogram of the consensus tree from the Bayesian analysis is shown in . […]

Pipeline specifications

Software tools Sequencher, Geneious, PartitionFinder, MrBayes
Application Phylogenetics
Diseases Genu Varum