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Testing Phylogenetic Stability with Variable Taxon Sampling

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Environmental Microbial Evolution

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2569))

Abstract

Over the past three decades, computational capabilities have grown at such a rapid rate that they have given rise to many computationally heavy science fields such as phylogenomics. As increasingly more genomes are sequenced in the three domains of life, larger and more species-complete phylogenetic tree reconstructions are leading to a better understanding of the tree of life and the evolutionary histories in deep times. However, these large datasets pose unique challenges from a modeling and computational perspective: accurately describing the evolutionary process of thousands of species is still beyond the capability of current models, while the computational burden limits our ability to test multiple hypotheses. Thus, it is common practice to reduce the size of a dataset by selecting species to represent a clade (taxon sampling). Unfortunately, this process is subjective, and comparisons of large tree of life studies show that choice and number of species used in a dataset can alter the topology obtained. Thus, taxon sampling is, in itself, a process that needs to be fully investigated to determine its effect on phylogenetic stability. Here, we present the theory and practical application of an automated pipeline that can be easily implemented to explore the effect of taxon sampling on phylogenetic reconstructions. The application of this approach was recently discussed in a study of Terrabacteria and shows its power in investigating the accuracy of deep nodes of a phylogeny.

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References

  1. Darwin C (1859) On the origin of species by means of natural selection, or preservation of favoured races in the struggle for life. John Murray, London

    Book  Google Scholar 

  2. Delsuc F, Brinkmann H, Philippe H (2005) Phylogenomics and the reconstruction of the tree of life. Nat Rev Genet 6:361–375. https://doi.org/10.1038/nrg1603

    Article  CAS  PubMed  Google Scholar 

  3. Bailey SF, Blanquart F, Bataillon T, Kassen R (2017) What drives parallel evolution? BioEssays 39:e201600176. https://doi.org/10.1002/bies.201600176

    Article  Google Scholar 

  4. Christin PA, Weinreich DM, Besnard G (2010) Causes and evolutionary significance of genetic convergence. Trends Genet 26:400–405. https://doi.org/10.1016/j.tig.2010.06.005

    Article  CAS  PubMed  Google Scholar 

  5. Jetz W, Thomas GH, Joy JB et al (2012) The global diversity of birds in space and time. Nature 491:444–448. https://doi.org/10.1038/nature11631

    Article  CAS  PubMed  Google Scholar 

  6. Losos J (2013) The Princeton guide to evolution. Princeton University Press

    Book  Google Scholar 

  7. Vahdati R, Wagner A (2016) Parallel or convergent evolution in human population genomic data revealed by genotype networks. BMC Evol Biol 16:1–20. https://doi.org/10.1186/s12862-016-0722-0

    Article  Google Scholar 

  8. Clerissi C, Touchon M, Capela D et al (2018) Parallels between experimental and natural evolution of legume symbionts. Nat Commun 9:2264. https://doi.org/10.1038/s41467-018-04778-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Zeller KA (1995) Phylogenetic relatedness within the genus Erysiphe estimated with morphological characteristics. Mycologia 87:525–531. https://doi.org/10.2307/3760771

    Article  Google Scholar 

  10. Zamani Z, Shahi-Gharahlar A, Fatahi R, Bouzari N (2012) Genetic relatedness among some wild cherry (Prunus subgenus Cerasus) genotypes native to Iran assayed by morphological traits and random amplified polymorphic DNA analysis. Plant Syst Evol 298:499–509. https://doi.org/10.1007/s00606-011-0561-9

    Article  CAS  Google Scholar 

  11. Sleator RD (2011) Phylogenetics. Arch Microbiol 193:235–239. https://doi.org/10.1007/s00203-011-0677-x

    Article  CAS  PubMed  Google Scholar 

  12. Lang JM, Darling AE, Eisen JA (2013) Phylogeny of bacterial and archaeal genomes using conserved genes: supertrees and supermatrices. PLoS One 8:e62510. https://doi.org/10.1371/journal.pone.0062510

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Rinke C, Schwientek P, Sczyrba A et al (2013) Insights into the phylogeny and coding potential of microbial dark matter. Nature 499:431–437. https://doi.org/10.1038/nature12352

    Article  CAS  PubMed  Google Scholar 

  14. Hug LA, Baker BJ, Anantharaman K et al (2016) A new view of the tree of life. Nat Microbiol 1:16048. https://doi.org/10.1038/nmicrobiol.2016.48

    Article  CAS  PubMed  Google Scholar 

  15. Simion P, Philippe H, Baurain D et al (2017) A large and consistent phylogenomic dataset supports sponges as the sister group to all other animals. Curr Biol 27:958–967. https://doi.org/10.1016/j.cub.2017.02.031

    Article  CAS  PubMed  Google Scholar 

  16. Linard B, Crampton-Platt A, Moriniere J et al (2018) The contribution of mitochondrial metagenomics to large-scale data mining and phylogenetic analysis of Coleoptera. Mol Phylogenet Evol 128:1–11. https://doi.org/10.1016/j.ympev.2018.07.008

    Article  CAS  PubMed  Google Scholar 

  17. Shen H, Jin D, Shu JP et al (2018) Large-scale phylogenomic analysis resolves a backbone phylogeny in ferns. Gigascience 7:1–11. https://doi.org/10.1093/gigascience/gix116

    Article  CAS  PubMed  Google Scholar 

  18. Estrada-Peña A, Cabezas-Cruz A (2019) Phyloproteomic and functional analyses do not support a split in the genus Borrelia (phylum Spirochaetes). BMC Evol Biol 19:54. https://doi.org/10.1186/s12862-019-1379-2

    Article  PubMed  PubMed Central  Google Scholar 

  19. Ashkenazy H, Kliger Y (2010) Reducing phylogenetic bias in correlated mutation analysis. Protein Eng Des Sel 23:321–326. https://doi.org/10.1093/protein/gzp078

    Article  CAS  PubMed  Google Scholar 

  20. Duchêne DA, Duchêne S, Ho SYW (2017) New statistical criteria detect phylogenetic bias caused by compositional heterogeneity. Mol Biol Evol 34:1529–1534. https://doi.org/10.1093/molbev/msx092

    Article  CAS  PubMed  Google Scholar 

  21. Superson AA, Phelan D, Dekovich A, Battistuzzi FU (2019) Choice of species affects phylogenetic stability of deep nodes: an empirical example in Terrabacteria. Bioinformatics 35:3608–3616. https://doi.org/10.1093/bioinformatics/btz121

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Townsend JP, Su Z, Tekle YI (2012) Phylogenetic signal and noise: predicting the power of a data set to resolve phylogeny. Syst Biol 61:835. https://doi.org/10.1093/sysbio/sys036

    Article  CAS  PubMed  Google Scholar 

  23. Shen XX, Hittinger CT, Rokas A (2017) Contentious relationships in phylogenomic studies can be driven by a handful of genes. Nat Ecol Evol 1:0126. https://doi.org/10.1038/s41559-017-0126

    Article  Google Scholar 

  24. Mongiardino Koch N, Gauthier JA (2018) Noise and biases in genomic data may underlie radically different hypotheses for the position of Iguania within Squamata. PLoS One 13:e0202729. https://doi.org/10.1371/journal.pone.0202729

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Rokas A, Carroll SB (2005) More genes or more taxa? The relative contribution of gene number and taxon number to phylogenetic accuracy. Mol Biol Evol 22:1337–1344. https://doi.org/10.1093/molbev/msi121

    Article  CAS  PubMed  Google Scholar 

  26. Sperling EA, Peterson KJ, Pisani D (2009) Phylogenetic-signal dissection of nuclear housekeeping genes supports the Paraphyly of sponges and the Monophyly of Eumetazoa. Mol Biol Evol 26:2261–2274. https://doi.org/10.1093/molbev/msp148

    Article  CAS  PubMed  Google Scholar 

  27. Kumar S, Filipski AJ, Battistuzzi FU et al (2012) Statistics and truth in phylogenomics. Mol Biol Evol 29:457–472. https://doi.org/10.1093/molbev/msr202

    Article  CAS  PubMed  Google Scholar 

  28. Pisani D, Pett W, Dohrmann M et al (2015) Genomic data do not support comb jellies as the sister group to all other animals. Proc Natl Acad Sci U S A 112:15402–15407. https://doi.org/10.1073/pnas.1518127112

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Hejase HA, Liu KJ (2016) A scalability study of phylogenetic network inference methods using empirical datasets and simulations involving a single reticulation. BMC Bioinformatics 17:422. https://doi.org/10.1186/s12859-016-1277-1

    Article  PubMed  PubMed Central  Google Scholar 

  30. Fourment M, Magee AF, Whidden C et al (2020) 19 dubious ways to compute the marginal likelihood of a phylogenetic tree topology. Syst Biol 69:209–220. https://doi.org/10.1093/sysbio/syz046

    Article  PubMed  Google Scholar 

  31. Whidden C, Claywell BC, Fisher T et al (2020) Systematic exploration of the high likelihood set of phylogenetic tree topologies. Syst Biol 69:280–293. https://doi.org/10.1093/sysbio/syz047

    Article  PubMed  Google Scholar 

  32. Rodríguez A, Burgon JD, Lyra M et al (2017) Inferring the shallow phylogeny of true salamanders (Salamandra) by multiple phylogenomic approaches. Mol Phylogenet Evol 115:16–26. https://doi.org/10.1016/j.ympev.2017.07.009

    Article  PubMed  Google Scholar 

  33. Joyce J (2019) Bayes’ Theorem. In: Zalta EN (ed) The Stanford encyclopedia of philosophy, Spring 201. Metaphysics Research Lab, Stanford University

    Google Scholar 

  34. Venn J (1888) The logic of chance, 3rd edn. Macmillan, New York

    Google Scholar 

  35. Neyman J (1937) Outline of a theory of statistical estimation based on the classical theory of probability. Philos Trans R Soc London Ser A, Math Phys Sci 236:333–380. https://doi.org/10.1098/rsta.1937.0005

    Article  Google Scholar 

  36. McCormack GP, Clewley JP (2002) The application of molecular phylogenetics to the analysis of viral genome diversity and evolution. Rev Med Virol 12:221–238. https://doi.org/10.1002/rmv.355

    Article  CAS  PubMed  Google Scholar 

  37. Saitou N, Nei M (1987) The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol Biol Evol 4:406–425. https://doi.org/10.1093/oxfordjournals.molbev.a040454

    Article  CAS  PubMed  Google Scholar 

  38. Sokal RR (1958) A statistical method for evaluating systematic relationships

    Google Scholar 

  39. Ané C, Larget B, Baum DA et al (2007) Bayesian estimation of concordance among gene trees. Mol Biol Evol 24:412–426. https://doi.org/10.1093/molbev/msl170

    Article  CAS  PubMed  Google Scholar 

  40. Drummond AJ, Rambaut A (2007) BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol Biol 7:214. https://doi.org/10.1186/1471-2148-7-214

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Ronquist F, Teslenko M, van der Mark P et al (2012) MrBayes 3.2: efficient Bayesian phylogenetic inference and model choice across a large model space. Syst Biol 61:539–542. https://doi.org/10.1093/sysbio/sys029

    Article  PubMed  PubMed Central  Google Scholar 

  42. Bouckaert R, Vaughan TG, Barido-Sottani J et al (2019) BEAST 2.5: an advanced software platform for Bayesian evolutionary analysis. PLoS Comput Biol 15:e1006650. https://doi.org/10.1371/journal.pcbi.1006650

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Price MN, Dehal PS, Arkin AP (2009) FastTree: computing large minimum evolution trees with profiles instead of a distance matrix. Mol Biol Evol 26:1641–1650. https://doi.org/10.1093/molbev/msp077

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Price MN, Dehal PS, Arkin AP (2010) FastTree 2 – approximately maximum-likelihood trees for large alignments. PLoS One 5:e9490. https://doi.org/10.1371/journal.pone.0009490

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Stamatakis A (2014) RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30:1312–1313. https://doi.org/10.1093/bioinformatics/btu033

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Nguyen L-T, Schmidt HA, von Haeseler A, Minh BQ (2015) IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol Biol Evol 32:268–274. https://doi.org/10.1093/molbev/msu300

    Article  CAS  PubMed  Google Scholar 

  47. Minh BQ, Schmidt HA, Chernomor O et al (2020) IQ-TREE 2: new models and efficient methods for phylogenetic inference in the genomic era. Mol Biol Evol 37:1530–1534. https://doi.org/10.1093/molbev/msaa015

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Hoang DT, Vinh LS, Flouri T et al (2018) MPBoot: fast phylogenetic maximum parsimony tree inference and bootstrap approximation. BMC Evol Biol 18:11. https://doi.org/10.1186/s12862-018-1131-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Giribet G (2007) Efficient tree searches with available algorithms. Evol Bioinforma 3:341–356. https://doi.org/10.1177/117693430700300014

    Article  CAS  Google Scholar 

  50. Sul SJ, Matthews S, Williams TL (2009) Using tree diversity to compare phylogenetic heuristics. BMC Bioinformatics 10:1–9. https://doi.org/10.1186/1471-2105-10-S4-S3

    Article  Google Scholar 

  51. Davison A, Hinkley DV (1997) Bootstrap methods and their application. Cambridge University Press

    Book  Google Scholar 

  52. Stamatakis A, Hoover P, Rougemont J (2008) A rapid bootstrap algorithm for the RAxML web servers. Syst Biol 57:758–771. https://doi.org/10.1080/10635150802429642

    Article  PubMed  Google Scholar 

  53. Nei M, Kumar S (2000) Molecular evolution and phylogenetics. Oxford University Press

    Google Scholar 

  54. Kapli P, Yang Z, Telford MJ (2020) Phylogenetic tree building in the genomic age. Nat Rev Genet 21:428–444. https://doi.org/10.1038/s41576-020-0233-0

    Article  CAS  PubMed  Google Scholar 

  55. Jukes TH, Cantor CR (1969) Evolution of protein molecules. In: Mammalian protein metabolism. Elsevier, pp 21–132

    Chapter  Google Scholar 

  56. Dayhoff MO (1969) Atlas of protein sequence and structure. National Biomedical Research Foundation

    Google Scholar 

  57. Lanave C, Preparata G, Sacone C, Serio G (1984) A new method for calculating evolutionary substitution rates. J Mol Evol 20:86–93. https://doi.org/10.1007/BF02101990

    Article  CAS  PubMed  Google Scholar 

  58. Le SQ, Gascuel O (2008) An improved general amino acid replacement matrix. Mol Biol Evol 25:1307–1320. https://doi.org/10.1093/molbev/msn067

    Article  CAS  PubMed  Google Scholar 

  59. Gatto L, Catanzaro D, Milinkovitch MC (2006) Assessing the applicability of the GTR nucleotide substitution model through simulations. Evol Bioinforma 2:117693430600200. https://doi.org/10.1177/117693430600200020

    Article  Google Scholar 

  60. Le SQ, Dang CC, Gascuel O (2012) Modeling protein evolution with several amino acid replacement matrices depending on site rates. Mol Biol Evol 29:2921–2936. https://doi.org/10.1093/molbev/mss112

    Article  CAS  PubMed  Google Scholar 

  61. Lopez P, Casane D, Philippe H (2002) Heterotachy, an important process of protein evolution. Mol Biol Evol 19:1–7. https://doi.org/10.1093/oxfordjournals.molbev.a003973

    Article  CAS  PubMed  Google Scholar 

  62. Lartillot N, Philippe H (2004) A Bayesian mixture model for across-site heterogeneities in the amino-acid replacement process. Mol Biol Evol 21:1095–1109. https://doi.org/10.1093/molbev/msh112

    Article  CAS  PubMed  Google Scholar 

  63. Pick KS, Philippe H, Schreiber F et al (2010) Improved phylogenomic taxon sampling noticeably affects Nonbilaterian relationships. Mol Biol Evol 27:1983–1987. https://doi.org/10.1093/molbev/msq089

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Plazzi F, Ferrucci RR, Passamonti M (2010) Phylogenetic representativeness: a new method for evaluating taxon sampling in evolutionary studies. BMC Bioinformatics 11:209. https://doi.org/10.1186/1471-2105-11-209

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Esselstyn JA, Oliveros CH, Swanson MT, Faircloth BC (2017) Investigating difficult nodes in the placental mammal tree with expanded taxon sampling and thousands of ultraconserved elements. Genome Biol Evol 9:2308–2321. https://doi.org/10.1093/gbe/evx168

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Park DS, Worthington S, Xi Z (2018) Taxon sampling effects on the quantification and comparison of community phylogenetic diversity. Mol Ecol 27:1296–1308. https://doi.org/10.1111/mec.14520

    Article  PubMed  Google Scholar 

  67. Zou H, Jakovlić I, Zhang D et al (2020) Architectural instability, inverted skews and mitochondrial phylogenomics of Isopoda: outgroup choice affects the long-branch attraction artefacts. R Soc Open Sci 7:191887. https://doi.org/10.1098/rsos.191887

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Mukherjee S, Stamatis D, Bertsch J et al (2019) Genomes OnLine database (GOLD) v.7: updates and new features. Nucleic Acids Res 47:D649–D659. https://doi.org/10.1093/nar/gky977

    Article  CAS  PubMed  Google Scholar 

  69. Mukherjee S, Stamatis D, Bertsch J et al (2021) Genomes OnLine Database (GOLD) v.8: overview and updates. Nucleic Acids Res 49:D723–D733. https://doi.org/10.1093/nar/gkaa983

    Article  CAS  PubMed  Google Scholar 

  70. Wheeler WC, Coddington JA, Crowley LM et al (2017) The spider tree of life: phylogeny of Araneae based on target-gene analyses from an extensive taxon sampling. Cladistics 33:574–616. https://doi.org/10.1111/cla.12182

    Article  PubMed  Google Scholar 

  71. Çıplak B, Yahyaoğlu Ö, Uluar O (2021) Revisiting Pholidopterini (Orthoptera, Tettigoniidae): Rapid radiation causes homoplasy and phylogenetic instability. Zool Scr 50:225–240. https://doi.org/10.1111/zsc.12463

    Article  Google Scholar 

  72. Żyła D, Bogri A, Heath TA, Solodovnikov A (2021) Total-evidence analysis resolves the phylogenetic position of an enigmatic group of Paederinae rove beetles (Coleoptera: Staphylinidae). Mol Phylogenet Evol 157:107059. https://doi.org/10.1016/j.ympev.2020.107059

    Article  PubMed  Google Scholar 

  73. Linder CR, Suri R, Liu K, Warnow T (2010) Benchmark datasets and software for developing and testing methods for large-scale multiple sequence alignment and phylogenetic inference. PLoS Curr 2:RRN1195. https://doi.org/10.1371/currents.RRN1195

    Article  PubMed  PubMed Central  Google Scholar 

  74. Didelot X, Croucher NJ, Bentley SD et al (2018) Bayesian inference of ancestral dates on bacterial phylogenetic trees. Nucleic Acids Res 46:e134–e134. https://doi.org/10.1093/nar/gky783

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Lees JA, Kendall M, Parkhill J et al (2018) Evaluation of phylogenetic reconstruction methods using bacterial whole genomes: a simulation based study. Wellcome Open Res 3:33. https://doi.org/10.12688/wellcomeopenres.14265.2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Wang Q (2019) Benchmarking and comparing software for phylogenetic analysis from genome-scale data

    Google Scholar 

  77. Li L (2003) OrthoMCL: identification of ortholog groups for eukaryotic genomes. Genome Res 13:2178–2189. https://doi.org/10.1101/gr.1224503

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Chen F (2006) OrthoMCL-DB: querying a comprehensive multi-species collection of ortholog groups. Nucleic Acids Res 34:D363–D368. https://doi.org/10.1093/nar/gkj123

    Article  CAS  PubMed  Google Scholar 

  79. Zerbino DR, Achuthan P, Akanni W et al (2018) Ensembl 2018. Nucleic Acids Res 46:D754–D761. https://doi.org/10.1093/nar/gkx1098

    Article  CAS  PubMed  Google Scholar 

  80. Lechner M, Findeiß S, Steiner L et al (2011) Proteinortho: detection of (Co-)orthologs in large-scale analysis. BMC Bioinformatics 12:124. https://doi.org/10.1186/1471-2105-12-124

    Article  PubMed  PubMed Central  Google Scholar 

  81. Sonnhammer ELL, Östlund G (2015) InParanoid 8: orthology analysis between 273 proteomes, mostly eukaryotic. Nucleic Acids Res 43:D234–D239. https://doi.org/10.1093/nar/gku1203

    Article  CAS  PubMed  Google Scholar 

  82. Huerta-Cepas J, Szklarczyk D, Heller D et al (2019) eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Res 47:D309–D314. https://doi.org/10.1093/nar/gky1085

    Article  CAS  PubMed  Google Scholar 

  83. Emms DM, Kelly S (2019) OrthoFinder: phylogenetic orthology inference for comparative genomics. Genome Biol 20:238. https://doi.org/10.1186/s13059-019-1832-y

    Article  PubMed  PubMed Central  Google Scholar 

  84. Notredame C, Higgins DG, Heringa J (2000) T-coffee: a novel method for fast and accurate multiple sequence alignment 1 1Edited by J. Thornton. J Mol Biol 302:205–217. https://doi.org/10.1006/jmbi.2000.4042

    Article  CAS  PubMed  Google Scholar 

  85. Edgar RC (2004) MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res 32:1792–1797. https://doi.org/10.1093/nar/gkh340

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Sievers F, Higgins DG (2018) Clustal Omega for making accurate alignments of many protein sequences. Protein Sci 27:135–145. https://doi.org/10.1002/pro.3290

    Article  CAS  PubMed  Google Scholar 

  87. Katoh K, Rozewicki J, Yamada KD (2019) MAFFT online service: multiple sequence alignment, interactive sequence choice and visualization. Brief Bioinform 20:1160–1166. https://doi.org/10.1093/bib/bbx108

    Article  CAS  PubMed  Google Scholar 

  88. de Queiroz A, Gatesy J (2007) The supermatrix approach to systematics. Trends Ecol Evol 22:34–41. https://doi.org/10.1016/j.tree.2006.10.002

    Article  PubMed  Google Scholar 

  89. Cotton JA, Wilkinson M (2009) Supertrees join the mainstream of phylogenetics. Trends Ecol Evol 24:1–3. https://doi.org/10.1016/j.tree.2008.08.006

    Article  PubMed  Google Scholar 

  90. Yang Z (2007) PAML 4: phylogenetic analysis by maximum likelihood. Mol Biol Evol 24:1586–1591. https://doi.org/10.1093/molbev/msm088

    Article  CAS  PubMed  Google Scholar 

  91. Larget BR, Kotha SK, Dewey CN, Ané C (2010) BUCKy: gene tree/species tree reconciliation with Bayesian concordance analysis. Bioinformatics 26:2910–2911. https://doi.org/10.1093/bioinformatics/btq539

    Article  CAS  PubMed  Google Scholar 

  92. Wilson IJ, Weale ME, Balding DJ (2003) Inferences from DNA data: population histories, evolutionary processes and forensic match probabilities. J R Stat Soc Ser A (Statistics Soc) 166:155–188. https://doi.org/10.1111/1467-985X.00264

    Article  Google Scholar 

  93. Thomas GH, Hartmann K, Jetz W et al (2013) PASTIS: an R package to facilitate phylogenetic assembly with soft taxonomic inferences. Methods Ecol Evol 4:1011–1017. https://doi.org/10.1111/2041-210X.12117

    Article  Google Scholar 

  94. Goloboff PA, Farris JS, Nixon KC (2008) TNT, a free program for phylogenetic analysis. Cladistics 24:774–786. https://doi.org/10.1111/j.1096-0031.2008.00217.x

    Article  Google Scholar 

  95. Kumar S, Tamura K, Jakobsen IB, Nei M (2001) MEGA2: molecular evolutionary genetics analysis software. Bioinformatics 17:1244–1245. https://doi.org/10.1093/bioinformatics/17.12.1244

    Article  CAS  PubMed  Google Scholar 

  96. Kumar S, Stecher G, Tamura K (2016) MEGA7: molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol Biol Evol 33:1870–1874. https://doi.org/10.1093/molbev/msw054

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Kumar S, Stecher G, Li M et al (2018) MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol Biol Evol 35:1547–1549. https://doi.org/10.1093/molbev/msy096

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Swofford D (2002) PAUP*. phylogenetic analysis using parsimony (*and other methods). Version 4.0b10

    Google Scholar 

  99. Howe K, Bateman A, Durbin R (2002) QuickTree: building huge Neighbour-Joining trees of protein sequences. Bioinformatics 18:1546–1547. https://doi.org/10.1093/bioinformatics/18.11.1546

    Article  CAS  PubMed  Google Scholar 

  100. Vinh LS, Von Haeseler A (2004) IQPNNI: moving fast through tree space and stopping in time. Mol Biol Evol 21:1565–1571. https://doi.org/10.1093/molbev/msh176

    Article  CAS  PubMed  Google Scholar 

  101. Letunic I, Bork P (2019) Interactive Tree Of Life (iTOL) v4: recent updates and new developments. Nucleic Acids Res 47:W256–W259. https://doi.org/10.1093/nar/gkz239

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. James TY, Stajich JE, Hittinger CT, Rokas A (2020) Toward a fully resolved fungal tree of life. Annu Rev Microbiol 74:291–313. https://doi.org/10.1146/annurev-micro-022020-051835

    Article  CAS  PubMed  Google Scholar 

  103. Williams TA, Cox CJ, Foster PG et al (2020) Phylogenomics provides robust support for a two-domains tree of life. Nat Ecol Evol 4:138–147. https://doi.org/10.1038/s41559-019-1040-x

    Article  PubMed  Google Scholar 

  104. Camacho C, Coulouris G, Avagyan V et al (2009) BLAST+: architecture and applications. BMC Bioinformatics 10:421. https://doi.org/10.1186/1471-2105-10-421

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Altschul SF, Gish W, Miller W et al (1990) Basic local alignment search tool. J Mol Biol 215:403–410. https://doi.org/10.1016/S0022-2836(05)80360-2

    Article  CAS  PubMed  Google Scholar 

  106. Altschul SF, Madden TL, Schäffer AA et al (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 25:3389–3402. https://doi.org/10.1093/nar/25.17.3389

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Henikoff S, Henikoff JG (1992) Amino acid substitution matrices from protein blocks. Proc Natl Acad Sci 89:10915–10919. https://doi.org/10.1073/pnas.89.22.10915

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. Buchfink B, Xie C, Huson DH (2015) Fast and sensitive protein alignment using DIAMOND. Nat Methods 12:59–60. https://doi.org/10.1038/nmeth.3176

    Article  CAS  PubMed  Google Scholar 

  109. Buchfink B, Reuter K, Drost H (2021) Sensitive protein alignments at tree-of-life scale using DIAMOND. Nat Methods 18. https://doi.org/10.1038/s41592-021-01101-x

  110. Robinson DF, Foulds LR (1981) Comparison of phylogenetic trees. Math Biosci 53:131–147. https://doi.org/10.1016/0025-5564(81)90043-2

    Article  Google Scholar 

  111. Anisimova M, Gascuel O (2006) Approximate likelihood-ratio test for branches: a fast, accurate, and powerful alternative. Syst Biol 55:539–552. https://doi.org/10.1080/10635150600755453

    Article  PubMed  Google Scholar 

  112. Shimodaira H, Hasegawa M (1999) Multiple comparisons of log-likelihoods with applications to phylogenetic inference. Mol Biol Evol 16:1114–1116. https://doi.org/10.1093/oxfordjournals.molbev.a026201

    Article  CAS  Google Scholar 

  113. Shimodaira H (2002) An approximately unbiased test of phylogenetic tree selection. Syst Biol 51:492–508. https://doi.org/10.1080/10635150290069913

    Article  PubMed  Google Scholar 

  114. Richards TA, Soanes DM, Foster PG et al (2009) Phylogenomic analysis demonstrates a pattern of rare and ancient horizontal gene transfer between plants and fungi. Plant Cell 21:1897–1911. https://doi.org/10.1105/tpc.109.065805

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. Leonard G, Soanes DM, Stevens JR (2011) Resolving the question of trypanosome monophyly: a comparative genomics approach using whole genome data sets with low taxon sampling. Infect Genet Evol 11:955–959. https://doi.org/10.1016/j.meegid.2011.03.005

    Article  PubMed  Google Scholar 

  116. Fang Y, Liu C, Lin J et al (2019) PhySpeTree: an automated pipeline for reconstructing phylogenetic species trees. BMC Evol Biol 19:219. https://doi.org/10.1186/s12862-019-1541-x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  117. De Oliveira D, Ocaña KACS, Ogasawara E et al (2013) Performance evaluation of parallel strategies in public clouds: a study with phylogenomic workflows. Futur Gener Comput Syst 29:1816–1825. https://doi.org/10.1016/j.future.2012.12.019

    Article  Google Scholar 

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Powell, C.L.E., Battistuzzi, F.U. (2022). Testing Phylogenetic Stability with Variable Taxon Sampling. In: Luo, H. (eds) Environmental Microbial Evolution. Methods in Molecular Biology, vol 2569. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2691-7_8

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