SCIENTIA SINICA Vitae, Volume 49 , Issue 4 : 456-471(2019) https://doi.org/10.1360/N052018-00222

Recent progress in phylogenomic methods

More info
  • ReceivedDec 20, 2018
  • AcceptedJan 31, 2019
  • PublishedApr 15, 2019



[1] Soltis D E, Soltis P S. Contributions of plant molecular systematics to studies of molecular evolution. Plant Mol Biol, 2000, 42: 45-75 CrossRef Google Scholar

[2] Soltis D E, Albert V A, Savolainen V, et al. Genome-scale data, angiosperm relationships, and “ending incongruence”: A cautionary tale in phylogenetics. Trends Plant Sci, 2004, 9: 477-483 CrossRef PubMed Google Scholar

[3] Nei M, Kumar S. Molecular Evolution and Phylogeneties. New York: Oxford University Press, 2000. Google Scholar

[4] Degnan J H, Rosenberg N A. Gene tree discordance, phylogenetic inference and the multispecies coalescent. Trends Ecol Evol, 2009, 24: 332-340 CrossRef PubMed Google Scholar

[5] Zou X H, Ge S. Conflicting gene trees and phylogenomics (in Chinese). J Syst Evol, 2008, 46: 795–807 [邹新慧, 葛颂. 基因树冲突与系统发育基因组学研究. 植物分类报, 2008, 46: 795–807]. Google Scholar

[6] Nakhleh L. Computational approaches to species phylogeny inference and gene tree reconciliation. Trends Ecol Evol, 2013, 28: 719-728 CrossRef PubMed Google Scholar

[7] Delsuc F, Brinkmann H, Philippe H. Phylogenomics and the reconstruction of the tree of life. Nat Rev Genet, 2005, 6: 361-375 CrossRef PubMed Google Scholar

[8] Posada D. Phylogenomics for systematic biology. Syst Biol, 2016, 65: 353-356 CrossRef PubMed Google Scholar

[9] Spatafora J W, Bushley K E. Phylogenomics and evolution of secondary metabolism in plant-associated fungi. Curr Opin Plant Biol, 2015, 26: 37-44 CrossRef PubMed Google Scholar

[10] Yu L, Zhang Y P. Phylogenomics—an attractive avenue to reconstruct “Tree of Life” (in Chinese). Hereditas, 2006, 28: 1445–1450 [于黎, 张亚平. 系统发育基因组学—重建生命之树的一条迷人途径. 遗传, 2006, 28: 1445–1450]. Google Scholar

[11] Wang Z Q, Xie Z Y, Cai Y F, et al. Advances in phylogenomics (in Chinese). Hereditas, 2014, 36: 669–678 [王章群, 解增言, 蔡应繁, 等. 系统发育基因组学研究进展. 遗传, 2014, 36: 669–678]. Google Scholar

[12] Lemmon E M, Lemmon A R. High-throughput genomic data in systematics and phylogenetics. Annu Rev Ecol Evol Syst, 2013, 44: 99-121 CrossRef Google Scholar

[13] McCormack J E, Hird S M, Zellmer A J, et al. Applications of next-generation sequencing to phylogeography and phylogenetics. Mol Phylogenets Evol, 2013, 66: 526-538 CrossRef PubMed Google Scholar

[14] Hackett S J, Kimball R T, Reddy S, et al. A phylogenomic study of birds reveals their evolutionary history. Science, 2008, 320: 1763-1768 CrossRef PubMed ADS Google Scholar

[15] Dunn C W, Hejnol A, Matus D Q, et al. Broad phylogenomic sampling improves resolution of the animal tree of life. Nature, 2008, 452: 745-749 CrossRef PubMed ADS Google Scholar

[16] Jung K H, Cao P, Seo Y S, et al. The rice kinase phylogenomics database: A guide for systematic analysis of the rice kinase super-family. Trends Plant Sci, 2010, 15: 595-599 CrossRef PubMed Google Scholar

[17] Chen M Y, Liang D, Zhang P. Phylogenomic resolution of the phylogeny of laurasiatherian mammals: Exploring phylogenetic signals within coding and noncoding sequences. Genome Biol Evol, 2017, 9: 1998-2012 CrossRef PubMed Google Scholar

[18] Feng Y J, Blackburn D C, Liang D, et al. Phylogenomics reveals rapid, simultaneous diversification of three major clades of Gondwanan frogs at the Cretaceous-Paleogene boundary. Proc Natl Acad Sci USA, 2017, 114: E5864-E5870 CrossRef PubMed Google Scholar

[19] Zhang S Q, Che L H, Li Y, et al. Evolutionary history of Coleoptera revealed by extensive sampling of genes and species. Nat Commun, 2018, 9: 205 CrossRef PubMed ADS Google Scholar

[20] Torruella G, de Mendoza A, Grau-Bové X, et al. Phylogenomics reveals convergent evolution of lifestyles in close relatives of animals and fungi. Curr Biol, 2015, 25: 2404-2410 CrossRef PubMed Google Scholar

[21] Rujirawat T, Patumcharoenpol P, Lohnoo T, et al. Probing the phylogenomics and putative pathogenicity genes of Pythium insidiosum by Oomycete genome analyses. Sci Rep, 2018, 8: 1-14 CrossRef PubMed ADS Google Scholar

[22] Brown M W, Heiss A A, Kamikawa R, et al. Phylogenomics places orphan protistan lineages in a novel eukaryotic super-group. Genome Biol Evol, 2018, 10: 427-433 CrossRef PubMed Google Scholar

[23] Fernández R, Kallal R J, Dimitrov D, et al. Phylogenomics, diversification dynamics, and comparative transcriptomics across the spider tree of life. Curr Biol, 2018, 28: 1489-1497.e5 CrossRef PubMed Google Scholar

[24] Regier J C, Shultz J W, Zwick A, et al. Arthropod relationships revealed by phylogenomic analysis of nuclear protein-coding sequences. Nature, 2010, 463: 1079-1083 CrossRef PubMed ADS Google Scholar

[25] Shen X X, Liang D, Feng Y J, et al. A versatile and highly efficient toolkit including 102 nuclear markers for vertebrate phylogenomics, tested by resolving the higher level relationships of the Caudata. Mol Biol Evol, 2013, 30: 2235-2248 CrossRef PubMed Google Scholar

[26] Li J N, He C, Guo P, et al. A workflow of massive identification and application of intron markers using snakes as a model. Ecol Evol, 2017, 7: 10042-10055 CrossRef PubMed Google Scholar

[27] Che L H, Zhang S Q, Li Y, et al. Genome-wide survey of nuclear protein-coding markers for beetle phylogenetics and their application in resolving both deep and shallow-level divergences. Mol Ecol Resour, 2017, 17: 1342-1358 CrossRef PubMed Google Scholar

[28] O’Neill E M, Schwartz R, Bullock C T, et al. Parallel tagged amplicon sequencing reveals major lineages and phylogenetic structure in the North American tiger salamander (Ambystoma tigrinum) species complex. Mol Ecol, 2013, 22: 111-129 CrossRef PubMed Google Scholar

[29] Bybee S M, Bracken-Grissom H, Haynes B D, et al. Targeted amplicon sequencing (TAS): A scalable next-gen approach to multilocus, multitaxa phylogenetics. Genome Biol Evol, 2011, 3: 1312-1323 CrossRef PubMed Google Scholar

[30] Wielstra B, Duijm E, Lagler P, et al. Parallel tagged amplicon sequencing of transcriptome-based genetic markers for Triturus newts with the Ion Torrent next-generation sequencing platform. Mol Ecol Resour, 2014, 160: 1080-1089 CrossRef PubMed Google Scholar

[31] Barrow L N, Ralicki H F, Emme S A, et al. Species tree estimation of North American chorus frogs (Hylidae: Pseudacris) with parallel tagged amplicon sequencing. Mol Phylogenets Evol, 2014, 75: 78-90 CrossRef PubMed Google Scholar

[32] Zieliński P, Stuglik M T, Dudek K, et al. Development, validation and high-throughput analysis of sequence markers in nonmodel species. Mol Ecol Resour, 2014, 14: 352-360 CrossRef PubMed Google Scholar

[33] Feng Y J, Liu Q F, Chen M Y, et al. Parallel tagged amplicon sequencing of relatively long PCR products using the Illumina HiSeq platform and transcriptome assembly. Mol Ecol Resour, 2016, 16: 91-102 CrossRef PubMed Google Scholar

[34] Grabherr M G, Haas B J, Yassour M, et al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat Biotech, 2011, 29: 644-652 CrossRef PubMed Google Scholar

[35] Bankevich A, Nurk S, Antipov D, et al. SPAdes: A new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol, 2012, 19: 455-477 CrossRef PubMed Google Scholar

[36] Wang Z, Gerstein M, Snyder M. RNA-Seq: A revolutionary tool for transcriptomics. Nat Rev Genet, 2009, 10: 57-63 CrossRef PubMed Google Scholar

[37] Simpson J T, Wong K, Jackman S D, et al. ABySS: A parallel assembler for short read sequence data. Genome Res, 2009, 19: 1117-1123 CrossRef PubMed Google Scholar

[38] Oakley T H, Wolfe J M, Lindgren A R, et al. Phylotranscriptomics to bring the understudied into the fold: Monophyletic Ostracoda, fossil placement, and pancrustacean phylogeny. Mol Biol Evol, 2013, 30: 215-233 CrossRef PubMed Google Scholar

[39] Wickett N J, Mirarab S, Nguyen N, et al. Phylotranscriptomic analysis of the origin and early diversification of land plants. Proc Natl Acad Sci USA, 2014, 111: E4859-E4868 CrossRef PubMed ADS Google Scholar

[40] Unruh S A, McKain M R, Lee Y I, et al. Phylotranscriptomic analysis and genome evolution of the Cypripedioideae (Orchidaceae). Am J Bot, 2018, 105: 631-640 CrossRef PubMed Google Scholar

[41] Irisarri I, Baurain D, Brinkmann H, et al. Phylotranscriptomic consolidation of the jawed vertebrate timetree. Nat Ecol Evol, 2017, 1: 1370-1378 CrossRef PubMed Google Scholar

[42] Misof B, Liu S, Meusemann K, et al. Phylogenomics resolves the timing and pattern of insect evolution. Science, 2014, 346: 763-767 CrossRef PubMed ADS Google Scholar

[43] Baird N A, Etter P D, Atwood T S, et al. Rapid SNP discovery and genetic mapping using sequenced RAD markers. PLoS ONE, 2008, 3: e3376 CrossRef PubMed ADS Google Scholar

[44] Miller M R, Dunham J P, Amores A, et al. Rapid and cost-effective polymorphism identification and genotyping using restriction site associated DNA (RAD) markers. Genome Res, 2007, 17: 240-248 CrossRef PubMed Google Scholar

[45] van Orsouw N J, Hogers R C J, Janssen A, et al. Complexity reduction of polymorphic sequences (CRoPS™): A novel approach for large-scale polymorphism discovery in complex genomes. PLoS ONE, 2007, 2: e1172 CrossRef PubMed ADS Google Scholar

[46] Van Tassell C P, Smith T P L, Matukumalli L K, et al. SNP discovery and allele frequency estimation by deep sequencing of reduced representation libraries. Nat Methods, 2008, 5: 247-252 CrossRef PubMed Google Scholar

[47] Elshire R J, Glaubitz J C, Sun Q, et al. A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS ONE, 2011, 6: e19379 CrossRef PubMed ADS Google Scholar

[48] Meier J I, Marques D A, Mwaiko S, et al. Ancient hybridization fuels rapid cichlid fish adaptive radiations. Nat Commun, 2017, 8: 14363 CrossRef PubMed ADS Google Scholar

[49] Puritz J B, Matz M V, Toonen R J, et al. Demystifying the RAD fad. Mol Ecol, 2015, 23: 5937-5942 CrossRef PubMed Google Scholar

[50] Andrews K R, Good J M, Miller M R, et al. Harnessing the power of RADseq for ecological and evolutionary genomics. Nat Rev Genet, 2016, 17: 81-92 CrossRef PubMed Google Scholar

[51] Gautier M, Gharbi K, Cezard T, et al. The effect of RAD allele dropout on the estimation of genetic variation within and between populations. Mol Ecol, 2013, 22: 3165-3178 CrossRef PubMed Google Scholar

[52] Davey J W, Cezard T, Fuentes-Utrilla P, et al. Special features of RAD sequencing data: Implications for genotyping. Mol Ecol, 2013, 22: 3151-3164 CrossRef PubMed Google Scholar

[53] Wang S, Meyer E, McKay J K, et al. 2b-RAD: A simple and flexible method for genome-wide genotyping. Nat Methods, 2012, 9: 808-810 CrossRef PubMed Google Scholar

[54] Guo Y, Yuan H, Fang D, et al. An improved 2b-RAD approach (I2b-RAD) offering genotyping tested by a rice (Oryza sativa L.) F2 population. BMC Genom, 2014, 15: 956 CrossRef PubMed Google Scholar

[55] Peterson B K, Weber J N, Kay E H, et al. Double digest RADseq: An inexpensive method for de novo SNP discovery and genotyping in model and non-model species. PLoS ONE, 2012, 7: e37135 CrossRef PubMed ADS Google Scholar

[56] Toonen R J, Puritz J B, Forsman Z H, et al. ezRAD: A simplified method for genomic genotyping in non-model organisms. PeerJ, 2013, 1: e203 CrossRef Google Scholar

[57] Ali O A, O’Rourke S M, Amish S J, et al. RAD capture (Rapture): Flexible and efficient sequence-based genotyping. Genetics, 2016, 202: 389-400 CrossRef PubMed Google Scholar

[58] Gnirke A, Melnikov A, Maguire J, et al. Solution hybrid selection with ultra-long oligonucleotides for massively parallel targeted sequencing. Nat Biotech, 2009, 27: 182-189 CrossRef PubMed Google Scholar

[59] Burbano H A, Hodges E, Green R E, et al. Targeted investigation of the neandertal genome by array-based sequence capture. Science, 2010, 328: 723-725 CrossRef PubMed ADS Google Scholar

[60] Briggs A W, Good J M, Green R E, et al. Primer extension capture: Targeted sequence retrieval from heavily degraded DNA sources. J Vis Exp, 2009, 3: 1573 CrossRef PubMed Google Scholar

[61] Lemmon A R, Emme S A, Lemmon E M. Anchored hybrid enrichment for massively high-throughput phylogenomics. Syst Biol, 2012, 61: 727-744 CrossRef PubMed Google Scholar

[62] Faircloth B C, McCormack J E, Crawford N G, et al. Ultraconserved elements anchor thousands of genetic markers spanning multiple evolutionary timescales. Syst Biol, 2012, 61: 717-726 CrossRef PubMed Google Scholar

[63] George R D, McVicker G, Diederich R, et al. Trans genomic capture and sequencing of primate exomes reveals new targets of positive selection. Genome Res, 2011, 21: 1686-1694 CrossRef PubMed Google Scholar

[64] Bi K, Vanderpool D, Singhal S, et al. Transcriptome-based exon capture enables highly cost-effective comparative genomic data collection at moderate evolutionary scales. BMC Genom, 2012, 13: 403 CrossRef PubMed Google Scholar

[65] Li C, Hofreiter M, Straube N, et al. Capturing protein-coding genes across highly divergent species. Biotechniques, 2013, 54: 321-326 CrossRef PubMed Google Scholar

[66] Maricic T, Whitten M, Pääbo S. Multiplexed DNA sequence capture of mitochondrial genomes using PCR products. PLoS ONE, 2010, 5: e14004 CrossRef PubMed ADS Google Scholar

[67] Mariac C, Scarcelli N, Pouzadou J, et al. Cost-effective enrichment hybridization capture of chloroplast genomes at deep multiplexing levels for population genetics and phylogeography studies. Mol Ecol Resour, 2014, 14: 1103-1113 CrossRef PubMed Google Scholar

[68] Peñalba J V, Smith L L, Tonione M A, et al. Sequence capture using PCR-generated probes: A cost-effective method of targeted high-throughput sequencing for nonmodel organisms. Mol Ecol Resour, 2014, 496: 1000-1010 CrossRef PubMed Google Scholar

[69] McCormack J E, Tsai W L E, Faircloth B C. Sequence capture of ultraconserved elements from bird museum specimens. Mol Ecol Resour, 2016, 16: 1189-1203 CrossRef PubMed Google Scholar

[70] Blaimer B B, Lloyd M W, Guillory W X, et al. Sequence capture and phylogenetic utility of genomic ultraconserved elements obtained from pinned insect specimens. PLoS ONE, 2016, 11: e0161531 CrossRef PubMed ADS Google Scholar

[71] Dornburg A, Townsend J P, Brooks W, et al. New insights on the sister lineage of percomorph fishes with an anchored hybrid enrichment dataset. Mol Phylogenets Evol, 2017, 110: 27-38 CrossRef PubMed Google Scholar

[72] Alfaro M E, Faircloth B C, Harrington R C, et al. Explosive diversification of marine fishes at the Cretaceous-Palaeogene boundary. Nat Ecol Evol, 2018, 2: 688-696 CrossRef PubMed Google Scholar

[73] Espeland M, Breinholt J, Willmott K R, et al. A comprehensive and dated phylogenomic analysis of butterflies. Curr Biol, 2018, 28: 770-778.e5 CrossRef PubMed Google Scholar

[74] Prum R O, Berv J S, Dornburg A, et al. A comprehensive phylogeny of birds (Aves) using targeted next-generation DNA sequencing. Nature, 2015, 526: 569-573 CrossRef PubMed ADS Google Scholar

[75] Jones M R, Good J M. Targeted capture in evolutionary and ecological genomics. Mol Ecol, 2016, 25: 185-202 CrossRef PubMed Google Scholar

[76] Allen J M, Boyd B, Nguyen N P, et al. Phylogenomics from whole genome sequences using aTRAM. Syst Biol, 2017, 105: 786-798 CrossRef PubMed Google Scholar

[77] Allen J M, Huang D I, Cronk Q C, et al. aTRAM-automated target restricted assembly method: A fast method for assembling loci across divergent taxa from next-generation sequencing data. BMC Bioinf, 2015, 16: 98 CrossRef PubMed Google Scholar

[78] Boyd B M, Allen J M, Nguyen N P, et al. Phylogenomics using target-restricted assembly resolves intra-generic relationships of parasitic lice (Phthiraptera: Columbicola). Syst Biol, 2017, 66: 896-911 CrossRef PubMed Google Scholar

[79] Bolger A M, Lohse M, Usadel B. Trimmomatic: A flexible trimmer for illumina sequence data. Bioinformatics, 2014, 30: 2114-2120 CrossRef PubMed Google Scholar

[80] Langmead B, Salzberg S L. Fast gapped-read alignment with Bowtie 2. Nat Methods, 2012, 9: 357-359 CrossRef PubMed Google Scholar

[81] Pertea M, Pertea G M, Antonescu C M, et al. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat Biotech, 2015, 33: 290-295 CrossRef PubMed Google Scholar

[82] Trapnell C, Williams B A, Pertea G, et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotech, 2010, 28: 511-515 CrossRef PubMed Google Scholar

[83] Xie Y, Wu G, Tang J, et al. SOAPdenovo-Trans: De novo transcriptome assembly with short RNA-Seq reads. Bioinformatics, 2014, 30: 1660-1666 CrossRef PubMed Google Scholar

[84] Luo R, Liu B, Xie Y, et al. SOAPdenovo2: An empirically improved memory-efficient short-read de novo assembler. Gigascience, 2012, 1: 18 CrossRef PubMed Google Scholar

[85] Zerbino D R, Birney E. Velvet: Algorithms for de novo short read assembly using de Bruijn graphs. Genome Res, 2008, 18: 821-829 CrossRef PubMed Google Scholar

[86] Dierckxsens N, Mardulyn P, Smits G. NOVOPlasty: De novo assembly of organelle genomes from whole genome data. Nucl Acids Res, 2016, 53: 955 CrossRef PubMed Google Scholar

[87] Hahn C, Bachmann L, Chevreux B. Reconstructing mitochondrial genomes directly from genomic next-generation sequencing reads—a baiting and iterative mapping approach. Nucl Acids Res, 2013, 41: e129 CrossRef PubMed Google Scholar

[88] Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics, 2009, 25: 1754-1760 CrossRef PubMed Google Scholar

[89] Chen F, Mackey A J, Stoeckert C J, et al. OrthoMCL-DB: Querying a comprehensive multi-species collection of ortholog groups. Nucl Acids Res, 2006, 34: D363-D368 CrossRef PubMed Google Scholar

[90] Waterhouse R M, Tegenfeldt F, Li J, et al. OrthoDB: A hierarchical catalog of animal, fungal and bacterial orthologs. Nucl Acids Res, 2013, 41: D358-D365 CrossRef PubMed Google Scholar

[91] Altenhoff A M, Schneider A, Gonnet G H, et al. OMA 2011: Orthology inference among 1000 complete genomes. Nucl Acids Res, 2011, 39: D289-D294 CrossRef PubMed Google Scholar

[92] Sonnhammer E L L, Östlund G. InParanoid 8: Orthology analysis between 273 proteomes, mostly eukaryotic. Nucl Acids Res, 2015, 43: D234-D239 CrossRef PubMed Google Scholar

[93] Masterson J. Stomatal size in fossil plants: Evidence for polyploidy in majority of angiosperms. Science, 1994, 264: 421-424 CrossRef PubMed ADS Google Scholar

[94] Wendel J F. Genome evolution in polyploids. Plant Mol Biol, 2000, 42: 225-249 CrossRef Google Scholar

[95] Katoh K, Standley D M. MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. Mol Biol Evol, 2013, 30: 772-780 CrossRef PubMed Google Scholar

[96] Larkin M A, Blackshields G, Brown N P, et al. Clustal W and Clustal X version 2.0. Bioinformatics, 2007, 23: 2947-2948 CrossRef PubMed Google Scholar

[97] Edgar R C. MUSCLE: Multiple sequence alignment with high accuracy and high throughput. Nucl Acids Res, 2004, 32: 1792-1797 CrossRef PubMed Google Scholar

[98] Liu K, Raghavan S, Nelesen S, et al. Rapid and accurate large-scale coestimation of sequence alignments and phylogenetic trees. Science, 2009, 324: 1561-1564 CrossRef PubMed ADS Google Scholar

[99] Mirarab S, Nguyen N, Guo S, et al. PASTA: Ultra-large multiple sequence alignment for nucleotide and amino-acid sequences. J Comput Biol, 2015, 22: 377-386 CrossRef PubMed Google Scholar

[100] Simion P, Philippe H, Baurain D, et al. A large and consistent phylogenomic dataset supports sponges as the sister group to all other animals. Curr Biol, 2017, 27: 958-967 CrossRef PubMed Google Scholar

[101] Shen X X, Hittinger C T, Rokas A. Contentious relationships in phylogenomic studies can be driven by a handful of genes. Nat Ecol Evol, 2017, 1: 0126 CrossRef PubMed Google Scholar

[102] Lanfear R, Calcott B, Ho S Y W, et al. PartitionFinder: Combined selection of partitioning schemes and substitution models for phylogenetic analyses. Mol Biol Evol, 2012, 29: 1695-1701 CrossRef PubMed Google Scholar

[103] Lanfear R, Frandsen P B, Wright A M, et al. PartitionFinder 2: New methods for selecting partitioned models of evolution for molecular and morphological phylogenetic analyses. Mol Biol Evol, 2016, 34: 772-773 CrossRef PubMed Google Scholar

[104] Posada D, Crandall K A. Modeltest: Testing the model of DNA substitution. Bioinformatics, 1998, 14: 817-818 CrossRef Google Scholar

[105] Posada D. jModelTest: Phylogenetic model averaging. Mol Biol Evol, 2008, 25: 1253-1256 CrossRef PubMed Google Scholar

[106] Abascal F, Zardoya R, Posada D. ProtTest: Selection of best-fit models of protein evolution. Bioinformatics, 2005, 21: 2104-2105 CrossRef PubMed Google Scholar

[107] Stamatakis A. RAxML version 8: A tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics, 2014, 30: 1312-1313 CrossRef PubMed Google Scholar

[108] Nguyen L T, Schmidt H A, von Haeseler A, et al. IQ-TREE: A fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol Biol Evol, 2015, 32: 268-274 CrossRef PubMed Google Scholar

[109] Minh B Q, Nguyen M A T, von Haeseler A. Ultrafast approximation for phylogenetic bootstrap. Mol Biol Evol, 2013, 30: 1188-1195 CrossRef PubMed Google Scholar

[110] Ronquist F, Teslenko M, van der Mark P, et al. Mrbayes 3.2: Efficient bayesian phylogenetic inference and model choice across a large model space. Syst Biol, 2012, 61: 539-542 CrossRef PubMed Google Scholar

[111] Lartillot N, Lepage T, Blanquart S. PhyloBayes 3: A Bayesian software package for phylogenetic reconstruction and molecular dating. Bioinformatics, 2009, 25: 2286-2288 CrossRef PubMed Google Scholar

[112] Aberer A J, Kobert K, Stamatakis A. ExaBayes: Massively parallel bayesian tree inference for the whole-genome era. Mol Biol Evol, 2014, 31: 2553-2556 CrossRef PubMed Google Scholar

[113] Song S, Liu L, Edwards S V, et al. Resolving conflict in eutherian mammal phylogeny using phylogenomics and the multispecies coalescent model. Proc Natl Acad Sci USA, 2012, 109: 14942-14947 CrossRef PubMed ADS Google Scholar

[114] Liu L, Yu L, Edwards S V. A maximum pseudo-likelihood approach for estimating species trees under the coalescent model. BMC Evol Biol, 2010, 10: 302 CrossRef PubMed Google Scholar

[115] Mirarab S, Reaz R, Bayzid M S, et al. ASTRAL: Genome-scale coalescent-based species tree estimation. Bioinformatics, 2014, 30: i541-i548 CrossRef PubMed Google Scholar

[116] Larget B R, Kotha S K, Dewey C N, et al. BUCKy: Gene tree/species tree reconciliation with Bayesian concordance analysis. Bioinformatics, 2010, 26: 2910-2911 CrossRef PubMed Google Scholar

[117] Ane C, Larget B, Baum D A, et al. Bayesian estimation of concordance among gene trees. Mol Biol Evol, 2007, 24: 1575 CrossRef Google Scholar

[118] Heled J, Drummond A J. Bayesian inference of species trees from multilocus data. Mol Biol Evol, 2010, 27: 570-580 CrossRef PubMed Google Scholar

[119] Bao J, Xia H, Zhou J, et al. Efficient implementation of MrBayes on multi-GPU. Mol Biol Evol, 2013, 30: 1471-1479 CrossRef PubMed Google Scholar

[120] Bapteste E, Brinkmann H, Lee J A, et al. The analysis of 100 genes supports the grouping of three highly divergent amoebae: Dictyostelium, Entamoeba, and Mastigamoeba. Proc Natl Acad Sci USA, 2002, 99: 1414-1419 CrossRef PubMed ADS Google Scholar

[121] Madsen O, Scally M, Douady C J, et al. Parallel adaptive radiations in two major clades of placental mammals. Nature, 2001, 409: 610-614 CrossRef PubMed Google Scholar

[122] Rokas A, Williams B L, King N, et al. Genome-scale approaches to resolving incongruence in molecular phylogenies. Nature, 2003, 425: 798-804 CrossRef PubMed Google Scholar

[123] Lockhart P J, Steel M A, Hendy M D, et al. Recovering evolutionary trees under a more realistic model of sequence evolution. Mol Biol Evol, 1994, 11: 605–612. Google Scholar

[124] Liu Q, Feng Y, Xue Q. Analysis of factors shaping codon usage in the mitochondrion genome of. Mitochondrion, 2004, 4: 313-320 CrossRef PubMed Google Scholar

[125] Behura S K, Severson D W. Codon usage bias: Causative factors, quantification methods and genome-wide patterns: With emphasis on insect genomes. Biol Rev, 2013, 88: 49-61 CrossRef PubMed Google Scholar

[126] Plotkin J B, Kudla G. Synonymous but not the same: The causes and consequences of codon bias. Nat Rev Genet, 2011, 12: 32-42 CrossRef PubMed Google Scholar

[127] Andreasen K, Baldwin B G. Unequal evolutionary rates between annual and perennial lineages of checker mallows (Sidalcea, Malvaceae): Evidence from 18S–26S rDNA internal and external transcribed spacers. Mol Biol Evol, 2001, 18: 936-944 CrossRef PubMed Google Scholar

[128] Philippe H, Zhou Y, Brinkmann H, et al. Heterotachy and long-branch attraction in phylogenetics. BMC Evol Biol, 2005, 5: 50 CrossRef PubMed Google Scholar

[129] Wiens J J. Can incomplete taxa rescue phylogenetic analyses from long-branch attraction?. Syst Biol, 2005, 54: 731-742 CrossRef PubMed Google Scholar

[130] Lartillot N, Philippe H ́. A bayesian mixture model for across-site heterogeneities in the amino-acid replacement process. Mol Biol Evol, 2004, 21: 1095-1109 CrossRef PubMed Google Scholar

[131] Kolaczkowski B, Thornton J W. Performance of maximum parsimony and likelihood phylogenetics when evolution is heterogeneous. Nature, 2004, 431: 980-984 CrossRef PubMed ADS Google Scholar

[132] Philippe H, Germot A. Phylogeny of eukaryotes based on ribosomal RNA: Long-branch attraction and models of sequence evolution. Mol Biol Evol, 2000, 17: 830-834 CrossRef PubMed Google Scholar

[133] Philippe H, Brinkmann H, Lavrov D V, et al. Resolving difficult phylogenetic questions: Why more sequences are not enough. PLoS Biol, 2011, 9: e1000602 CrossRef PubMed Google Scholar

[134] Salichos L, Rokas A. Inferring ancient divergences requires genes with strong phylogenetic signals. Nature, 2013, 497: 327-331 CrossRef PubMed ADS Google Scholar

[135] Chen M Y, Liang D, Zhang P. Selecting question-specific genes to reduce incongruence in phylogenomics: A case study of jawed vertebrate backbone phylogeny. Syst Biol, 2015, 64: 1104-1120 CrossRef PubMed Google Scholar

[136] Wang Y H, Wu H Y, Rédei D, et al. When did the ancestor of true bugs become stinky? Disentangling the phylogenomics of Hemiptera-Heteroptera. Cladistics, 2019, 35: 42-66 CrossRef Google Scholar

[137] i5K Consortium. The i5K Initiative: Advancing arthropod genomics for knowledge, human health, agriculture, and the environment. J Hered, 2013, 104: 595–600. Google Scholar

  • Figure 1

    The bioinformatic pipeline for analyzing high-throughput phylogenetic data. Beginning with high-throughput data, Fillet square boxes and arrows represent the order and steps of data analysis. Gray dotted lines and square boxes represent common used software and methods (color online)

  • Table 1   Comparison of five RAD-seq technologies





















    100 ng~1 μg

    低至1 ng

    50 ng~1 μg

    50 ng~100 ng

    50 ng








    ≤300 bp

    33~36 bp

    ≤300 bp

    ≤300 bp

    ≤300 bp


    ~24 h

    ~4 h

    ~6 h

    ~8 h

    ~30 h

















  • Table 2   Comparison of four target sequence capture approaches












    ~123 KB







    ~352 KB







    ~25.3 MB






    ~4 MB






    ~400 KB







    ~17 KB






    ~146 KB






    ~52 KB



  • Table 3   Comparison of five methods of data collection in phylogenomics based on high-throughput sequencing























    50~200 KB

    1~20 MB


    1.5~26 KB

    100 KB以上


    DNA片段>5 KB


    DNA片段>20 KB

    DNA片段>200 bp

    DNA片段>200 bp

















Contact and support