UPSI Digital Repository (UDRep)
Start | FAQ | About
Menu Icon

QR Code Link :

Type :Thesis
Subject :QH Natural history
Main Author :Wong, Ee Bhei
Title :The supermatrix approach on inferring phylogeny through bioinformatics framework
Hits :50
Place of Production :Tanjong Malim
Publisher :Fakulti Sains dan Matematik
Year of Publication :2024
Corporate Name :Perpustakaan Tuanku Bainun
PDF Guest :Click to view PDF file
PDF Full Text :You have no permission to view this item.

Abstract : Perpustakaan Tuanku Bainun
The objective of the study was to develop a supermatrix framework by incorporating the unique evolutionary signals of each gene that can improve phylogenetic inferences. The aim was to address the issue of the supermatrix approach, which potentially disregards individual gene properties. The study involved two main experiments: i) Experiment 1 focused on the effect of evolutionary signals across housekeeping genes on phylogeny inference using Chlorellaceae species. ii) Experiment 2 involved the development of a bioinformatics framework using the supermatrix approach. The framework incorporates unique properties of each gene in phylogenetic tree construction, such as sequence heterogeneity, sequence informative sites, taxonomic conflicts at the kingdom level, and tree distance between the gene tree and species tree. The genes were concatenated, based on their similar gene properties, into a supermatrix for phylogeny inference. Generated phylogenetic tree was compared with tree-of-life, which was used as a benchmark dataset, to validate the usability of the supermatrix framework. Our findings revealed that each individual housekeeping gene has different evolutionary signals, and ignoring these signals would affect the inferred phylogeny. The developed bioinformatics framework demonstrated an improvement in the accuracy of the inferred phylogenetic trees compared to the conventional tree inference approach based on Robinson-Foulds tree distance and Shimodaira Hasegawa test. This framework also corroborates with previous studies, which suggest that incorporating more genes in the supermatrix approach can enhance phylogenetic inference. Analysing individual gene properties by considering the unique evolutionary signals in gene concatenation through the supermatrix approach could improve phylogenetic inferences. The improvement of using the supermatrix approach could enhance the understanding of the evolutionary relationships between species, which further could be applied in various fields such as biodiversity conservation, medicine and healthcare.

References

Abhilash, M., & Rohitaksha. (2014). A Comparative Study on Global and Local Alignment Algorithm. Semantic Scholar. 

 

Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716–723. https://doi.org/10.1109/TAC.1974.1100705 

 

Alam, F., Mobin, S., & Chowdhury, H. (2015). Third Generation Biofuel from Algae. Procedia Engineering, 105, 763–768. https://doi.org/10.1016/j.proeng.2015.05.068 

 

Al-Mandhari, A., Barakat, A., Abubakar, A., & Brennan, R. (2022). Genomic sequencing for epidemic and pandemic preparedness and response: EMRO’s vision and strategic interventions. Eastern Mediterranean Health Journal, 28(12), 851–852. https://doi.org/10.26719/2022.28.12.851 

 

Altschul, S. F., Gish, W., Miller, W., Myers, E. W., & Lipman, D. J. (1990). Basic local alignment search tool. Journal of Molecular Biology, 215(3), 403–410. https://doi.org/10.1016/S0022-2836(05)80360-2 

 

Bakker, F. T., Culham, A., Gomez-Martinez*, R., Carvalho, J., Compton, J., Dawtrey, R., & Gibby, M. (2000). Patterns of Nucleotide Substitution in Angiosperm cpDNA trnL (UAA)–trnF (GAA) Regions. Molecular Biology and Evolution, 17(8), 1146–1155. https://doi.org/10.1093/oxfordjournals.molbev.a026397 

 

Berkemer, S. J., Siederdissen, C. H. Z., & Stadler, P. (2018). Alignments as Compositional Structures. https://www.semanticscholar.org/paper/Alignments-as-Compositional-Structures-Berkemer-Siederdissen/17419625c298a288856d4aa9bc653824ca2566f5 

 

Betancur, R., Naylor, G. J. P., & Ortí, G. (2014). Conserved genes, sampling error, and phylogenomic inference. Systematic Biology, 63(2), 257–262. https://doi.org/10.1093/sysbio/syt073 

 

Betancur-R., R., Broughton, R. E., Wiley, E. O., Carpenter, K., López, J. A., Li, C., et al., (2013). The Tree of Life and a New Classification of Bony Fishes. PLoS Currents, 5. https://doi.org/10.1371/currents.tol.53ba26640df0ccaee75bb165c8c26288 

 

Bininda-Emonds, O. R. P., & Sanderson, M. J. (2001). Asessment of the Accuracy of Matrix Representation with Parsimony Analysis Supertree Construction. SYSTEMATIC BIOLOGY, 50(4), 565–579. 

 

Birdsell, J. A. (2002). Integrating Genomics, Bioinformatics, and Classical Genetics to Study the Effects of Recombination on Genome Evolution. Molecular Biology and Evolution, 19(7), 1181–1197. https://doi.org/10.1093/oxfordjournals.molbev.a004176 

 

Bock, C., Pröschold, T., & Krienitz, L. (2011). Updating the genus Dictyosphaerium and description of Mucidosphaerium gen. Nov. (Trebouxiophyceae) based on morphological and molecular data. Journal of Phycology, 47(3), 638–652. https://doi.org/10.1111/j.1529-8817.2011.00989.x 

 

Boeckmann, B., Marcet-Houben, M., Rees, J. A., Forslund, K., Huerta-Cepas, J., Muffato, M., et al. (2015). Quest for Orthologs Entails Quest for Tree of Life: In Search of the Gene 

Stream. Genome Biology and Evolution, 7(7), 1988–1999. https://doi.org/10.1093/gbe/evv121 

 

Boussau, B., Szöllosi, G. J., Duret, L., Gouy, M., Tannier, E., & Daubin, V. (2013). Genome-scale coestimation of species and gene trees. Genome Research, 23(2), 323–330. https://doi.org/10.1101/gr.141978.112 

 

Bull, J. J., & Swofford, D. L. (1993). Partitioning and Combining Data in Phylogenetic Analysis. Systematic Biology. 

 

Burleigh, J. G. (2016). Supertree Methods, Phylogenetic. 4, 250–255. https://doi.org/10.1016/B978-0-12-800049-6.00222-5 

 

Cavanaugh, J. E. (1997). Unifying the derivations for the Akaike and corrected Akaike information criteria. Statistics & Probability Letters, 33(2), 201–208. https://doi.org/10.1016/S0167-7152(96)00128-9 

 

Certnerová, D. (2021). Nuclei isolation protocols for flow cytometry allowing nuclear DNA content estimation in problematic microalgal groups. Journal of Applied Phycology, 33, 2057–2067. https://doi.org/10.1007/s10811-021-02433-z 

 

Chaliotis, A., Vlastaridis, P., Mossialos, D., Ibba, M., Becker, H. D., Stathopoulos, C., et al., (2017). The complex evolutionary history of aminoacyl-tRNA synthetases. Nucleic Acids Research, 45(3), 1059–1068. https://doi.org/10.1093/nar/gkw1182 

 

Choudhuri, S. (2014). Bioinformatics for Beginners: Phylogenetic Analysis. Genes, Genomes, Molecular Evolution, Databases and Analytical Tools, 209–218. https://doi.org/10.1016/B978-0-12-410471-6.00009-8 

 

Ciccarelli, F. D., Doerks, T., von Mering, C., Creevey, C. J., Snel, B., & Bork, P. (2006). Toward Automatic Reconstruction of a Highly Resolved Tree of Life. Science, 311(5765), 1283–1287. https://doi.org/10.1126/science.1123061 

 

Darienko, T., & Pröschold, T. (2015). Genetic variability and taxonomic revision of the genus Auxenochlorella (Shihira et Krauss) Kalina et Puncocharova (Trebouxiophyceae, Chlorophyta). Journal of Phycology, 51(2), 394–400. https://doi.org/10.1111/jpy.12279 

 

Darienko, T., Rad-Menéndez, C., Campbell, C., & Pröschold, T. (2019). Are there any true marine Chlorella species? Molecular phylogenetic assessment and ecology of marine Chlorella-like organisms, including a description of Droopiella gen. Nov. Systematics and Biodiversity, 17(8), 811–829. https://doi.org/10.1080/14772000.2019.1690597 

 

Darriba, D., Posada, D., Kozlov, A. M., Stamatakis, A., Morel, B., & Flouri, T. (2020). ModelTest-NG: A New and Scalable Tool for the Selection of DNA and Protein Evolutionary Models. Molecular Biology and Evolution, 37(1), 291–294. https://doi.org/10.1093/molbev/msz189 

 

Das, B., & Deka, S. (2019). A cost-effective and environmentally sustainable process for phycoremediation of oil field formation water for its safe disposal and reuse. Scientific Reports, 9(1), 1–15. https://doi.org/10.1038/s41598-019-51806-5 

 

Davis, B. W., Li, G., & Murphy, W. J. (2010). Supermatrix and species tree methods resolve phylogenetic relationships within the big cats, Panthera (Carnivora: Felidae). 

Molecular Phylogenetics and Evolution, 56(1), 64–76. https://doi.org/10.1016/j.ympev.2010.01.036 

 

Dayhoff, M. O., Schwartz, R. M., & Orcutt, B. C. (1978). A Model of Evolutionary Change in Proteins. In Atlas of Protein Sequence and Structure (pp. 345–352). Natl. Biomed. Res. Found. 

 

Degnan, J. H., & Rosenberg, N. A. (2006). Discordance of Species Trees with Their Most Likely Gene Trees. PLoS Genetics, 2(5). https://doi.org/10.1371/journal.pgen.0020068 

 

Del Amparo, R., & Arenas, M. (2022). Consequences of Substitution Model Selection on Protein Ancestral Sequence Reconstruction. Molecular Biology and Evolution, 39(7), msac144. https://doi.org/10.1093/molbev/msac144 

 

Dequeiroz, A., & Gatesy, J. (2007). The supermatrix approach to systematics. Trends in Ecology & Evolution, 22(1), 34–41. https://doi.org/10.1016/j.tree.2006.10.002 

 

Dewey, C. N., & Pachter, L. (2006). Evolution at the nucleotide level: The problem of multiple whole-genome alignment. Human Molecular Genetics, 15(suppl_1), R51–R56. https://doi.org/10.1093/hmg/ddl056 

 

Dohm, J. C., Vingron, M., & Staub, E. (2006). Horizontal Gene Transfer in Aminoacyl-tRNA Synthetases Including Leucine-Specific Subtypes. Journal of Molecular Evolution, 63(4), 437–447. https://doi.org/10.1007/s00239-005-0094-3 

 

Dong, W., Li, E., Liu, Y., Xu, C., Wang, Y., Liu, K., et al. (2022). Phylogenomic approaches untangle early divergences and complex diversifications of the olive plant family. BMC Biology, 20(1), 92. https://doi.org/10.1186/s12915-022-01297-0 

 

Eddy, S. R. (1998). Profile hidden Markov models. Bioinformatics, 14(9), 755–763. https://doi.org/10.1093/bioinformatics/14.9.755 

 

Edgar, R. C., Drive, R. M., & Valley, M. (2004). MUSCLE: multiple sequence alignment with high accuracy and high throughput. 32(5), 1792–1797. https://doi.org/10.1093/nar/gkh340 

 

Edwards, S. V., Xi, Z., Janke, A., Faircloth, B. C., McCormack, J. E., Glenn, T. C., et al. (2016). Implementing and testing the multispecies coalescent model: A valuable paradigm for phylogenomics. Molecular Phylogenetics and Evolution, 94, 447–462. https://doi.org/10.1016/J.YMPEV.2015.10.027 

 

Eisenberg, E., & Levanon, E. Y. (2013). Human housekeeping genes, revisited. Trends in Genetics, 29(10), 569–574. https://doi.org/10.1016/j.tig.2013.05.010 

 

Emms, D. M., & Kelly, S. (2019). OrthoFinder: Phylogenetic orthology inference for comparative genomics. Genome Biology, 20(1), 238. https://doi.org/10.1186/s13059-019-1832-y 

 

Fang, L., Leliaert, F., Novis, P. M., Zhang, Z., Zhu, H., Liu, G., et al (2018). Improving phylogenetic inference of core Chlorophyta using chloroplast sequences with strong phylogenetic signals and heterogeneous models. Molecular Phylogenetics and Evolution, 127, 248–255. https://doi.org/10.1016/j.ympev.2018.06.006 

 

Felsenstein, J. (1985). Confidence Limits on Phylogenies: An Approach Using the Bootstrap. Evolution, 39(4), 783–791. https://doi.org/10.1111/j.1558-5646.1985.tb00420.x 

 

Feng, P., Xu, Z., Qin, L., Asraful Alam, M., Wang, Z., & Zhu, S. (2020). Effects of different nitrogen sources and light paths of flat plate photobioreactors on the growth and lipid accumulation of Chlorella sp. GN1 outdoors. Bioresource Technology, 301, 122762. https://doi.org/10.1016/j.biortech.2020.122762 

 

Fitch, W. M. (1967). Evidence suggesting a non-random character to nucleotide replacements in naturally occurring mutations. Journal of Molecular Biology, 26(3), 499–507. https://doi.org/10.1016/0022-2836(67)90317-8 

 

Fontes, J. T., Vieira, P. E., Ekrem, T., Soares, P., & Costa, F. O. (2021). BAGS: An automated Barcode, Audit & Grade System for DNA barcode reference libraries. Molecular Ecology Resources, 21(2), 573–583. https://doi.org/10.1111/1755-0998.13262 

 

Forterre, P. (2015). The universal tree of life: An update. Frontiers in Microbiology, 6, 717. https://doi.org/10.3389/fmicb.2015.00717 

 

Francis, W. R., & Canfield, D. E. (2020). Very few sites can reshape the inferred phylogenetic tree. PeerJ, 8, e8865. https://doi.org/10.7717/peerj.8865 

 

Galperin, M. Y., Kristensen, D. M., Makarova, K. S., Wolf, Y. I., & Koonin, E. V. (2017). Microbial genome analysis: The COG approach. Briefings in Bioinformatics, 20(4), 1063–1070. https://doi.org/10.1093/bib/bbx117 

 

Galperin, M. Y., Wolf, Y. I., Garushyants, S. K., Vera Alvarez, R., & Koonin, E. V. (2021). Nonessential Ribosomal Proteins in Bacteria and Archaea Identified Using Clusters of Orthologous Genes. Journal of Bacteriology, 203(11). https://doi.org/10.1128/JB.00058-21 

 

Galperin, M. Y., Wolf, Y. I., Makarova, K. S., Vera Alvarez, R., Landsman, D., & Koonin, E. V. (2021). COG database update: Focus on microbial diversity, model organisms, and widespread pathogens. Nucleic Acids Research, 49(D1), D274–D281. https://doi.org/10.1093/nar/gkaa1018 

 

Galtier, N., & Daubin, V. (2008). Dealing with incongruence in phylogenomic analyses. Philosophical Transactions of the Royal Society B: Biological Sciences, 363(1512), 4023–4029. https://doi.org/10.1098/rstb.2008.0144 

 

Gatesy, J., Matthee, C., DeSalle, R., & Hayashi, C. (2002). Resolution of a Supertree/Supermatrix Paradox. Systematic Biology, 51(4), 652–664. https://doi.org/10.1080/10635150290102311 

 

Gatesy, J., Sloan, D. B., Warren, J. M., Baker, R. H., Simmons, M. P., & Springer, M. S. (2019). Partitioned coalescence support reveals biases in species-tree methods and detects gene trees that determine phylogenomic conflicts. Molecular Phylogenetics and Evolution, 139, 106539. https://doi.org/10.1016/j.ympev.2019.106539 

 

Goloboff, P. A., & Szumik, C. A. (2016). Problems with supertrees based on the subtree prune-and-regraft distance, with comments on majority rule supertrees. Cladistics, 32(1), 82–89. https://doi.org/10.1111/cla.12111 

 

Guccione, A., Biondi, N., Sampietro, G., Rodolfi, L., Bassi, N., & Tredici, M. R. (2014). Chlorella for protein and biofuels: From strain selection to outdoor cultivation in a Green Wall Panel photobioreactor. Biotechnology for Biofuels, 7(1), 1–12. https://doi.org/10.1186/1754-6834-7-84 

 

Guindon, S., Dufayard, J.-F., Lefort, V., Anisimova, M., Hordijk, W., & Gascuel, O. (2010). New Algorithms and Methods to Estimate Maximum-Likelihood Phylogenies: Assessing the Performance of PhyML 3.0. Systematic Biology, 59(3), 307–321. https://doi.org/10.1093/sysbio/syq010 

 

Guiry, M. D., & Guiry, G. M. (2021). AlgaeBase. World-Wide Electronic Publication. http://www.algaebase.org 

 

Hall, R. J., Whelan, F. J., McInerney, J. O., Ou, Y., & Domingo-Sananes, M. R. (2020). Horizontal Gene Transfer as a Source of Conflict and Cooperation in Prokaryotes. Frontiers in Microbiology, 11. https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2020.01569 

 

Harris, J. K., Kelley, S. T., Spiegelman, G. B., & Pace, N. R. (2003). The Genetic Core of the Universal Ancestor. Genome Research, 13(3), 407–412. https://doi.org/10.1101/gr.652803 

 

Heeg, J. S., & Wolf, M. (2015). ITS2 and 18S rDNA sequence-structure phylogeny of Chlorella and allies (Chlorophyta, Trebouxiophyceae, Chlorellaceae). Plant Gene, 4, 20–28. https://doi.org/10.1016/j.plgene.2015.08.001 

 

Hess, J., & Goldman, N. (2011). Addressing Inter-Gene Heterogeneity in Maximum Likelihood Phylogenomic Analysis: Yeasts Revisited. PLoS ONE, 6(8), e22783. https://doi.org/10.1371/journal.pone.0022783 

 

Hillis, D. M., & Bull, J. J. (1993). An Empirical Test of Bootstrapping as a Method for Assessing Confidence in Phylogenetic Analysis. Systematic Biology, 42(2), 182–192. https://doi.org/10.1093/sysbio/42.2.182 

 

Hinchliff, C. E., & Roalson, E. H. (2013). Using supermatrices for phylogenetic inquiry: An example using the sedges. Systematic Biology, 62(2), 205–219. https://doi.org/10.1093/sysbio/sys088 

 

Horiike, T. (2016). An Introduction to Molecular Phylogenetic Analysis. Reviews in Agricultural Science, 4(0), 36–45. https://doi.org/10.7831/ras.4.0_36 

 

Horreo, J. L. (2012). ‘Representative Genes’, is it OK to use a small amount of data to obtain a phylogeny that is at least close to the true tree? Journal of Evolutionary Biology, 25(12), 2661–2664. https://doi.org/10.1111/j.1420-9101.2012.02622.x 

 

Hounkpe, B. W., Chenou, F., de Lima, F., & De Paula, E. V. (2021). HRT Atlas v1.0 database: Redefining human and mouse housekeeping genes and candidate reference transcripts by mining massive RNA-seq datasets. Nucleic Acids Research, 49(D1), D947–D955. https://doi.org/10.1093/nar/gkaa609 

 

Huerta-Cepas, J., Serra, F., & Bork, P. (2016). ETE 3: Reconstruction, Analysis, and Visualization of Phylogenomic Data. Molecular Biology and Evolution, 33(6), 1635–1638. https://doi.org/10.1093/molbev/msw046 

 

Hug, L. A., Baker, B. J., Anantharaman, K., Brown, C. T., Probst, A. J., Castelle, C. J., et al. (2016). A new view of the tree of life. Nature Microbiology. https://doi.org/10.1038/nmicrobiol.2016.48 

 

Ismail, M., Ahmad, A., Nadeem, M., Javed, M. A., Khan, S. H., Khawaish, I., et al. (2020). Development of DNA barcodes for selected Acacia species by using rbcL and matK DNA markers. Saudi Journal of Biological Sciences, 27(12), 3735–3742. https://doi.org/10.1016/j.sjbs.2020.08.020 

 

Jagadesh Kumar, M. (2023). Origin of Species: Darwin and Beyond. IETE Technical Review, 40(3), 285–286. https://doi.org/10.1080/02564602.2023.2215597 

 

Jalovecka, M., Sojka, D., Ascencio, M., & Schnittger, L. (2019). Babesia Life Cycle – When Phylogeny Meets Biology. Trends in Parasitology, 35(5), 356–368. https://doi.org/10.1016/j.pt.2019.01.007 

 

Jamdade, R., Mosa, K. A., El-Keblawy, A., Al Shaer, K., Al Harthi, E., Al Sallani, M., et al (2022). DNA Barcodes for Accurate Identification of Selected Medicinal Plants (Caryophyllales): Toward Barcoding Flowering Plants of the United Arab Emirates. Diversity, 14(4), Article 4. https://doi.org/10.3390/d14040262 

 

Janies, D. A., Studer, J., Handelman, S. K., & Linchangco, G. (2013). A comparison of supermatrix and supertree methods for multilocus phylogenetics using organismal datasets. Cladistics, 29(5), 560–566. https://doi.org/10.1111/cla.12014 

 

Jeffroy, O., Brinkmann, H., Delsuc, F., & Philippe, H. (2006). Phylogenomics: The beginning of incongruence? Trends in Genetics, 22(4), 225–231. https://doi.org/10.1016/j.tig.2006.02.003 

 

Jones, D. T., Taylor, W. R., & Thornton, J. M. (1992). The rapid generation of mutation data matrices from protein sequences. Computer Applications in the Biosciences: CABIOS, 8(3), 275–282. https://doi.org/10.1093/bioinformatics/8.3.275 

 

Jones, K. E., Purvis, A., MacLarnon, A., Bininda-Emonds, O. R. P., & Simmons, N. B. (2002). A phylogenetic supertree of the bats (Mammalia: Chiroptera). Biological Reviews of the Cambridge Philosophical Society, 77(2), 223–259. https://doi.org/10.1017/S1464793101005899 

 

Jones, L., Twyford, A. D., Ford, C. R., Rich, T. C. G., Davies, H., Forrest, L. L., et al (2021). Barcode UK: A complete DNA barcoding resource for the flowering plants and conifers of the United Kingdom. Molecular Ecology Resources, 21(6), 2050–2062. https://doi.org/10.1111/1755-0998.13388 

 

Jonge, H. J. M. de, Fehrmann, R. S. N., Bont, E. S. J. M. de, Hofstra, R. M. W., Gerbens, F., Kamps, W. A., et al (2007). Evidence Based Selection of Housekeeping Genes. PLOS ONE, 2(9), e898. https://doi.org/10.1371/journal.pone.0000898 

 

Joshi, C. J., Ke, W., Drangowska-Way, A., O’Rourke, E. J., & Lewis, N. E. (2022). What are housekeeping genes? PLOS Computational Biology, 18(7), e1010295. https://doi.org/10.1371/journal.pcbi.1010295 

 

Jukes, T. H., & Cantor, C. R. (1969). CHAPTER 24—Evolution of Protein Molecules. In H. N. Munro (Ed.), Mammalian Protein Metabolism (pp. 21–132). Academic Press. https://doi.org/10.1016/B978-1-4832-3211-9.50009-7 

 

Kapli, P., Flouri, T., & Telford, M. J. (2021). Systematic errors in phylogenetic trees. Current Biology, 31(2), R59–R64. https://doi.org/10.1016/j.cub.2020.11.043 

 

Kapli, P., Yang, Z., & Telford, M. J. (2020). Phylogenetic tree building in the genomic age. Nature Reviews Genetics, 21(7), 428–444. https://doi.org/10.1038/s41576-020-0233-0 

 

Karczewski, K. J., Francioli, L. C., Tiao, G., Cummings, B. B., Alföldi, J., Wang, Q., et al (2020). The mutational constraint spectrum quantified from variation in 141,456 humans. Nature, 581(7809), Article 7809. https://doi.org/10.1038/s41586-020-2308-7 

 

Katoh, K., Misawa, K., Kuma, K., & Miyata, T. (2002). MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. 30(14), 3059–3066. 

 

Katoh, K., & Standley, D. M. (2013). MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. Molecular Biology and Evolution, 30(4), 772–780. https://doi.org/10.1093/molbev/mst010 

 

Katsura, Y., Stanley, C. E., Kumar, S., & Nei, M. (2017). The Reliability and Stability of an Inferred Phylogenetic Tree from Empirical Data. Molecular Biology and Evolution, 34(3), 718–723. https://doi.org/10.1093/molbev/msw272 

 

Khaw, Y. S., Khong, N. M. H., Shaharuddin, N. A., & Yusoff, F. M. (2020). A simple 18S rDNA approach for the identification of cultured eukaryotic microalgae with an emphasis on primers. Journal of Microbiological Methods, 172, 105890. https://doi.org/10.1016/j.mimet.2020.105890 

 

Kimura, M. (1980). A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. Journal of Molecular Evolution, 16(2), 111–120. https://doi.org/10.1007/BF01731581 

 

Koch, N. M., & Gauthier, J. A. (2018). Noise and biases in genomic data may underlie radically different hypotheses for the position of Iguania within Squamata. PLOS ONE, 13(8), e0202729. https://doi.org/10.1371/journal.pone.0202729 

 

Kolter, A., & Gemeinholzer, B. (2021). Plant DNA barcoding necessitates marker-specific efforts to establish more comprehensive reference databases. Genome, 64(3), 265–298. https://doi.org/10.1139/gen-2019-0198 

 

Koonin, E. V. (2005). Orthologs, Paralogs, and Evolutionary Genomics. Annual Review of Genetics, 39(1), 309–338. https://doi.org/10.1146/annurev.genet.39.073003.114725 

 

Kozlov, A. M., Darriba, D., Flouri, T., Morel, B., & Stamatakis, A. (2019). RAxML-NG: A fast, scalable and user-friendly tool for maximum likelihood phylogenetic inference. Bioinformatics, 35(21), 4453–4455. https://doi.org/10.1093/bioinformatics/btz305 

 

Krienitz, L., Bock, C., Luo, W., & Pröschold, T. (2010). Polyphyletic origin of the dictyosphaerium morphotype within chlorellaceae (trebouxiophyceae). Journal of Phycology, 46(3), 559–563. https://doi.org/10.1111/j.1529-8817.2010.00813.x 

 

Krienitz, L., Hegewald, E. H., Hepperle, D., Huss, V. A. R., Rohr, T., & Wolf, M. (2004). Phylogenetic relationship of Chlorella and Parachlorella gen. Nov. (Chlorophyta, Trebouxiophyceae). Phycologia, 43(5), 529–542. https://doi.org/10.2216/i0031-8884-43-5-529.1 

 

Krivina, E. S., Temraleeva, A. D., & Bukin, Yu. S. (2021). Species Delimitation and Cryptic Diversity Analysis of Parachlorella-.lade Microalgae (Chlorophyta). Microbiology, 90(4), 455–469. https://doi.org/10.1134/S0026261721040081 

 

Kumar, S. (1996). Patterns of Nucleotide Substitution in Mitochondrial Protein Coding Genes of Vertebrates. Genetics, 143(1), 537–548. 

 

Kumar, S., Filipski, A. J., Battistuzzi, F. U., Pond, S. L. K., & Tamura, K. (2012). Statistics and truth in phylogenomics. Molecular Biology and Evolution, 29(2), 457–472. https://doi.org/10.1093/molbev/msr202 

 

Kumar, S., & Gadagkar, S. R. (2001). Disparity index: A simple statistic to measure and test the homogeneity of substitution patterns between molecular sequences. Genetics, 158(3), 1321–1327. https://doi.org/10.1093/genetics/158.3.1321 

 

Kumar, S., Stecher, G., Li, M., Knyaz, C., & Tamura, K. (2018). MEGA X: Molecular Evolutionary Genetics Analysis across Computing Platforms. Molecular Biology and Evolution, 35(6), 1547–1549. https://doi.org/10.1093/molbev/msy096 

 

Kunrunmi, O., Adesalu, T., & Kumar, S. (2017). Genetic identification of new microalgal species from Epe Lagoon of West Africa accumulating high lipids. Algal Research, 22, 68–78. https://doi.org/10.1016/j.algal.2016.12.009 

 

Lal, A., Banerjee, S., & Das, D. (2021). Aspergillus sp. Assisted bioflocculation of Chlorella MJ 11/11 for the production of biofuel from the algal-fungal co-pellet. Separation and Purification Technology, 272, 118320. https://doi.org/10.1016/j.seppur.2021.118320 

 

Lanfear, R., Calcott, B., Kainer, D., Mayer, C., & Stamatakis, A. (2014). Selecting optimal partitioning schemes for phylogenomic datasets. BMC Evolutionary Biology, 14(1), 82. https://doi.org/10.1186/1471-2148-14-82 

 

Lapierre, P., Lasek-Nesselquist, E., & Gogarten, J. P. (2014). The impact of HGT on phylogenomic reconstruction methods. Briefings in Bioinformatics, 15(1), 79–90. https://doi.org/10.1093/bib/bbs050 

 

Larsen, B. B., Miller, E. C., Rhodes, M. K., & Wiens, J. J. (2017). Inordinate Fondness Multiplied and Redistributed: The Number of Species on Earth and the New Pie of Life. The Quarterly Review of Biology, 92(3), 229–265. https://doi.org/10.1086/693564 

 

Lartillot, N., Brinkmann, H., & Philippe, H. (2007). Suppression of long-branch attraction artefacts in the animal phylogeny using a site-heterogeneous model. BMC Evolutionary Biology, 7(Suppl 1), S4. https://doi.org/10.1186/1471-2148-7-S1-S4 

 

Lassmann, T., & Sonnhammer, E. (2005). Kalign – an accurate and fast multiple sequence alignment algorithm. BMC Bioinformatics, 6(1), 298. https://doi.org/10.1186/1471-2105-6-298 

 

Le, S. Q., & Gascuel, O. (2008). An Improved General Amino Acid Replacement Matrix. Molecular Biology and Evolution, 25(7), 1307–1320. https://doi.org/10.1093/molbev/msn067 

 

Lee, J.-Y. (2023). The Principles and Applications of High-Throughput Sequencing Technologies. Development & Reproduction, 27(1), 9–24. https://doi.org/10.12717/DR.2023.27.1.9 

 

Leebens-Mack, J. H., Barker, M. S., Carpenter, E. J., Deyholos, M. K., Gitzendanner, M. A., Graham, S. W., et al (2019). One thousand plant transcriptomes and the phylogenomics of green plants. Nature, 574(7780), 679–685. https://doi.org/10.1038/s41586-019-1693-2 

 

Lemieux, C., Otis, C., & Turmel, M. (2014). Chloroplast phylogenomic analysis resolves deep-level relationships within the green algal class Trebouxiophyceae. BMC Evolutionary Biology, 14(1), 1–15. https://doi.org/10.1186/s12862-014-0211-2 

 

Lemoine, F., Domelevo Entfellner, J.-B., Wilkinson, E., Correia, D., Dávila Felipe, M., De Oliveira, T., et al (2018). Renewing Felsenstein’s phylogenetic bootstrap in the era of big data. Nature, 556(7702), 452–456. https://doi.org/10.1038/s41586-018-0043-0 

 

Liu, Q., Yang, Y., Liu, J., Song, J., Li, D., Wang, R., et al (2023). Establishment of regeneration system of Pyrus and the genetic stability analysis of regenerated population. Plant Cell, Tissue and Organ Culture (PCTOC), 152(1), 215–228. https://doi.org/10.1007/s11240-022-02378-2 

 

Lopes, I., Altab, G., Raina, P., & de Magalhães, J. P. (2021). Gene Size Matters: An Analysis of Gene Length in the Human Genome. Frontiers in Genetics, 12. https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2021.559998 

 

López-Maury, L., Marguerat, S., & Bähler, J. (2008). Tuning gene expression to changing environments: From rapid responses to evolutionary adaptation. Nature Reviews Genetics, 9(8), 583–593. https://doi.org/10.1038/nrg2398 

 

Low, S. J., Džunková, M., Chaumeil, P.-A., Parks, D. H., & Hugenholtz, P. (2019). Evaluation of a concatenated protein phylogeny for classification of tailed double-stranded DNA viruses belonging to the order Caudovirales. Nature Microbiology, 4(8), 1306–1315. https://doi.org/10.1038/s41564-019-0448-z 

 

Löytynoja, A., & Goldman, N. (2010). webPRANK: a phylogeny-aware multiple sequence aligner with interactive alignment browser webPRANK : a phylogeny-aware multiple sequence aligner with interactive alignment browser. 579(November). 

 

Luo, W., Pröschold, T., Bock, C., & Krienitz, L. (2010). Generic concept in Chlorella-related coccoid green algae (Chlorophyta, Trebouxiophyceae). Plant Biology, 12(3), 545–553. https://doi.org/10.1111/j.1438-8677.2009.00221.x 

 

Mahadani, A. K., Awasthi, S., Sanyal, G., Bhattacharjee, P., & Pippal, S. (2022). Indel-K2P: A modified Kimura 2 Parameters (K2P) model to incorporate insertion and deletion (Indel) information in phylogenetic analysis. Cyber-Physical Systems. https://www.tandfonline.com/doi/full/10.1080/23335777.2021.1879274 

 

Makarenkov, V., Barseghyan, G. S., & Tahiri, N. (2023). Inferring Multiple Consensus Trees and Supertrees Using Clustering: A Review. In B. Goldengorin & S. Kuznetsov (Eds.), Data Analysis and Optimization: In Honor of Boris Mirkin’s 80th Birthday (pp. 191–213). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-31654-8_13 

 

Malavasi, V., Škvorová, Z., Nemcová, Y., & Škaloud, P. (2022). Laetitia sardoa gen. & sp. Nov., a new member of the Chlorellales (Trebouxiophyceae, Chlorophyta) isolated from Sardinia Island. Phycologia, 61(4), 375–383. https://doi.org/10.1080/00318884.2022.2054252 

 

Mandreoli, F., & Montangero, M. (2019). Dealing with Data Heterogeneity in a Data Fusion Perspective: Models, Methodologies, and Algorithms. Data Handling in Science and Technology, 31, 235–270. https://doi.org/10.1016/B978-0-444-63984-4.00009-0 

 

Mccarthy, C. G. P., & Fitzpatrick, D. A. (2017). Phylogenomic Reconstruction of the Oomycete Phylogeny Derived from 37 Genomes. https://doi.org/10.1128/mSphere 

 

McMorris, F. R., & Wilkinson, M. (2011). Conservative Supertrees. Systematic Biology, 60(2), 232–238. https://doi.org/10.1093/sysbio/syq091 

 

Meier-Kolthoff, J. P., Klenk, H.-P., & Göker, M. (2014). Taxonomic use of DNA G+C content and DNA–DNA hybridization in the genomic age. International Journal of Systematic and Evolutionary Microbiology, 64(Pt_2), 352–356. https://doi.org/10.1099/ijs.0.056994-0 

 

Mezhzherin, S. V., Morozov-Leonov, S. Y., Zhalay, O. I., Kokodiy, S. V., Tereshchenko, V. O., Rostovskaya, .. V., et al (2023). Evolutionary transition-transversion bias by the example of the cytb gene of palearctic Muridae (Rodentia) and Vespertilionidae (Chiroptera). Reports of the National Academy of Sciences of Ukraine, 2, Article 2. https://doi.org/10.15407/dopovidi2023.02.093 

 

Milano, J., Chyuan, H., Masjuki, H. H., Chong, W. T., & Kee, M. (2016). Microalgae biofuels as an alternative to fossil fuel for power generation. Renewable and Sustainable Energy Reviews, 58, 180–197. https://doi.org/10.1016/j.rser.2015.12.150 

 

Moqtaderi, Z., Brown, S., & Bender, W. (2021). Genome-wide oscillations in G + C density and sequence conservation. Genome Research, 31(11), 2050–2057. https://doi.org/10.1101/gr.274332.120 

 

Mora, C., Tittensor, D. P., Adl, S., Simpson, A. G. B., & Worm, B. (2011). How Many Species Are There on Earth and in the Ocean? PLoS Biology, 9(8), e1001127. https://doi.org/10.1371/journal.pbio.1001127 

 

Morales-Briones, D. F., Kadereit, G., Tefarikis, D. T., Moore, M. J., Smith, S. A., Brockington, S. F., et al (2021). Disentangling Sources of Gene Tree Discordance in Phylogenomic Data Sets: Testing Ancient Hybridizations in Amaranthaceae s.l. Systematic Biology, 70(2), 219–235. https://doi.org/10.1093/sysbio/syaa066 

 

Morgan-Lang, C., McLaughlin, R., Armstrong, Z., Zhang, G., Chan, K., & Hallam, S. J. (2020). TreeSAPP: The Tree-based Sensitive and Accurate Phylogenetic Profiler. Bioinformatics, 36(18), 4706–4713. https://doi.org/10.1093/bioinformatics/btaa588 

 

Moroz, L. L., Kocot, K. M., Citarella, M. R., Dosung, S., Norekian, T. P., Povolotskaya, I. S., et al (2014). The ctenophore genome and the evolutionary origins of neural systems. Nature, 510(7503), 109–114. https://doi.org/10.1038/nature13400 

 

Nater, A., Burri, R., Kawakami, T., Smeds, L., & Ellegren, H. (2015). Resolving Evolutionary Relationships in Closely Related Species with Whole-Genome Sequencing Data. Systematic Biology, 64(6), 1000–1017. https://doi.org/10.1093/sysbio/syv045 

 

Needleman, S. B., & Wunsch, C. D. (1970). A general method applicable to the search for similarities in the amino acid sequence of two proteins. Journal of Molecular Biology, 48(3), 443–453. https://doi.org/10.1016/0022-2836(70)90057-4 

 

Nithaniyal, S., Majumder, S., Umapathy, S., & Parani, M. (2021). Forensic application of DNA barcoding in the identification of commonly occurring poisonous plants. Journal of Forensic and Legal Medicine, 78, 102126. https://doi.org/10.1016/j.jflm.2021.102126 

 

Nordin, N., Yusof, N., Maeda, T., Mustapha, N. A., Mohd-Yusoff, M. Z., & Raja-Khairuddin, R. F. (2020). Mechanism of carbon partitioning towards starch and triacylglycerol in Chlorella vulgaris under nitrogen stress through whole-transcriptome analysis. Biomass and Bioenergy, 138, 105600. https://doi.org/10.1016/j.biombioe.2020.105600 

 

O’Halloran, D. (2014). A practical guide to phylogenetics for nonexperts. Journal of Visualized Experiments, 84. https://doi.org/10.3791/50975 

 

O’Leary, N. A., Wright, M. W., Brister, J. R., Ciufo, S., Haddad, D., McVeigh, R., et al (2016). Reference sequence (RefSeq) database at NCBI: Current status, taxonomic expansion, and functional annotation. Nucleic Acids Research, 44(D1), D733–D745. https://doi.org/10.1093/nar/gkv1189 

 

Ousmael, K., Whetten, R. W., Xu, J., Nielsen, U. B., Lamour, K., & Hansen, O. K. (2023). Identification and high-throughput genotyping of single nucleotide polymorphism markers in a non-model conifer (Abies nordmanniana (Steven) Spach). Scientific Reports, 13(1), 22488. https://doi.org/10.1038/s41598-023-49462-x 

 

Oyebanji, O. O., Chukwuma, E. C., Bolarinwa, K. A., Adejobi, O. I., Adeyemi, S. B., & Ayoola, A. O. (2020). Re-evaluation of the phylogenetic relationships and species delimitation of two closely related families (Lamiaceae and Verbenaceae) using two DNA barcode markers. Journal of Biosciences, 45(1), 96. https://doi.org/10.1007/s12038-020-00061-2 

 

Pace, N. R. (2009). Mapping the Tree of Life: Progress and Prospects. Microbiology and Molecular Biology Reviews, 73(4), 565–576. https://doi.org/10.1128/mmbr.00033-09 

 

Pamilo, P., & Nei, M. (1988). Relationships between Gene Trees and Species Trees. 5(5), 568–583. https://doi.org/10.1093/oxfordjournals.molbev.a040517 

 

Patwardhan, A., Ray, S., & Roy, A. (2014). Molecular Markers in Phylogenetic Studies-A Review. Journal of Phylogenetics & Evolutionary Biology, 02(02). https://doi.org/10.4172/2329-9002.1000131 

 

Pinto-Ledezma, J. N., Díaz, S., Halpern, B. S., Khoury, C., & Cavender-Bares, J. (2023). No branch left behind: Tracking terrestrial biodiversity from a phylogenetic completeness perspective Wiley Online Library. Frontiers in Ecology and the Environment. https://doi.org/10.1002/fee.2696 

 

Pollock, D. D., & Goldstein, D. B. (1995). A comparison of two methods for constructing evolutionary distances from a weighted contribution of transition and transversion differences. Molecular Biology and Evolution, 12(4), 713–717. Scopus. 

 

Prüfer, K., Munch, K., Hellmann, I., Akagi, K., Miller, J. R., Walenz, B., et al (2012). The bonobo genome compared with the chimpanzee and human genomes. Nature, 486(7404), 527–531. https://doi.org/10.1038/nature11128 

 

Queiroz, A. De, & Gatesy, J. (2006). The supermatrix approach to systematics. 22(1). https://doi.org/10.1016/j.tree.2006.10.002 

 

Rajendran, V., Kalita, P., Shukla, H., Kumar, A., & Tripathi, T. (2018). Aminoacyl-tRNA synthetases: Structure, function, and drug discovery. International Journal of Biological Macromolecules, 111, 400–414. https://doi.org/10.1016/j.ijbiomac.2017.12.157 

 

Redmond, A. K., & McLysaght, A. (2021). Evidence for sponges as sister to all other animals from partitioned phylogenomics with mixture models and recoding. Nature Communications, 12(1), 1783. https://doi.org/10.1038/s41467-021-22074-7 

 

Ren, F., Tanaka, H., & Yang, Z. (2009). A likelihood look at the supermatrix-supertree controversy. Gene, 441(1–2), 119–125. https://doi.org/10.1016/j.gene.2008.04.002 

 

Richter, F., Haegeman, B., Etienne, R. S., & Wit, E. C. (2020). Introducing a general class of species diversification models for phylogenetic trees. Statistica Neerlandica, 74(3), 261–274. https://doi.org/10.1111/stan.12205 

 

Ridgeway, T., & Nw, L. (2000). T-Coffee: A Novel Method for Fast and Accurate Multiple Sequence Alignment. https://doi.org/10.1006/jmbi.2000.4042 

 

Robinson, D. F., & Foulds, L. R. (1981). Comparison of phylogenetic trees. Mathematical Biosciences, 53(1), 131–147. https://doi.org/10.1016/0025-5564(81)90043-2 

 

Saif, R., Nadeem, S., Khaliq, A., Zia, S., & Iftekhar, A. (2022). Mathematical Understanding of Sequence Alignment and Phylogenetic Algorithms: A Comprehensive Review of Computation of Different Methods. Advancements in Life Sciences, 9(4), 401–411. 

 

Saitou, N., & Nei, M. (1987). The neighbor-joining method: A new method for reconstructing phylogenetic trees. Molecular Biology and Evolution, 4(4), 406–425. https://doi.org/10.1093/oxfordjournals.molbev.a040454 

 

Salichos, L., & Rokas, A. (2013). Inferring ancient divergences requires genes with strong phylogenetic signals. Nature, 497(7449), 327–331. https://doi.org/10.1038/nature12130 

 

Schisler, D. A., Janisiewicz, W. J., Boekhout, T., & Kurtzman, C. P. (2011). Chapter 4 - Agriculturally Important Yeasts: Biological Control of Field and Postharvest Diseases Using Yeast Antagonists, and Yeasts as Pathogens of Plants. In C. P. Kurtzman, J. W. Fell, & T. Boekhout (Eds.), The Yeasts (Fifth Edition) (pp. 45–52). Elsevier. https://doi.org/10.1016/B978-0-444-52149-1.00004-5 

 

Schwarz, G. (1978). Estimating the Dimension of a Model. The Annals of Statistics, 6(2), 461–464. https://doi.org/10.1214/aos/1176344136 

 

Shahzad, K., Liu, M.-L., Zhao, Y.-H., Zhang, T.-T., Liu, J.-N., & Li, Z.-H. (2020). Evolutionary history of endangered and relict tree species Dipteronia sinensis in response to geological and climatic events in the Qinling Mountains and adjacent areas. Ecology and Evolution, 10(24), 14052–14066. https://doi.org/10.1002/ece3.6996 

 

Shen, X. X., Hittinger, C. T., & Rokas, A. (2017). Contentious relationships in phylogenomic studies can be driven by a handful of genes. Nature Ecology and Evolution, 1, 0126. https://doi.org/10.1038/s41559-017-0126 

 

Shimodaira, H. (2002). An Approximately Unbiased Test of Phylogenetic Tree Selection. Systematic Biology, 51(3), 492–508. https://doi.org/10.1080/10635150290069913 

 

Shimodaira, H., & Hasegawa, M. (1999). Multiple comparisons of log-likelihoods with applications to phylogenetic inference. Molecular Biology and Evolution, 16(8), 1114–1116. https://doi.org/10.1093/oxfordjournals.molbev.a026201 

 

Shimodaira, H., & Hasegawa, M. (2001). CONSEL: For assessing the confidence of phylogenetic tree selection. Bioinformatics, 17(12), 1246–1247. https://doi.org/10.1093/bioinformatics/17.12.1246 

 

Sievers, F., Wilm, A., Dineen, D., Gibson, T. J., Karplus, K., Li, W., et al (2011). Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. 539. https://doi.org/10.1038/msb.2011.75 

 

Simon, C. (2022). An Evolving View of Phylogenetic Support. Systematic Biology, 71(4), 921–928. https://doi.org/10.1093/sysbio/syaa068 

 

Singh, P., Kumari, S., Guldhe, A., Misra, R., Rawat, I., & Bux, F. (2016). Trends and novel strategies for enhancing lipid accumulation and quality in microalgae. Renewable and Sustainable Energy Reviews, 55(November), 1–16. https://doi.org/10.1016/j.rser.2015.11.001 

 

Smith, B. T., Mauck, W. M. III, Benz, B. W., & Andersen, M. J. (2020). Uneven Missing Data Skew Phylogenomic Relationships within the Lories and Lorikeets. Genome Biology and Evolution, 12(7), 1131–1147. https://doi.org/10.1093/gbe/evaa113 

 

Smith, M. R. (2020). Information theoretic generalized Robinson–Foulds metrics for comparing phylogenetic trees. Bioinformatics, 36(20), 5007–5013. https://doi.org/10.5281/zenodo.3528123. 

 

Smith, S. A., Walker-Hale, N., Walker, J. F., & Brown, J. W. (2020). Phylogenetic Conflicts, Combinability, and Deep Phylogenomics in Plants. Systematic Biology, 69(3), 579–592. https://doi.org/10.1093/sysbio/syz078 

 

Smith, T. F., & Waterman, M. S. (1981). Identification of common molecular subsequences. Journal of Molecular Biology, 147(1), 195–197. https://doi.org/10.1016/0022-2836(81)90087-5 

 

Som, A. (2014). Causes, consequences and solutions of phylogenetic incongruence. Briefings in Bioinformatics, 16(3), 536–548. https://doi.org/10.1093/bib/bbu015 

 

Stamatakis, A. (2006). RAxML-VI-HPC: Maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models. Bioinformatics, 22(21), 2688–2690. https://doi.org/10.1093/bioinformatics/btl446 

 

Stamatakis, A. (2014). RAxML version 8: A tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics, 30(9), 1312–1313. https://doi.org/10.1093/bioinformatics/btu033 

 

Stamatakis, A., Ludwig, T., & Meier, H. (2005a). RAxML-II: A program for sequential, parallel and distributed inference of large phylogenetic trees. Concurrency and Computation: Practice and Experience, 17(14), 1705–1723. https://doi.org/10.1002/cpe.954 

 

Stamatakis, A., Ludwig, T., & Meier, H. (2005b). RAxML-III: A fast program for maximum likelihood-based inference of large phylogenetic trees. Bioinformatics, 21(4), 456–463. https://doi.org/10.1093/bioinformatics/bti191 

 

Steenwyk, J. L., Buida T. J. III, Li, Y., Shen, X.-X., & Rokas, A. (2020). ClipKIT: A multiple sequence alignment trimming software for accurate phylogenomic inference. PLOS Biology, 18(12), e3001007. https://doi.org/10.1371/journal.pbio.3001007 

 

Steppan, S. J., & Schenk, J. J. (2017). Muroid rodent phylogenetics: 900-Species tree reveals increasing diversification rates. In PLoS ONE (Vol. 12, Issue 8). https://doi.org/10.1371/journal.pone.0183070 

 

Susko, E., & Roger, A. J. (2021). Long Branch Attraction Biases in Phylogenetics. Systematic Biology, 70(4), 838–843. https://doi.org/10.1093/sysbio/syab001 

 

Tatusov, R. L., Natale, D. A., Garkavtsev, I. V., Tatusova, T. A., Shankavaram, U. T., Rao, B. S., et al (2001). The COG database: New developments in phylogenetic classification of proteins from complete genomes. Nucleic Acids Research, 29(1), 22–28. https://doi.org/10.1093/nar/29.1.22 

 

Tear, C. J. Y., Lim, C., Wu, J., & Zhao, H. (2013). Accumulated lipids rather than the rigid cell walls impede the extraction of genetic materials for effective colony PCRs in Chlorella vulgaris. Microbial Cell Factories, 12, 106. https://doi.org/10.1186/1475-2859-12-106 

 

Thiergart, T., Landan, G., & Martin, W. F. (2014). Concatenated alignments and the case of the disappearing tree. BMC Evolutionary Biology, 14(1), 266. https://doi.org/10.1186/s12862-014-0266-0 

 

Threlfall, J., & Blaxter, M. (2021). Launching the Tree of Life Gateway. Wellcome Open Research, 6, 125. https://doi.org/10.12688/wellcomeopenres.16913.1 

 

Tran, Q. N., Arabnia, H., Porter, J., Berkhahn, J., & Zhang, L. (2015). A Comparative Analysis of Read Mapping and Indel Calling Pipelines for Next-Generation Sequencing Data. Emerging Trends in Computational Biology, Bioinformatics, and Systems Biology, 521–535. https://doi.org/10.1016/B978-0-12-802508-6.00029-6 

 

Tu, Z., Wang, L., Xu, M., Zhou, X., Chen, T., & Sun, F. (2006). Further understanding human disease genes by comparing with housekeeping genes and other genes. BMC Genomics, 7(1), 31. https://doi.org/10.1186/1471-2164-7-31 

 

von Haeseler, A. (2012). Do we still need supertrees? BMC Biology, 10(1), 13. https://doi.org/10.1186/1741-7007-10-13 

 

von Mering, C., Huynen, M., Jaeggi, D., Schmidt, S., Bork, P., & Snel, B. (2003). STRING: A database of predicted functional associations between proteins. Nucleic Acids Research, 31(1), 258–261. https://doi.org/10.1093/nar/gkg034 

 

Wang, C., Zou, S., Fei, C., Wang, C., Gao, Z., Bao, Y., et al (2016). How DNA barcoding can be more effective in microalgae identification: A case of cryptic diversity revelation in Scenedesmus (Chlorophyceae). Scientific Reports, 6, 36822. https://doi.org/10.1038/srep36822 

 

Wang, H.-X., Morales-Briones, D. F., Moore, M. J., Wen, J., & Wang, H.-F. (2021). A phylogenomic perspective on gene tree conflict and character evolution in 

Caprifoliaceae using target enrichment data, with Zabelioideae recognized as a new subfamily. Journal of Systematics and Evolution, 59(5), 897–914. https://doi.org/10.1111/jse.12745 

 

Whelan, S., & Goldman, N. (2001). A General Empirical Model of Protein Evolution Derived from Multiple Protein Families Using a Maximum-Likelihood Approach. Molecular Biology and Evolution, 18(5), 691–699. https://doi.org/10.1093/oxfordjournals.molbev.a003851 

 

White, O. W., Biswas, M. K., Abebe, W. M., Dussert, Y., Kebede, F., Nichols, R. A., et al (2023). Maintenance and expansion of genetic and trait variation following domestication in a clonal crop. Molecular Ecology, 32(15), 4165–4180. https://doi.org/10.1111/mec.17033 

 

Williams, T. A., Davin, A. A., Morel, B., Szánthó, L. L., Spang, A., Stamatakis, A., et al (2023). The power and limitations of species tree-aware phylogenetics (p. 2023.03.17.533068). bioRxiv. https://doi.org/10.1101/2023.03.17.533068 

 

Williams, T. A., & Heaps, S. E. (2014). An Introduction to Phylogenetics and the Tree of Life. Methods in Microbiology, 41, 13–44. https://doi.org/10.1016/BS.MIM.2014.05.001 

 

Wu, F.-F., Gao, Q., Liu, F., Wang, Z., Wang, J.-L., & Wang, X.-G. (2020). DNA barcoding evaluation of Vicia (Fabaceae): Comparative efficacy of six universal barcode loci on abundant species. Journal of Systematics and Evolution, 58(1), 77–88. https://doi.org/10.1111/jse.12474 

 

Xue, J., Dong, S., Wang, M., Song, T., Zhou, G., Li, Z., et al (2022). Mitochondrial genes from 18 angiosperms fill sampling gaps for phylogenomic inferences of the early diversification of flowering plants. Journal of Systematics and Evolution, 60(4), 773–788. https://doi.org/10.1111/jse.12708 

 

Yang, C.-H., Wu, K.-C., Chuang, L.-Y., & Chang, H.-W. (2022). DeepBarcoding: Deep Learning for Species Classification Using DNA Barcoding. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 19(4), 2158–2165. https://doi.org/10.1109/TCBB.2021.3056570 

 

Yang, L., Abduraimov, O., Tojibaev, K., Shomurodov, K., Zhang, Y.-M., & Li, W.-J. (2022). Analysis of complete chloroplast genome sequences and insight into the phylogenetic relationships of Ferula L. BMC Genomics, 23(1), 643. https://doi.org/10.1186/s12864-022-08868-z 

 

Yang, Z. (1993). Maximum-likelihood estimation of phylogeny from DNA sequences when substitution rates differ over sites. Molecular Biology and Evolution, 10(6), 1396–1401. https://doi.org/10.1093/oxfordjournals.molbev.a040082 

 

Yang, Z. (1994). Maximum likelihood phylogenetic estimation from DNA sequences with variable rates over sites: Approximate methods. Journal of Molecular Evolution, 39(3), 306–314. https://doi.org/10.1007/BF00160154 

 

Yang, Z. (1996). Maximum-likelihood models for combined analyses of multiple sequence data. Journal of Molecular Evolution, 42(5), 587–596. https://doi.org/10.1007/BF02352289 

 

Yang, Z. (2010). Computational Molecular Evolution. In Computational Molecular Evolution. Oxford University Press. https://doi.org/10.1093/acprof:oso/9780198567028.001.0001 

 

Yang, Z., & Rannala, B. (1997). Bayesian phylogenetic inference using DNA sequences: A Markov Chain Monte Carlo Method. Molecular Biology and Evolution, 14(7), 717–724. https://doi.org/10.1093/oxfordjournals.molbev.a025811 

 

Yang, Z., & Rannala, B. (2012). Molecular phylogenetics: Principles and practice. 13(May). https://doi.org/10.1038/nrg3186 

 

Yuan, Q., Li, H., Wei, Z., Lv, K., Gao, C., Liu, Y., et al (2020). Isolation, structures and biological activities of polysaccharides from Chlorella: A review. International Journal of Biological Macromolecules, 163, 2199–2209. https://doi.org/10.1016/j.ijbiomac.2020.09.080 

 

Zaharias, P., Lemoine, F., & Gascuel, O. (2023). Robustness of Felsenstein’s Versus Transfer Bootstrap Supports with Respect to Taxon Sampling. Systematic Biology, syad052. https://doi.org/10.1093/sysbio/syad052 

 

Zhang, H., Lundberg, M., Tarka, M., Hasselquist, D., & Hansson, B. (2023). Evidence of Site-Specific and Male-Biased Germline Mutation Rate in a Wild Songbird. Genome Biology and Evolution, 15(11), evad180. https://doi.org/10.1093/gbe/evad180 

 

Zhang, L., & Li, W.-H. (2004). Mammalian Housekeeping Genes Evolve More Slowly than Tissue-Specific Genes. Molecular Biology and Evolution, 21(2), 236–239. https://doi.org/10.1093/molbev/msh010 

 

Zhou, X., Shen, X.-X., Hittinger, C. T., & Rokas, A. (2018). Evaluating Fast Maximum Likelihood-Based Phylogenetic Programs Using Empirical Phylogenomic Data Sets. Molecular Biology and Evolution, 35(2), 486–503. https://doi.org/10.1093/molbev/msx302 

 

Zhu, J., He, F., Hu, S., & Yu, J. (2008). On the nature of human housekeeping genes. Trends in Genetics, 24(10), 481–484. 

 

 


This material may be protected under Copyright Act which governs the making of photocopies or reproductions of copyrighted materials.
You may use the digitized material for private study, scholarship, or research.

Back to previous page

Installed and configured by Bahagian Automasi, Perpustakaan Tuanku Bainun, Universiti Pendidikan Sultan Idris
If you have enquiries, kindly contact us at pustakasys@upsi.edu.my or 016-3630263. Office hours only.