The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Whole-proteome phylogeny of prokaryotes by feature frequency profiles: An alignment-free method with optimal feature resolution
Open accessAbstract
We present a whole-proteome phylogeny of prokaryotes constructed by comparing feature frequency profiles (FFPs) of whole proteomes. Features are l-mers of amino acids, and each organism is represented by a profile of frequencies of all features. The selection of feature length is critical in the FFP method, and we have developed a procedure for identifying the optimal feature lengths for inferring the phylogeny of prokaryotes, strictly speaking, a proteome phylogeny. Our FFP trees are constructed with whole proteomes of 884 prokaryotes, 16 unicellular eukaryotes, and 2 random sequences. To highlight the branching order of major groups, we present a simplified proteome FFP tree of monophyletic class or phylum with branch support. In our whole-proteome FFP trees (i) Archaea, Bacteria, Eukaryota, and a random sequence outgroup are clearly separated; (ii) Archaea and Bacteria form a sister group when rooted with random sequences; (iii) Planctomycetes, which possesses an intracellular membrane compartment, is placed at the basal position of the Bacteria domain; (iv) almost all groups are monophyletic in prokaryotes at most taxonomic levels, but many differences in the branching order of major groups are observed between our proteome FFP tree and trees built with other methods; and (v) previously “unclassified” genomes may be assigned to the most likely taxa. We describe notable similarities and differences between our FFP trees and those based on other methods in grouping and phylogeny of prokaryotes.
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Information theory and statistical mechanics
Jaynes, E. T. (1957) · Physical Review · doi:10.1103/physrev.106.620 · Cited by 12,931
Versatile and open software for comparing large genomes
Kurtz, S., Phillippy, A., Delcher, A. L., Smoot, M., Shumway, M., Antonescu, C., & Salzberg, S. L. (2004) · Genome Biology · doi:10.1186/gb-2004-5-2-r12 · Cited by 5,254
Toward Automatic Reconstruction of a Highly Resolved Tree of Life
Ciccarelli, F. D., Doerks, T., von Mering, C., Creevey, C. J., Snel, B., & Bork, P. (2006) · Science · doi:10.1126/science.1123061 · Cited by 1,663
Iwabe, N., Kuma, K., Hasegawa, M., Osawa, S., & Miyata, T. (1989) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.86.23.9355 · Cited by 902
Evolutionary Implications of Microbial Genome Tetranucleotide Frequency Biases
Pride, D. T., Meinersmann, R. J., Wassenaar, T. M., & Blaser, M. J. (2003) · Genome Research · doi:10.1101/gr.335003 · Cited by 289
Cited by
Whole Genome Phylogeny of Bacillus by Feature Frequency Profiles (FFP)
Wang, A., & Ash, G. J. (2015) · Scientific Reports · doi:10.1038/srep13644 · Cited by 517
On the maverick Planctomycetes
Wiegand, S., Jogler, M., & Jogler, C. (2018) · FEMS Microbiology Reviews · doi:10.1093/femsre/fuy029 · Cited by 319
Tan, G., Muffato, M., Ledergerber, C., Herrero, J., Goldman, N., Gil, M., & Dessimoz, C. (2015) · Systematic Biology · doi:10.1093/sysbio/syv033 · Cited by 261
Volke, D. C., Olavarría, K., & Nikel, P. I. (2021) · mSystems · doi:10.1128/msystems.00014-21 · Cited by 42
Metadata
- Authors
- Jun, S.-R., Sims, G. E., Wu, G. A., & Kim, S.-H.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2009-12-29
- Volume
- 107
- Issue
- 1
- Pages
- 133-138
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2068340306
- Cited by
- 160
- References
- 5
- FWCI
- 2.04
- Type
- journal article
- Language
- en
- Open access
- green
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- Jun, S.-R., Sims, G. E., Wu, G. A., & Kim, S.-H. (2010). Whole-proteome phylogeny of prokaryotes by feature frequency profiles: An alignment-free method with optimal feature resolution. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.0913033107
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