universal distribution of evolutionary rates of genes and distinct characteristics of eukaryotic genes of different apparent ages
The evolutionary rates of protein-coding genes in an organism span, approximately, 3 orders of magnitude and show a universal, approximately log-normal distribution in a broad variety of species from prokaryotes to mammals. This universal distribution implies a steady-state process, with identical d...
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| Published in | Proceedings of the National Academy of Sciences - PNAS Vol. 106; no. 18; pp. 7273 - 7280 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
National Academy of Sciences
05.05.2009
National Acad Sciences |
| Series | Inaugural Article |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0027-8424 1091-6490 1091-6490 |
| DOI | 10.1073/pnas.0901808106 |
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| Abstract | The evolutionary rates of protein-coding genes in an organism span, approximately, 3 orders of magnitude and show a universal, approximately log-normal distribution in a broad variety of species from prokaryotes to mammals. This universal distribution implies a steady-state process, with identical distributions of evolutionary rates among genes that are gained and genes that are lost. A mathematical model of such process is developed under the single assumption of the constancy of the distributions of the propensities for gene loss (PGL). This model predicts that genes of different ages, that is, genes with homologs detectable at different phylogenetic depths, substantially differ in those variables that correlate with PGL. We computationally partition protein-coding genes from humans, flies, and Aspergillus fungus into age classes, and show that genes of different ages retain the universal log-normal distribution of evolutionary rates, with a shift toward higher rates in "younger" classes but also with a substantial overlap. The only exception involves human primate-specific genes that show a heavy tail of rapidly evolving genes, probably owing to gene annotation artifacts. As predicted, the gene age classes differ in characteristics correlated with PGL. Compared with "young" genes (e.g., mammal-specific human ones), "old" genes (e.g., eukaryote-specific), on average, are longer, are expressed at a higher level, possess a higher intron density, evolve slower on the short time scale, and are subject to stronger purifying selection. Thus, genome evolution fits a simple model with approximately uniform rates of gene gain and loss, without major bursts of genomic innovation. |
|---|---|
| AbstractList | The evolutionary rates of protein-coding genes in an organism span, approximately, 3 orders of magnitude and show a universal, approximately log-normal distribution in a broad variety of species from prokaryotes to mammals. This universal distribution implies a steady-state process, with identical distributions of evolutionary rates among genes that are gained and genes that are lost. A mathematical model of such process is developed under the single assumption of the constancy of the distributions of the propensities for gene loss (PGL). This model predicts that genes of different ages, that is, genes with homologs detectable at different phylogenetic depths, substantially differ in those variables that correlate with PGL. We computationally partition protein-coding genes from humans, flies, and
Aspergillus
fungus into age classes, and show that genes of different ages retain the universal log-normal distribution of evolutionary rates, with a shift toward higher rates in “younger” classes but also with a substantial overlap. The only exception involves human primate-specific genes that show a heavy tail of rapidly evolving genes, probably owing to gene annotation artifacts. As predicted, the gene age classes differ in characteristics correlated with PGL. Compared with “young” genes (e.g., mammal-specific human ones), “old” genes (e.g., eukaryote-specific), on average, are longer, are expressed at a higher level, possess a higher intron density, evolve slower on the short time scale, and are subject to stronger purifying selection. Thus, genome evolution fits a simple model with approximately uniform rates of gene gain and loss, without major bursts of genomic innovation. The evolutionary rates of protein-coding genes in an organism span, approximately, 3 orders of magnitude and show a universal, approximately log-normal distribution in a broad variety of species from prokaryotes to mammals. This universal distribution implies a steady-state process, with identical distributions of evolutionary rates among genes that are gained and genes that are lost. A mathematical model of such process is developed under the single assumption of the constancy of the distributions of the propensities for gene loss (PGL). This model predicts that genes of different ages, that is, genes with homologs detectable at different phylogenetic depths, substantially differ in those variables that correlate with PGL. We computationally partition protein-coding genes from humans, flies, and Aspergillus fungus into age classes, and show that genes of different ages retain the universal log-normal distribution of evolutionary rates, with a shift toward higher rates in 'younger' classes but also with a substantial overlap. The only exception involves human primate-specific genes that show a heavy tail of rapidly evolving genes, probably owing to gene annotation artifacts. As predicted, the gene age classes differ in characteristics correlated with PGL. Compared with 'young' genes (e.g., mammal-specific human ones), 'old' genes (e.g., eukaryote-specific), on average, are longer, are expressed at a higher level, possess a higher intron density, evolve slower on the short time scale, and are subject to stronger purifying selection. Thus, genome evolution fits a simple model with approximately uniform rates of gene gain and loss, without major bursts of genomic innovation. The evolutionary rates of protein-coding genes in an organism span, approximately, 3 orders of magnitude and show a universal, approximately log-normal distribution in a broad variety of species from prokaryotes to mammals. This universal distribution implies a steady-state process, with identical distributions of evolutionary rates among genes that are gained and genes that are lost. A mathematical model of such process is developed under the single assumption of the constancy of the distributions of the propensities for gene loss (PGL). This model predicts that genes of different ages, that is, genes with homologs detectable at different phylogenetic depths, substantially differ in those variables that correlate with PGL. We computationally partition protein-coding genes from humans, flies, and Aspergillus fungus into age classes, and show that genes of different ages retain the universal log-normal distribution of evolutionary rates, with a shift toward higher rates in "younger" classes but also with a substantial overlap. The only exception involves human primate-specific genes that show a heavy tail of rapidly evolving genes, probably owing to gene annotation artifacts. As predicted, the gene age classes differ in characteristics correlated with PGL. Compared with "young" genes (e.g., mammal-specific human ones), "old" genes (e.g., eukaryote-specific), on average, are longer, are expressed at a higher level, possess a higher intron density, evolve slower on the short time scale, and are subject to stronger purifying selection. Thus, genome evolution fits a simple model with approximately uniform rates of gene gain and loss, without major bursts of genomic innovation.The evolutionary rates of protein-coding genes in an organism span, approximately, 3 orders of magnitude and show a universal, approximately log-normal distribution in a broad variety of species from prokaryotes to mammals. This universal distribution implies a steady-state process, with identical distributions of evolutionary rates among genes that are gained and genes that are lost. A mathematical model of such process is developed under the single assumption of the constancy of the distributions of the propensities for gene loss (PGL). This model predicts that genes of different ages, that is, genes with homologs detectable at different phylogenetic depths, substantially differ in those variables that correlate with PGL. We computationally partition protein-coding genes from humans, flies, and Aspergillus fungus into age classes, and show that genes of different ages retain the universal log-normal distribution of evolutionary rates, with a shift toward higher rates in "younger" classes but also with a substantial overlap. The only exception involves human primate-specific genes that show a heavy tail of rapidly evolving genes, probably owing to gene annotation artifacts. As predicted, the gene age classes differ in characteristics correlated with PGL. Compared with "young" genes (e.g., mammal-specific human ones), "old" genes (e.g., eukaryote-specific), on average, are longer, are expressed at a higher level, possess a higher intron density, evolve slower on the short time scale, and are subject to stronger purifying selection. Thus, genome evolution fits a simple model with approximately uniform rates of gene gain and loss, without major bursts of genomic innovation. The evolutionary rates of protein-coding genes in an organism span, approximately, 3 orders of magnitude and show a universal, approximately log-normal distribution in a broad variety of species from prokaryotes to mammals. This universal distribution implies a steady-state process, with identical distributions of evolutionary rates among genes that are gained and genes that are lost. A mathematical model of such process is developed under the single assumption of the constancy of the distributions of the propensities for gene loss (PGL). This model predicts that genes of different ages, that is, genes with homologs detectable at different phylogenetic depths, substantially differ in those variables that correlate with PGL. We computationally partition protein-coding genes from humans, flies, and Aspergillus fungus into age classes, and show that genes of different ages retain the universal log-normal distribution of evolutionary rates, with a shift toward higher rates in "younger" classes but also with a substantial overlap. The only exception involves human primate-specific genes that show a heavy tail of rapidly evolving genes, probably owing to gene annotation artifacts. As predicted, the gene age classes differ in characteristics correlated with PGL. Compared with "young" genes (e.g., mammal-specific human ones), "old" genes (e.g., eukaryotespecific), on average, are longer, are expressed at a higher level, possess a higher intron density, evolve slower on the short time scale, and are subject to stronger purifying selection. Thus, genome evolution fits a simple model with approximately uniform rates of gene gain and loss, without major bursts of genomic innovation. The evolutionary rates of protein-coding genes in an organism span, approximately, 3 orders of magnitude and show a universal, approximately log-normal distribution in a broad variety of species from prokaryotes to mammals. This universal distribution implies a steady-state process, with identical distributions of evolutionary rates among genes that are gained and genes that are lost. A mathematical model of such process is developed under the single assumption of the constancy of the distributions of the propensities for gene loss (PGL). This model predicts that genes of different ages, that is, genes with homologs detectable at different phylogenetic depths, substantially differ in those variables that correlate with PGL. We computationally partition protein-coding genes from humans, flies, and Aspergillus fungus into age classes, and show that genes of different ages retain the universal log-normal distribution of evolutionary rates, with a shift toward higher rates in "younger" classes but also with a substantial overlap. The only exception involves human primate-specific genes that show a heavy tail of rapidly evolving genes, probably owing to gene annotation artifacts. As predicted, the gene age classes differ in characteristics correlated with PGL. Compared with "young" genes (e.g., mammal-specific human ones), "old" genes (e.g., eukaryote-specific), on average, are longer, are expressed at a higher level, possess a higher intron density, evolve slower on the short time scale, and are subject to stronger purifying selection. Thus, genome evolution fits a simple model with approximately uniform rates of gene gain and loss, without major bursts of genomic innovation. [PUBLICATION ABSTRACT] |
| Author | Koonin, Eugene V Novichkov, Pavel S Wolf, Yuri I Lipman, David J Karev, Georgy P |
| Author_xml | – sequence: 1 fullname: Wolf, Yuri I – sequence: 2 fullname: Novichkov, Pavel S – sequence: 3 fullname: Karev, Georgy P – sequence: 4 fullname: Koonin, Eugene V – sequence: 5 fullname: Lipman, David J |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/19351897$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1093/molbev/msj038 10.1073/pnas.0501761102 10.1016/j.cell.2008.05.042 10.1098/rspb.2006.3472 10.1016/S0962-8924(99)01664-5 10.1093/nar/gkl792 10.1038/nrg1838 10.1093/nar/gkg033 10.1038/nature02848 10.1093/genetics/158.2.927 10.1016/S1367-5931(01)00278-2 10.1038/nature04546 10.1093/molbev/msi122 10.1016/S0968-0004(03)00052-5 10.1093/nar/gkm883 10.1016/j.jhevol.2004.11.007 10.1101/gr.1589103 10.1093/nar/gkn668 10.1093/nar/gkh340 10.1371/journal.pbio.0040352 10.1214/aoms/1177704472 10.1016/S0076-6879(96)66026-1 10.1093/bioinformatics/btm015 10.1016/j.tig.2006.04.009 10.1073/pnas.0709013104 10.1101/gr.87702 10.1016/S0168-9525(02)02722-1 10.1073/pnas.0504070102 10.1101/gr.3194805 10.1007/0-387-36747-0_12 10.1093/molbev/msh160 10.1093/nar/29.14.2994 10.1101/gr.174302 10.1017/CBO9780511623486 10.1186/1471-2148-2-18 10.1016/S0960-9822(99)80334-0 10.1146/annurev.bi.46.070177.003041 10.1038/35012500 10.1093/nar/gkl928 10.1038/nature06107 10.1016/S0959-437X(00)00252-5 10.1007/978-3-642-86659-3 10.1126/science.278.5338.631 10.1016/j.tig.2004.09.001 10.1101/gr.5978207 10.1186/gb-2003-4-4-r28 10.1016/j.tig.2007.10.002 10.1038/nrg1204 10.1016/B978-1-4832-3211-9.50009-7 10.1093/molbev/msm088 10.1016/j.copbio.2006.08.003 10.1186/gb-2004-5-2-r7 10.1038/35082561 10.1093/molbev/msj006 10.1093/nar/25.17.3389 10.1101/gr.10.7.991 10.1016/j.tibtech.2005.07.009 10.1038/nrg1271 10.1186/1471-2148-2-20 10.1016/j.gene.2008.02.014 10.1093/molbev/msi045 |
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| Copyright | Copyright 1993-2008 National Academy of Sciences of the United States of America Copyright National Academy of Sciences May 5, 2009 |
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| Issue | 18 |
| Language | English |
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| Notes | SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-2 content type line 23 ObjectType-Article-1 ObjectType-Feature-2 Author contributions: Y.I.W., E.V.K., and D.J.L. designed research; Y.I.W., P.S.N., and E.V.K. performed research; Y.I.W., G.P.K., E.V.K., and D.J.L. analyzed data; and Y.I.W., E.V.K., and D.J.L. wrote the paper. This contribution is part of the special series of Inaugural Articles by members of the National Academy of Sciences elected in 2003. Contributed by David J. Lipman, February 20, 2009 |
| OpenAccessLink | http://doi.org/10.1073/pnas.0901808106 |
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| References | e_1_3_3_50_2 e_1_3_3_16_2 e_1_3_3_18_2 e_1_3_3_39_2 e_1_3_3_12_2 e_1_3_3_37_2 e_1_3_3_58_2 e_1_3_3_14_2 e_1_3_3_35_2 e_1_3_3_56_2 e_1_3_3_33_2 e_1_3_3_54_2 e_1_3_3_10_2 e_1_3_3_31_2 e_1_3_3_52_2 e_1_3_3_40_2 Koonin EV (e_1_3_3_17_2) 2008 e_1_3_3_5_2 e_1_3_3_7_2 e_1_3_3_9_2 e_1_3_3_27_2 e_1_3_3_29_2 e_1_3_3_23_2 e_1_3_3_48_2 e_1_3_3_25_2 e_1_3_3_46_2 e_1_3_3_1_2 e_1_3_3_44_2 e_1_3_3_3_2 e_1_3_3_21_2 e_1_3_3_42_2 e_1_3_3_63_2 e_1_3_3_51_2 Jones DT (e_1_3_3_61_2) 1992; 8 Li WH (e_1_3_3_41_2) 1997 e_1_3_3_19_2 e_1_3_3_38_2 e_1_3_3_13_2 e_1_3_3_36_2 e_1_3_3_59_2 e_1_3_3_15_2 e_1_3_3_34_2 e_1_3_3_57_2 e_1_3_3_32_2 e_1_3_3_55_2 e_1_3_3_11_2 e_1_3_3_30_2 e_1_3_3_53_2 e_1_3_3_62_2 e_1_3_3_60_2 e_1_3_3_6_2 e_1_3_3_8_2 e_1_3_3_28_2 e_1_3_3_49_2 e_1_3_3_24_2 e_1_3_3_47_2 e_1_3_3_26_2 e_1_3_3_45_2 e_1_3_3_2_2 e_1_3_3_20_2 e_1_3_3_43_2 e_1_3_3_4_2 e_1_3_3_22_2 e_1_3_3_64_2 10899148 - Genome Res. 2000 Jul;10(7):991-1000 18054114 - Trends Genet. 2008 Jan;24(1):1-4 11452024 - Nucleic Acids Res. 2001 Jul 15;29(14):2994-3005 16572163 - Nature. 2006 Mar 30;440(7084):623-30 17105342 - PLoS Biol. 2006 Nov;4(11):e352 18378100 - Gene. 2008 May 15;414(1-2):85-94 14759257 - Genome Biol. 2004;5(2):R7 18662548 - Cell. 2008 Jul 25;134(2):341-52 17142222 - Nucleic Acids Res. 2007 Jan;35(Database issue):D668-73 15155797 - Mol Biol Evol. 2004 Sep;21(9):1643-60 9254694 - Nucleic Acids Res. 1997 Sep 1;25(17):3389-402 16697070 - Trends Genet. 2006 Jul;22(7):354-7 16176987 - Proc Natl Acad Sci U S A. 2005 Oct 4;102(40):14338-43 16619049 - Nat Rev Genet. 2006 May;7(5):337-48 16962765 - Curr Opin Biotechnol. 2006 Oct;17(5):481-7 15537804 - Mol Biol Evol. 2005 Mar;22(3):598-606 10421576 - Curr Biol. 1999 Jul 15;9(14):747-50 15737392 - J Hum Evol. 2005 Mar;48(3):237-57 17158152 - Nucleic Acids Res. 2007;35(1):e7 17984083 - Nucleic Acids Res. 2008 Jan;36(Database issue):D440-4 12519941 - Nucleic Acids Res. 2003 Jan 1;31(1):28-33 409339 - Annu Rev Biochem. 1977;46:573-639 1633570 - Comput Appl Biosci. 1992 Jun;8(3):275-82 14735123 - Nat Rev Genet. 2004 Feb;5(2):123-35 12713906 - Trends Biochem Sci. 2003 Apr;28(4):215-20 17237036 - Bioinformatics. 2007 Apr 1;23(7):815-24 12410938 - BMC Evol Biol. 2002 Nov 1;2:20 10611671 - Trends Cell Biol. 1999 Dec;9(12):M5-8 15746013 - Mol Biol Evol. 2005 May;22(5):1345-54 11827821 - Curr Opin Chem Biol. 2002 Feb;6(1):39-45 17483113 - Mol Biol Evol. 2007 Aug;24(8):1586-91 12045149 - Genome Res. 2002 Jun;12(6):962-8 15475113 - Trends Genet. 2004 Nov;20(11):544-9 17805289 - Nature. 2007 Sep 6;449(7158):54-61 12702209 - Genome Biol. 2003;4(4):R28 15800036 - Proc Natl Acad Sci U S A. 2005 Apr 12;102(15):5483-8 16777745 - Proc Biol Sci. 2006 Jun 22;273(1593):1507-15 16054255 - Trends Biotechnol. 2005 Oct;23(10):485-7 10830951 - Nature. 2000 May 18;405(6784):299-304 18040051 - Proc Natl Acad Sci U S A. 2007 Dec 4;104(49):19428-33 18948295 - Nucleic Acids Res. 2008 Dec;36(21):6688-719 9381173 - Science. 1997 Oct 24;278(5338):631-7 11430355 - Genetics. 2001 Jun;158(2):927-31 15034147 - Nucleic Acids Res. 2004;32(5):1792-7 15356622 - Nature. 2004 Sep 9;431(7005):152-5 12379152 - BMC Evol Biol. 2002;2:18 11682312 - Curr Opin Genet Dev. 2001 Dec;11(6):673-80 8743697 - Methods Enzymol. 1996;266:418-27 11429604 - Nature. 2001 Jun 28;411(6841):1046-9 12097341 - Genome Res. 2002 Jul;12(7):1048-59 16237209 - Mol Biol Evol. 2006 Feb;23(2):327-37 14634634 - Nat Rev Genet. 2003 Nov;4(11):865-75 17495009 - Genome Res. 2007 Jul;17(7):1045-50 15687288 - Genome Res. 2005 Feb;15(2):250-9 12175810 - Trends Genet. 2002 Sep;18(9):486 14525925 - Genome Res. 2003 Oct;13(10):2229-35 16151190 - Mol Biol Evol. 2006 Jan;23(1):1-3 |
| References_xml | – ident: e_1_3_3_30_2 doi: 10.1093/molbev/msj038 – ident: e_1_3_3_26_2 doi: 10.1073/pnas.0501761102 – ident: e_1_3_3_31_2 doi: 10.1016/j.cell.2008.05.042 – ident: e_1_3_3_32_2 doi: 10.1098/rspb.2006.3472 – ident: e_1_3_3_1_2 doi: 10.1016/S0962-8924(99)01664-5 – ident: e_1_3_3_15_2 doi: 10.1093/nar/gkl792 – ident: e_1_3_3_20_2 doi: 10.1038/nrg1838 – ident: e_1_3_3_53_2 doi: 10.1093/nar/gkg033 – ident: e_1_3_3_6_2 doi: 10.1038/nature02848 – ident: e_1_3_3_27_2 doi: 10.1093/genetics/158.2.927 – ident: e_1_3_3_52_2 doi: 10.1016/S1367-5931(01)00278-2 – ident: e_1_3_3_4_2 doi: 10.1038/nature04546 – ident: e_1_3_3_29_2 doi: 10.1093/molbev/msi122 – ident: e_1_3_3_42_2 doi: 10.1016/S0968-0004(03)00052-5 – volume: 8 start-page: 275 year: 1992 ident: e_1_3_3_61_2 article-title: The rapid generation of mutation data matrices from protein sequences publication-title: Comput Appl Biosci – ident: e_1_3_3_44_2 doi: 10.1093/nar/gkm883 – ident: e_1_3_3_38_2 doi: 10.1016/j.jhevol.2004.11.007 – ident: e_1_3_3_14_2 doi: 10.1101/gr.1589103 – start-page: 11 volume-title: Evolutionary Genomics and Proteomics year: 2008 ident: e_1_3_3_17_2 – ident: e_1_3_3_3_2 doi: 10.1093/nar/gkn668 – ident: e_1_3_3_57_2 doi: 10.1093/nar/gkh340 – ident: e_1_3_3_48_2 doi: 10.1371/journal.pbio.0040352 – ident: e_1_3_3_64_2 doi: 10.1214/aoms/1177704472 – ident: e_1_3_3_60_2 doi: 10.1016/S0076-6879(96)66026-1 – ident: e_1_3_3_37_2 doi: 10.1093/bioinformatics/btm015 – ident: e_1_3_3_19_2 doi: 10.1016/j.tig.2006.04.009 – ident: e_1_3_3_39_2 doi: 10.1073/pnas.0709013104 – ident: e_1_3_3_25_2 doi: 10.1101/gr.87702 – ident: e_1_3_3_40_2 doi: 10.1016/S0168-9525(02)02722-1 – volume-title: Molecular Evolution year: 1997 ident: e_1_3_3_41_2 – ident: e_1_3_3_28_2 doi: 10.1073/pnas.0504070102 – ident: e_1_3_3_47_2 doi: 10.1101/gr.3194805 – ident: e_1_3_3_35_2 doi: 10.1007/0-387-36747-0_12 – ident: e_1_3_3_5_2 doi: 10.1093/molbev/msh160 – ident: e_1_3_3_55_2 doi: 10.1093/nar/29.14.2994 – ident: e_1_3_3_51_2 doi: 10.1101/gr.174302 – ident: e_1_3_3_21_2 doi: 10.1017/CBO9780511623486 – ident: e_1_3_3_49_2 doi: 10.1186/1471-2148-2-18 – ident: e_1_3_3_23_2 doi: 10.1016/S0960-9822(99)80334-0 – ident: e_1_3_3_22_2 doi: 10.1146/annurev.bi.46.070177.003041 – ident: e_1_3_3_2_2 doi: 10.1038/35012500 – ident: e_1_3_3_62_2 doi: 10.1093/nar/gkl928 – ident: e_1_3_3_36_2 doi: 10.1038/nature06107 – ident: e_1_3_3_11_2 doi: 10.1016/S0959-437X(00)00252-5 – ident: e_1_3_3_10_2 doi: 10.1007/978-3-642-86659-3 – ident: e_1_3_3_56_2 doi: 10.1126/science.278.5338.631 – ident: e_1_3_3_12_2 doi: 10.1016/j.tig.2004.09.001 – ident: e_1_3_3_43_2 doi: 10.1101/gr.5978207 – ident: e_1_3_3_63_2 doi: 10.1186/gb-2003-4-4-r28 – ident: e_1_3_3_9_2 doi: 10.1016/j.tig.2007.10.002 – ident: e_1_3_3_13_2 doi: 10.1038/nrg1204 – ident: e_1_3_3_58_2 doi: 10.1016/B978-1-4832-3211-9.50009-7 – ident: e_1_3_3_59_2 doi: 10.1093/molbev/msm088 – ident: e_1_3_3_18_2 doi: 10.1016/j.copbio.2006.08.003 – ident: e_1_3_3_8_2 doi: 10.1186/gb-2004-5-2-r7 – ident: e_1_3_3_24_2 doi: 10.1038/35082561 – ident: e_1_3_3_46_2 doi: 10.1093/molbev/msj006 – ident: e_1_3_3_54_2 doi: 10.1093/nar/25.17.3389 – ident: e_1_3_3_34_2 doi: 10.1101/gr.10.7.991 – ident: e_1_3_3_16_2 doi: 10.1016/j.tibtech.2005.07.009 – ident: e_1_3_3_7_2 doi: 10.1038/nrg1271 – ident: e_1_3_3_33_2 doi: 10.1186/1471-2148-2-20 – ident: e_1_3_3_50_2 doi: 10.1016/j.gene.2008.02.014 – ident: e_1_3_3_45_2 doi: 10.1093/molbev/msi045 – reference: 14735123 - Nat Rev Genet. 2004 Feb;5(2):123-35 – reference: 15537804 - Mol Biol Evol. 2005 Mar;22(3):598-606 – reference: 16237209 - Mol Biol Evol. 2006 Feb;23(2):327-37 – reference: 16619049 - Nat Rev Genet. 2006 May;7(5):337-48 – reference: 16054255 - Trends Biotechnol. 2005 Oct;23(10):485-7 – reference: 10611671 - Trends Cell Biol. 1999 Dec;9(12):M5-8 – reference: 17142222 - Nucleic Acids Res. 2007 Jan;35(Database issue):D668-73 – reference: 18040051 - Proc Natl Acad Sci U S A. 2007 Dec 4;104(49):19428-33 – reference: 11682312 - Curr Opin Genet Dev. 2001 Dec;11(6):673-80 – reference: 16572163 - Nature. 2006 Mar 30;440(7084):623-30 – reference: 16176987 - Proc Natl Acad Sci U S A. 2005 Oct 4;102(40):14338-43 – reference: 14759257 - Genome Biol. 2004;5(2):R7 – reference: 15155797 - Mol Biol Evol. 2004 Sep;21(9):1643-60 – reference: 12379152 - BMC Evol Biol. 2002;2:18 – reference: 12713906 - Trends Biochem Sci. 2003 Apr;28(4):215-20 – reference: 11429604 - Nature. 2001 Jun 28;411(6841):1046-9 – reference: 18054114 - Trends Genet. 2008 Jan;24(1):1-4 – reference: 10421576 - Curr Biol. 1999 Jul 15;9(14):747-50 – reference: 16151190 - Mol Biol Evol. 2006 Jan;23(1):1-3 – reference: 15687288 - Genome Res. 2005 Feb;15(2):250-9 – reference: 15356622 - Nature. 2004 Sep 9;431(7005):152-5 – reference: 409339 - Annu Rev Biochem. 1977;46:573-639 – reference: 15737392 - J Hum Evol. 2005 Mar;48(3):237-57 – reference: 10830951 - Nature. 2000 May 18;405(6784):299-304 – reference: 17805289 - Nature. 2007 Sep 6;449(7158):54-61 – reference: 18378100 - Gene. 2008 May 15;414(1-2):85-94 – reference: 12410938 - BMC Evol Biol. 2002 Nov 1;2:20 – reference: 15746013 - Mol Biol Evol. 2005 May;22(5):1345-54 – reference: 12702209 - Genome Biol. 2003;4(4):R28 – reference: 1633570 - Comput Appl Biosci. 1992 Jun;8(3):275-82 – reference: 12097341 - Genome Res. 2002 Jul;12(7):1048-59 – reference: 17158152 - Nucleic Acids Res. 2007;35(1):e7 – reference: 12045149 - Genome Res. 2002 Jun;12(6):962-8 – reference: 17105342 - PLoS Biol. 2006 Nov;4(11):e352 – reference: 11452024 - Nucleic Acids Res. 2001 Jul 15;29(14):2994-3005 – reference: 9381173 - Science. 1997 Oct 24;278(5338):631-7 – reference: 15800036 - Proc Natl Acad Sci U S A. 2005 Apr 12;102(15):5483-8 – reference: 17483113 - Mol Biol Evol. 2007 Aug;24(8):1586-91 – reference: 17495009 - Genome Res. 2007 Jul;17(7):1045-50 – reference: 8743697 - Methods Enzymol. 1996;266:418-27 – reference: 17984083 - Nucleic Acids Res. 2008 Jan;36(Database issue):D440-4 – reference: 18948295 - Nucleic Acids Res. 2008 Dec;36(21):6688-719 – reference: 17237036 - Bioinformatics. 2007 Apr 1;23(7):815-24 – reference: 11827821 - Curr Opin Chem Biol. 2002 Feb;6(1):39-45 – reference: 16962765 - Curr Opin Biotechnol. 2006 Oct;17(5):481-7 – reference: 14525925 - Genome Res. 2003 Oct;13(10):2229-35 – reference: 12175810 - Trends Genet. 2002 Sep;18(9):486 – reference: 12519941 - Nucleic Acids Res. 2003 Jan 1;31(1):28-33 – reference: 16697070 - Trends Genet. 2006 Jul;22(7):354-7 – reference: 18662548 - Cell. 2008 Jul 25;134(2):341-52 – reference: 16777745 - Proc Biol Sci. 2006 Jun 22;273(1593):1507-15 – reference: 9254694 - Nucleic Acids Res. 1997 Sep 1;25(17):3389-402 – reference: 10899148 - Genome Res. 2000 Jul;10(7):991-1000 – reference: 11430355 - Genetics. 2001 Jun;158(2):927-31 – reference: 15034147 - Nucleic Acids Res. 2004;32(5):1792-7 – reference: 15475113 - Trends Genet. 2004 Nov;20(11):544-9 – reference: 14634634 - Nat Rev Genet. 2003 Nov;4(11):865-75 |
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| SubjectTerms | Age structure Animals Aspergillus Biological evolution Biological Sciences Correlation analysis Diptera Divergent evolution Eukaryotes Eukaryotic Cells - metabolism Evolution Evolution, Molecular fungi gene deletion Gene Transfer, Horizontal Genes Genome Genomes Genomics Humans introns lognormal distribution Mammals Mathematical models Models, Genetic phylogeny prokaryotic cells Proteins Proteins - genetics Wolves |
| Title | universal distribution of evolutionary rates of genes and distinct characteristics of eukaryotic genes of different apparent ages |
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