Skip to main content
  • ASM Journals
    • Antimicrobial Agents and Chemotherapy
    • Applied and Environmental Microbiology
    • Clinical Microbiology Reviews
    • Clinical and Vaccine Immunology
    • EcoSal Plus
    • Infection and Immunity
    • Journal of Bacteriology
    • Journal of Clinical Microbiology
    • Journal of Microbiology & Biology Education
    • Journal of Virology
    • mBio
    • Microbiology and Molecular Biology Reviews
    • Microbiology Resource Announcements
    • Microbiology Spectrum
    • Molecular and Cellular Biology
    • mSphere
    • mSystems
  • Log in
  • My alerts
  • My Cart

Main menu

  • Home
  • Articles
    • Latest Articles
    • Special Issues
    • COVID-19 Special Collection
    • Editor's Picks
    • Special Series: Sponsored Minireviews and Video Abstracts
    • Archive
  • Topics
    • Applied and Environmental Science
    • Ecological and Evolutionary Science
    • Host-Microbe Biology
    • Molecular Biology and Physiology
    • Novel Systems Biology Techniques
    • Early-Career Systems Microbiology Perspectives
  • For Authors
    • Getting Started
    • Submit a Manuscript
    • Scope
    • Editorial Policy
    • Submission, Review, & Publication Processes
    • Organization and Format
    • Errata, Author Corrections, Retractions
    • Illustrations and Tables
    • Nomenclature
    • Abbreviations and Conventions
    • Publication Fees
    • Ethics
  • About the Journal
    • About mSystems
    • Editor in Chief
    • Board of Editors
    • For Reviewers
    • For the Media
    • For Librarians
    • For Advertisers
    • Alerts
    • RSS
    • FAQ
  • ASM Journals
    • Antimicrobial Agents and Chemotherapy
    • Applied and Environmental Microbiology
    • Clinical Microbiology Reviews
    • Clinical and Vaccine Immunology
    • EcoSal Plus
    • Infection and Immunity
    • Journal of Bacteriology
    • Journal of Clinical Microbiology
    • Journal of Microbiology & Biology Education
    • Journal of Virology
    • mBio
    • Microbiology and Molecular Biology Reviews
    • Microbiology Resource Announcements
    • Microbiology Spectrum
    • Molecular and Cellular Biology
    • mSphere
    • mSystems

User menu

  • Log in
  • My alerts
  • My Cart

Search

  • Advanced search
mSystems
publisher-logosite-logo

Advanced Search

  • Home
  • Articles
    • Latest Articles
    • Special Issues
    • COVID-19 Special Collection
    • Editor's Picks
    • Special Series: Sponsored Minireviews and Video Abstracts
    • Archive
  • Topics
    • Applied and Environmental Science
    • Ecological and Evolutionary Science
    • Host-Microbe Biology
    • Molecular Biology and Physiology
    • Novel Systems Biology Techniques
    • Early-Career Systems Microbiology Perspectives
  • For Authors
    • Getting Started
    • Submit a Manuscript
    • Scope
    • Editorial Policy
    • Submission, Review, & Publication Processes
    • Organization and Format
    • Errata, Author Corrections, Retractions
    • Illustrations and Tables
    • Nomenclature
    • Abbreviations and Conventions
    • Publication Fees
    • Ethics
  • About the Journal
    • About mSystems
    • Editor in Chief
    • Board of Editors
    • For Reviewers
    • For the Media
    • For Librarians
    • For Advertisers
    • Alerts
    • RSS
    • FAQ
Observation | Ecological and Evolutionary Science

An Important Role for Purifying Selection in Archaeal Genome Evolution

Zhe Lyu, Zhi-Gang Li, Fei He, Ziding Zhang
Michael Rust, Editor
Zhe Lyu
a College of Resources and Environmental Sciences, China Agricultural University, Beijing, China
b Department of Microbiology, University of Georgia, Athens, Georgia, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Zhe Lyu
Zhi-Gang Li
c State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China
d State Key Laboratory of Agrobiotechnology and Ministry of Agriculture Key Laboratory of Plant Pathology, Beijing, China
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Fei He
c State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Fei He
Ziding Zhang
c State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Ziding Zhang
Michael Rust
Institute for Genomics & Systems Biology
Roles: Editor
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
DOI: 10.1128/mSystems.00112-17
  • Article
  • Figures & Data
  • Info & Metrics
  • PDF
Loading

ABSTRACT

As the null hypothesis of genome evolution, population genetic theory suggests that selection strength controls genome size. Through the process of genetic drift, this theory predicts that compact genomes are maintained by strong purifying selection while complex genomes are enabled by weak purifying selection. It offers a unifying framework that explains why prokaryotic genomes are much smaller than their eukaryotic counterparts. However, recent findings suggest that bigger prokaryotic genomes appear to experience stronger purifying selection, indicating that purifying selection may not dominate prokaryotic genome evolution. Since archaeal genomes were underrepresented in those studies, generalization of the conclusions to both archaeal and bacterial genomes may not be warranted. In this study, we revisited this matter by focusing on archaeal and bacterial genomes separately. We found that bigger bacterial genomes indeed experienced stronger purifying selection, but the opposite was observed in archaeal genomes. This new finding would predict an enrichment of noncoding sequences in large archaeal genomes, which was confirmed by an analysis of coding density. In contrast, coding density remained stable regardless of bacterial genome size. In conclusion, this study suggests that purifying selection may play a more important role in archaeal genome evolution than previously hypothesized, indicating that there could be a major difference between the evolutionary regimes of Archaea and Bacteria.

IMPORTANCE The evolution of genome complexity is a fundamental question in biology. A hallmark of eukaryotic genome complexity is that larger genomes tend to have more noncoding sequences, which are believed to be minimal in archaeal and bacterial genomes. However, we found that archaeal genomes also possessed this eukaryotic feature while bacterial genomes did not. This could be predicted from our analysis on genetic drift, which showed a relaxation of purifying selection in larger archaeal genomes, also a eukaryotic feature. In contrast, the opposite was evident in bacterial genomes.

OBSERVATION

Eukaryotic genomes vary in size by orders of magnitude more than prokaryotic genomes. The genome size range is about 106 to 1011 bp in eukaryotes, which are rich in noncoding sequences, and 105 to 107 bp in prokaryotes, which are streamlined and have minimal noncoding sequences (1, 2). This genome size gap is believed to be shaped primarily by nonadaptive processes based on the population genetic theory (3). That theory suggests that prokaryotes often undergo strong purifying selection owing to a generally large effective population size to maintain compact genomes (2). In contrast, eukaryotes typically have a much smaller effective population size and are subject to weak purifying selection, which enables large genomes (4). This is because all excess DNA is mutationally hazardous, and the efficiency of selection determines whether the excess DNA is removed from or fixed in the genome through the process of genetic drift (5).

The efficiency of selection can be approximately measured by the genome-wide dN/dS ratio (ratio of nonsynonymous to synonymous substitution rates) for orthologous genes shared by closely related lineages, and the stronger the purifying selection becomes, the lower the dN/dS ratio is (6). The population genetic theory predicts that bigger genomes experience weaker purifying selection or higher dN/dS ratios (2–4). However, recent findings regarding prokaryotic genomes suggest otherwise, showing that genome size is negatively correlated with the dN/dS ratio (7, 8). This only seems possible when gene gains could be slightly beneficial and that the benefits would be diluted out because of genome expansion and deletion bias (i.e., DNA loss outpaces gain), based on mathematical models (8). The benefits of gene gains thus make genome expansion possible under strong purifying selection, but the expansion stops once the benefits diminish, thus restraining the overall genome size. Indeed, deletion bias appears to be universal across the full range of cellular life forms, and its strength tends to decline when the genome expands, indicating a dynamic balance between DNA loss and DNA gain (1, 9, 10).

While bacterial species were extensively sampled in these studies, archaeal species were underrepresented (7, 8). Therefore, generalization of the mechanisms identified therein may not be warranted in Archaea. In this study, we concentrated on archaeal genomes to revisit previous hypotheses of genome size evolution. Bacterial genomes were also sampled and examined for comparison when necessary. We observed that the strength of purifying selection and the amount of noncoding genes were negatively and positively associated with archaeal genome size, respectively, as predicted by population genetic theory. In contrast, the opposite trend was evident in bacterial genomes.

Expansion of archaeal genomes associated with relaxed purifying selection.In eukaryotes, relaxed purifying selection is associated with genome expansion, which is consistent with the accumulation of introns and mobile elements that are often deleterious (3).

While our bacterial data set reproduced the previous observations showing that strong purifying selection was associated with genome expansion (7, 8), our archaeal data set revealed a eukaryote-like pattern based on a genome-wide dN/dS ratio analysis (Fig. 1A and B; see Tables S1 and S2 in the supplemental material). This finding predicts an enrichment of noncoding sequences in larger archaeal genomes, as also observed in eukaryotic genomes (see below for coding density analysis).

FIG 1
  • Open in new tab
  • Download powerpoint
FIG 1

Association between genome size and the dN/dS ratio for archaeal (A; n = 21) and bacterial (B; n = 28) genome pairs and association between coding density and genome size in Archaea (C; n = 49) and Bacteria (D; n = 78). Note that the stronger the purifying selection becomes, the lower the dN/dS ratio is. Spearman rank correlation coefficients (Rs) and two-sided significance (P) values are indicated in each panel. Genome sizes are shown in megabase pairs (Mb) in panels C and D but are in protein coding sequences (CDS) in panels A and B, as only protein coding sequences were used for dN/dS ratio analysis. Regardless, the same trends were reproduced when genome size measured in base pairs instead of protein coding sequences was used.

TABLE S1

dN/dS ratio data extracted from 21 archaeal species pairs. Download TABLE S1, DOCX file, 0.05 MB.
Copyright © 2017 Lyu et al.

This content is distributed under the terms of the Creative Commons Attribution 4.0 International license .

TABLE S2

dN/dS ratio data extracted from 28 bacterial species pairs. Download TABLE S2, DOCX file, 0.03 MB.
Copyright © 2017 Lyu et al.

This content is distributed under the terms of the Creative Commons Attribution 4.0 International license .

At least a couple of observations indicated that our archaeal and bacterial data sets were comparable and likely representative. First, both archaeal and bacterial mean dN/dS ratios were between 0.05 and 0.20 (Fig. 1A and B) and a similar range has also been observed in eukaryotic lineages (11, 12). Second, most archaeal and bacterial genome pairs had an average nucleotide identity (ANI) of 75 to 95% (Fig. 2), an empirical range that delineated closely related prokaryotic species belonging to the same genus (13). This minimized the potential bias of dN/dS ratio computations caused by uneven variation in evolutionary distances between genomes (7). Nevertheless, while the bacterial samplings both here and in previous studies seem to have captured substantial taxonomic diversities of bacterial genomes, the archaeal samplings need further improvements. Sampling of archaeal genomes across more phyla will be necessary to begin to address their full genome size range. This is because the archaeal genome size narrowly ranges from 0.5 to 6 Mb compared to the 0.6- to 9-Mb range of bacterial genomes (Tables S3 and S4). Of the 29 prokaryotic phyla taxonomically established, only 2 belong to Archaea, where most complete archaeal genomes currently come from, and an estimated ~300 archaeal phyla still wait to be sampled (14).

FIG 2
  • Open in new tab
  • Download powerpoint
FIG 2

Association between the dN/dS ratio and genomic distance as measured by percent ANI. Closed circles represent bacterial pairs (n = 28), and open triangles represent archaeal pairs (n = 21).

TABLE S3

Coding densities of 49 archaeal genomes. Download TABLE S3, DOCX file, 0.04 MB.
Copyright © 2017 Lyu et al.

This content is distributed under the terms of the Creative Commons Attribution 4.0 International license .

TABLE S4

Coding densities of 79 bacterial genomes. Download TABLE S4, DOCX file, 0.04 MB.
Copyright © 2017 Lyu et al.

This content is distributed under the terms of the Creative Commons Attribution 4.0 International license .

Coding density in Archaea shows a trend similar to that seen in eukaryotes.Coding density or gene density, as measured by the proportion of a genome sequence that is composed of coding or gene sequence, has been shown to correlate negatively and neutrally with eukaryotic and bacterial genome sizes, respectively (7, 15, 16). This observation made in eukaryotes is consistent with a genomic structure showing that noncoding sequences, primarily spliceosomal introns and mobile genetic elements, are overrepresented in larger eukaryotic genomes (3). The observation regarding Bacteria is also consistent with the dynamic balance between DNA loss and DNA gain (1, 8, 9). While our bacterial data set confirmed previous observations, our archaeal data set again revealed a eukaryote-like pattern (Fig. 1C and D and Tables S3 and S4). That is to say, a strong negative correlation was observed between coding density and archaeal genome size, indicating that an insertion bias enriching noncoding sequences was also evident during the expansion of archaeal genomes. It remains elusive what category of noncoding sequences is overrepresented in larger archaeal genomes, as the nature of most archaeal noncoding sequences is poorly characterized (17). Regardless of the category, it would probably be different from that observed in eukaryotes, as spliceosomal introns have never been found in Archaea and the distribution of known mobile elements in archaeal genomes is similar to that in their bacterial counterparts (18). However, the presence of novel mobile elements in Archaea could not be ruled out. Compared to eukaryotes, one common feature is still shared by Archaea and Bacteria, both of which had a similar and narrow range of coding density, i.e., about 70 to 95% in our data set. In contrast, the eukaryotic coding density roughly ranged from 1 to 80% (15). Therefore, there could be an unknown force(s) that prevents noncoding regions in archaeal genomes from enriching to the extremely high levels observed in eukaryotes.

This study presents the first evidence that the evolutionary regimen of the complexity of archaeal genomes could be significantly different from that of their bacterial counterparts. Instead, certain key eukaryote-like evolutionary features seem to be already embedded in archaeal genomes. On the one hand, those observations seem striking, as the architecture of archaeal genomes is very similar to that of bacterial genomes (2). On the other hand, they may simply reflect the close evolutionary connections between Archaea and eukaryotes, since eukaryotes likely evolved within the Archaea (19). Although the archaeal samplings here are still limited and the similarities drawn between Archaea and eukaryotes here are preliminary, we believe our study serves as a sufficient reminder that it is probably no longer a safe practice to group archaeal and bacterial genomes into one category when testing evolutionary hypotheses. Rather, comparisons should be made among Archaea, Bacteria, and eukaryotes.

Data collection and coding density calculation.Genomic data from closely related species that belong to the same genus were downloaded from NCBI (http://www.ncbi.nlm.nih.gov/ ). Genera or species that were not well established, i.e., those whose taxonomic names were not formally proposed or alternative names were available, were removed before further analysis. The coding density of each genome was calculated as follows: % coding density = (DNA length of all coding sequences/total DNA length of complete genome) ×100%.

dN/dS ratios and ANI analyses.For each pair of genomes, pairs of orthologs were identified by a hybrid procedure combining Bidirectional Best Hit and BLASTclust (20). The orthologous pairs identified were aligned by ClustalW2, and dN/dS ratios were calculated by using YN00 in the PAML package (21, 22). Synonymous sites and ratios of synonymous to nonsynonymous sites considered to be saturated or unreliable (that is, a dS of <0.1, a dS of >1.6, or a dN/dS ratio of >99) were discarded before the mean dN/dS ratio and standard error of the mean of each genome pair were both calculated and reported in Tables S1 and S2. Although data filtration is a common practice to ensure better estimation of selective pressure, it also substantially reduces the number of pairwise orthologs available for downstream analyses (7, 11). This would make the data set less representative for inferring genome-wide patterns. Therefore, only genome pairs retaining pairwise orthologs accounting for no less than 5% of the pairs’ average coding capacity after all filtrations were kept for ultimate analyses. This cutoff is the starting point to represent most of the essential genes determined in prokaryotic genomes (23). ANI values were retrieved from IMG (24).

Correlation analysis.Spearman rank correlation (v1.0.3) was employed to obtain correlation coefficients (Rs) and two-sided P values (25).

ACKNOWLEDGMENTS

We thank William B. Whitman, Deng-Ke Niu, and anonymous reviewers for suggestions about this work. We are in debt to Yahai Lu for strong support with the initiation of the long-term cooperation among the coauthors. We apologize for being unable to cite all of the relevant references because of space limitation.

This work was supported in part by National Natural Science Foundation of China grant 31471249.

FOOTNOTES

    • Received August 22, 2017.
    • Accepted October 6, 2017.
  • Copyright © 2017 Lyu et al.

This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license .

REFERENCES

  1. 1.↵
    1. Mira A,
    2. Ochman H,
    3. Moran NA
    . 2001. Deletional bias and the evolution of bacterial genomes. Trends Genet17:589–596. doi:10.1016/S0168-9525(01)02447-7.
    OpenUrlCrossRefPubMedWeb of Science
  2. 2.↵
    1. Lynch M
    . 2006. Streamlining and simplification of microbial genome architecture. Annu Rev Microbiol60:327–349. doi:10.1146/annurev.micro.60.080805.142300.
    OpenUrlCrossRefPubMedWeb of Science
  3. 3.↵
    1. Lynch M,
    2. Conery JS
    . 2003. The origins of genome complexity. Science302:1401–1404. doi:10.1126/science.1089370.
    OpenUrlAbstract/FREE Full Text
  4. 4.↵
    1. Lynch M
    . 2006. The origins of eukaryotic gene structure. Mol Biol Evol23:450–468. doi:10.1093/molbev/msj050.
    OpenUrlCrossRefPubMedWeb of Science
  5. 5.↵
    1. Lynch M,
    2. Marinov GK
    . 2015. The bioenergetic costs of a gene. Proc Natl Acad Sci U S A112:15690–15695. doi:10.1073/pnas.1514974112.
    OpenUrlAbstract/FREE Full Text
  6. 6.↵
    1. Yang Z,
    2. Bielawski JP
    . 2000. Statistical methods for detecting molecular adaptation. Trends Ecol Evol15:496–503. doi:10.1016/S0169-5347(00)01994-7.
    OpenUrlCrossRefPubMedWeb of Science
  7. 7.↵
    1. Novichkov PS,
    2. Wolf YI,
    3. Dubchak I,
    4. Koonin EV
    . 2009. Trends in prokaryotic evolution revealed by comparison of closely related bacterial and archaeal genomes. J Bacteriol191:65–73. doi:10.1128/JB.01237-08.
    OpenUrlAbstract/FREE Full Text
  8. 8.↵
    1. Sela I,
    2. Wolf YI,
    3. Koonin EV
    . 2016. Theory of prokaryotic genome evolution. Proc Natl Acad Sci U S A113:11399–11407. doi:10.1073/pnas.1614083113.
    OpenUrlAbstract/FREE Full Text
  9. 9.↵
    1. Kuo CH,
    2. Ochman H
    . 2009. Deletional bias across the three domains of life. Genome Biol Evol1:145–152. doi:10.1093/gbe/evp016.
    OpenUrlCrossRefPubMed
  10. 10.↵
    1. Petrov DA,
    2. Sangster TA,
    3. Johnston JS,
    4. Hartl DL,
    5. Shaw KL
    . 2000. Evidence for DNA loss as a determinant of genome size. Science287:1060–1062. doi:10.1126/science.287.5455.1060.
    OpenUrlAbstract/FREE Full Text
  11. 11.↵
    1. Kelkar YD,
    2. Ochman H
    . 2012. Causes and consequences of genome expansion in fungi. Genome Biol Evol4:13–23. doi:10.1093/gbe/evr124.
    OpenUrlCrossRefPubMed
  12. 12.↵
    1. Hutter B,
    2. Bieg M,
    3. Helms V,
    4. Paulsen M
    . 2010. Divergence of imprinted genes during mammalian evolution. BMC Evol Biol10:116. doi:10.1186/1471-2148-10-116.
    OpenUrlCrossRefPubMed
  13. 13.↵
    1. Kim M,
    2. Oh HS,
    3. Park SC,
    4. Chun J
    . 2014. Towards a taxonomic coherence between average nucleotide identity and 16S rRNA gene sequence similarity for species demarcation of prokaryotes. Int J Syst Evol Microbiol64:346–351. doi:10.1099/ijs.0.059774-0.
    OpenUrlCrossRefPubMedWeb of Science
  14. 14.↵
    1. Yarza P,
    2. Yilmaz P,
    3. Pruesse E,
    4. Glöckner FO,
    5. Ludwig W,
    6. Schleifer KH,
    7. Whitman WB,
    8. Euzéby J,
    9. Amann R,
    10. Rosselló-Móra R
    . 2014. Uniting the classification of cultured and uncultured bacteria and archaea using 16S rRNA gene sequences. Nat Rev Microbiol12:635–645. doi:10.1038/nrmicro3330.
    OpenUrlCrossRefPubMed
  15. 15.↵
    1. Hou Y,
    2. Lin S
    . 2009. Distinct gene number-genome size relationships for eukaryotes and non-eukaryotes: gene content estimation for dinoflagellate genomes. PLoS One4:e6978. doi:10.1371/journal.pone.0006978.
    OpenUrlCrossRefPubMed
  16. 16.↵
    1. Kuo CH,
    2. Moran NA,
    3. Ochman H
    . 2009. The consequences of genetic drift for bacterial genome complexity. Genome Res19:1450–1454. doi:10.1101/gr.091785.109.
    OpenUrlAbstract/FREE Full Text
  17. 17.↵
    1. Makarova KS,
    2. Wolf YI,
    3. Forterre P,
    4. Prangishvili D,
    5. Krupovic M,
    6. Koonin EV
    . 2014. Dark matter in archaeal genomes: a rich source of novel mobile elements, defense systems and secretory complexes. Extremophiles18:877–893. doi:10.1007/s00792-014-0672-7.
    OpenUrlCrossRef
  18. 18.↵
    1. Filée J,
    2. Siguier P,
    3. Chandler M
    . 2007. Insertion sequence diversity in archaea. Microbiol Mol Biol Rev71:121–157. doi:10.1128/MMBR.00031-06.
    OpenUrlAbstract/FREE Full Text
  19. 19.↵
    1. Zaremba-Niedzwiedzka K,
    2. Caceres EF,
    3. Saw JH,
    4. Bäckström D,
    5. Juzokaite L,
    6. Vancaester E,
    7. Seitz KW,
    8. Anantharaman K,
    9. Starnawski P,
    10. Kjeldsen KU,
    11. Stott MB,
    12. Nunoura T,
    13. Banfield JF,
    14. Schramm A,
    15. Baker BJ,
    16. Spang A,
    17. Ettema TJG
    . 2017. Asgard archaea illuminate the origin of eukaryotic cellular complexity. Nature541:353–358. doi:10.1038/nature21031.
    OpenUrlCrossRefPubMed
  20. 20.↵
    1. Madan Babu M,
    2. Teichmann SA,
    3. Aravind L
    . 2006. Evolutionary dynamics of prokaryotic transcriptional regulatory networks. J Mol Biol358:614–633. doi:10.1016/j.jmb.2006.02.019.
    OpenUrlCrossRefPubMedWeb of Science
  21. 21.↵
    1. Friedman R,
    2. Drake JW,
    3. Hughes AL
    . 2004. Genome-wide patterns of nucleotide substitution reveal stringent functional constraints on the protein sequences of thermophiles. Genetics167:1507–1512. doi:10.1534/genetics.104.026344.
    OpenUrlAbstract/FREE Full Text
  22. 22.↵
    1. Yang Z
    . 2007. PAML 4: phylogenetic analysis by maximum likelihood. Mol Biol Evol24:1586–1591. doi:10.1093/molbev/msm088.
    OpenUrlCrossRefPubMedWeb of Science
  23. 23.↵
    1. Luo H,
    2. Lin Y,
    3. Gao F,
    4. Zhang CT,
    5. Zhang R
    . 2014. DEG 10, an update of the database of essential genes that includes both protein-coding genes and noncoding genomic elements. Nucleic Acids Res42:D574–D580. doi:10.1093/nar/gkt1131.
    OpenUrlCrossRefPubMedWeb of Science
  24. 24.↵
    1. Varghese NJ,
    2. Mukherjee S,
    3. Ivanova N,
    4. Konstantinidis KT,
    5. Mavrommatis K,
    6. Kyrpides NC,
    7. Pati A
    . 2015. Microbial species delineation using whole genome sequences. Nucleic Acids Res43:6761–6771. doi:10.1093/nar/gkv657.
    OpenUrlCrossRefPubMed
  25. 25.↵
    1. Wessa P
    . 2017. Spearman rank correlation (v1.0.3) in free statistics software (v1.2.1). Office for Research Development and Education, Leuven Institute for Research on Information Systems, University of Leuven, Leuven, Belgium. https://www.wessa.net/rwasp_spearman.wasp/ .
PreviousNext
Back to top
Download PDF
Citation Tools
An Important Role for Purifying Selection in Archaeal Genome Evolution
Zhe Lyu, Zhi-Gang Li, Fei He, Ziding Zhang
mSystems Oct 2017, 2 (5) e00112-17; DOI: 10.1128/mSystems.00112-17

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Print
Alerts
Sign In to Email Alerts with your Email Address
Email

Thank you for sharing this mSystems article.

NOTE: We request your email address only to inform the recipient that it was you who recommended this article, and that it is not junk mail. We do not retain these email addresses.

Enter multiple addresses on separate lines or separate them with commas.
An Important Role for Purifying Selection in Archaeal Genome Evolution
(Your Name) has forwarded a page to you from mSystems
(Your Name) thought you would be interested in this article in mSystems.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
An Important Role for Purifying Selection in Archaeal Genome Evolution
Zhe Lyu, Zhi-Gang Li, Fei He, Ziding Zhang
mSystems Oct 2017, 2 (5) e00112-17; DOI: 10.1128/mSystems.00112-17
del.icio.us logo Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
  • Top
  • Article
    • ABSTRACT
    • OBSERVATION
    • ACKNOWLEDGMENTS
    • FOOTNOTES
    • REFERENCES
  • Figures & Data
  • Info & Metrics
  • PDF

KEYWORDS

Archaea
evolution
genome analysis

Related Articles

Cited By...

About

  • About mSystems
  • Author Videos
  • Board of Editors
  • Policies
  • Overleaf Pilot
  • For Reviewers
  • For the Media
  • For Librarians
  • For Advertisers
  • Alerts
  • RSS
  • FAQ
  • Permissions
  • Journal Announcements

Authors

  • ASM Author Center
  • Submit a Manuscript
  • Author Warranty
  • Types of Articles
  • Getting Started
  • Ethics
  • Contact Us

Follow #mSystemsJ

@ASMicrobiology

       

 

ASM Journals

ASM journals are the most prominent publications in the field, delivering up-to-date and authoritative coverage of both basic and clinical microbiology.

About ASM | Contact Us | Press Room

 

ASM is a member of

Scientific Society Publisher Alliance

Copyright © 2021 American Society for Microbiology | Privacy Policy | Website feedback

Online ISSN: 2379-5077