We are analyzing https://link.springer.com/article/10.1007/s00239-004-2597-8.

Title:
A Maximum Likelihood Method for Detecting Functional Divergence at Individual Codon Sites, with Application to Gene Family Evolution | Journal of Molecular Evolution
Description:
The tailoring of existing genetic systems to new uses is called genetic co-option. Mechanisms of genetic co-option have been difficult to study because of difficulties in identifying functionally important changes. One way to study genetic co-option in protein-coding genes is to identify those amino acid sites that have experienced changes in selective pressure following a genetic co-option event. In this paper we present a maximum likelihood method useful for measuring divergent selective pressures and identifying the amino acid sites affected by divergent selection. The method is based on a codon model of evolution and uses the nonsynonymous-to-synonymous rate ratio (ω) as a measure of selection on the protein, with ω = 1, <1, and >1 indicating neutral evolution, purifying selection, and positive selection, respectively. The model allows variation in ω among sites, with a fraction of sites evolving under divergent selective pressures. Divergent selection is indicated by different ω’s between clades, such as between paralogous clades of a gene family. We applied the codon model to duplication followed by functional divergence of (i) the ε and γ globin genes and (ii) the eosinophil cationic protein (ECP) and eosinophil-derived neurotoxin (EDN) genes. In both cases likelihood ratio tests suggested the presence of sites evolving under divergent selective pressures. Results of the ε and γ globin analysis suggested that divergent selective pressures might be a consequence of a weakened relationship between fetal hemoglobin and 2,3-diphosphoglycerate. We suggest that empirical Bayesian identification of sites evolving under divergent selective pressures, combined with structural and functional information, can provide a valuable framework for identifying and studying mechanisms of genetic co-option. Limitations of the new method are discussed.
Website Age:
28 years and 1 months (reg. 1997-05-29).

Matching Content Categories {📚}

Science
Education
Animals & Wildlife

Content Management System {📝}

What CMS is link.springer.com built with?

Custom-built

No common CMS systems were detected on Link.springer.com, and no known web development framework was identified.

Traffic Estimate {📈}

What is the average monthly size of link.springer.com audience?

🌠 Phenomenal Traffic: 5M - 10M visitors per month

Based on our best estimate, this website will receive around 5,000,019 visitors per month in the current month.
However, some sources were not loaded, we suggest to reload the page to get complete results.

check SE Ranking
check Ahrefs
check Similarweb
check Ubersuggest
check Semrush

How Does Link.springer.com Make Money? {💸}

We can't tell how the site generates income.

While many websites aim to make money, others are created to share knowledge or showcase creativity. People build websites for various reasons. This could be one of them. Link.springer.com has a secret sauce for making money, but we can't detect it yet.

Keywords {🔍}

google, scholar, cas, pubmed, article, gene, evolution, mol, evol, yang, genes, selection, biol, likelihood, sites, duplication, molecular, detecting, functional, bielawski, sci, goodman, divergence, divergent, proc, usa, maximum, codon, genetic, access, evolutionary, genetics, natl, acad, cooption, selective, model, science, schneider, privacy, cookies, content, journal, research, method, family, amino, acid, pressures, ratio,

Topics {✒️}

month download article/chapter zhang hf rosenberg neiswanger dl gumucio gamma-globin gene sequences czelusniak sl page gamma-globin gene mediated redundant gamma-globin gene shh-bmp2 signalling module fetal globin expression human eosinophil-derived neurotoxin full article pdf goodman ca porter goldman a-mk pedersen related subjects gene duplication proc sieu jl slightom evolutionary rate shifts detecting positive selection detecting molecular adaptation detecting functional divergence hemoglobin beta-chain genes detecting adaptive evolution codon-substitution models intronless coding sequences host-specific selection privacy choices/manage cookies molecular evolution aims long ch langley γ globin genes likelihood ratio tests article bielawski knudsen mm miyamoto positive darwinian selection predicting functional divergence human-ape splitting article journal amino acid sites protein-coding genes maximum-likelihood approach gene family evolution codon-based model codon based model maximum likelihood method genome coding regions eosinophil cationic protein synonymous rate ratio heterogeneous selection pressure likelihood ratio test maximum likelihood methods eosinophil-derived neurotoxin

Questions {❓}

What if there are only 30,000 human genes?

Schema {🗺️}

WebPage:
      mainEntity:
         headline:A Maximum Likelihood Method for Detecting Functional Divergence at Individual Codon Sites, with Application to Gene Family Evolution
         description:The tailoring of existing genetic systems to new uses is called genetic co-option. Mechanisms of genetic co-option have been difficult to study because of difficulties in identifying functionally important changes. One way to study genetic co-option in protein-coding genes is to identify those amino acid sites that have experienced changes in selective pressure following a genetic co-option event. In this paper we present a maximum likelihood method useful for measuring divergent selective pressures and identifying the amino acid sites affected by divergent selection. The method is based on a codon model of evolution and uses the nonsynonymous-to-synonymous rate ratio (ω) as a measure of selection on the protein, with ω = 1, <1, and >1 indicating neutral evolution, purifying selection, and positive selection, respectively. The model allows variation in ω among sites, with a fraction of sites evolving under divergent selective pressures. Divergent selection is indicated by different ω’s between clades, such as between paralogous clades of a gene family. We applied the codon model to duplication followed by functional divergence of (i) the ε and γ globin genes and (ii) the eosinophil cationic protein (ECP) and eosinophil-derived neurotoxin (EDN) genes. In both cases likelihood ratio tests suggested the presence of sites evolving under divergent selective pressures. Results of the ε and γ globin analysis suggested that divergent selective pressures might be a consequence of a weakened relationship between fetal hemoglobin and 2,3-diphosphoglycerate. We suggest that empirical Bayesian identification of sites evolving under divergent selective pressures, combined with structural and functional information, can provide a valuable framework for identifying and studying mechanisms of genetic co-option. Limitations of the new method are discussed.
         datePublished:
         dateModified:
         pageStart:121
         pageEnd:132
         sameAs:https://doi.org/10.1007/s00239-004-2597-8
         keywords:
            Maximum likelihood
            Functional divergence
            Codon model
            ECP
            EDN
            Globins
            Evolutionary Biology
            Microbiology
            Plant Sciences
            Plant Genetics and Genomics
            Animal Genetics and Genomics
            Cell Biology
         image:
            https://media.springernature.com/lw1200/springer-static/image/art%3A10.1007%2Fs00239-004-2597-8/MediaObjects/fig1.jpg
            https://media.springernature.com/lw1200/springer-static/image/art%3A10.1007%2Fs00239-004-2597-8/MediaObjects/fig2.jpg
            https://media.springernature.com/lw1200/springer-static/image/art%3A10.1007%2Fs00239-004-2597-8/MediaObjects/fig3.jpg
         isPartOf:
            name:Journal of Molecular Evolution
            issn:
               1432-1432
               0022-2844
            volumeNumber:59
            type:
               Periodical
               PublicationVolume
         publisher:
            name:Springer-Verlag
            logo:
               url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
               type:ImageObject
            type:Organization
         author:
               name:Joseph P. Bielawski
               affiliation:
                     name:University College London
                     address:
                        name:Department of Biology, University College London, London, UK
                        type:PostalAddress
                     type:Organization
                     name:Dalhousie University
                     address:
                        name:Department of Biology, Dalhousie University, Halifax, Canada
                        type:PostalAddress
                     type:Organization
               email:[email protected]
               type:Person
               name:Ziheng Yang
               affiliation:
                     name:University College London
                     address:
                        name:Department of Biology, University College London, London, UK
                        type:PostalAddress
                     type:Organization
               type:Person
         isAccessibleForFree:
         hasPart:
            isAccessibleForFree:
            cssSelector:.main-content
            type:WebPageElement
         type:ScholarlyArticle
      context:https://schema.org
ScholarlyArticle:
      headline:A Maximum Likelihood Method for Detecting Functional Divergence at Individual Codon Sites, with Application to Gene Family Evolution
      description:The tailoring of existing genetic systems to new uses is called genetic co-option. Mechanisms of genetic co-option have been difficult to study because of difficulties in identifying functionally important changes. One way to study genetic co-option in protein-coding genes is to identify those amino acid sites that have experienced changes in selective pressure following a genetic co-option event. In this paper we present a maximum likelihood method useful for measuring divergent selective pressures and identifying the amino acid sites affected by divergent selection. The method is based on a codon model of evolution and uses the nonsynonymous-to-synonymous rate ratio (ω) as a measure of selection on the protein, with ω = 1, <1, and >1 indicating neutral evolution, purifying selection, and positive selection, respectively. The model allows variation in ω among sites, with a fraction of sites evolving under divergent selective pressures. Divergent selection is indicated by different ω’s between clades, such as between paralogous clades of a gene family. We applied the codon model to duplication followed by functional divergence of (i) the ε and γ globin genes and (ii) the eosinophil cationic protein (ECP) and eosinophil-derived neurotoxin (EDN) genes. In both cases likelihood ratio tests suggested the presence of sites evolving under divergent selective pressures. Results of the ε and γ globin analysis suggested that divergent selective pressures might be a consequence of a weakened relationship between fetal hemoglobin and 2,3-diphosphoglycerate. We suggest that empirical Bayesian identification of sites evolving under divergent selective pressures, combined with structural and functional information, can provide a valuable framework for identifying and studying mechanisms of genetic co-option. Limitations of the new method are discussed.
      datePublished:
      dateModified:
      pageStart:121
      pageEnd:132
      sameAs:https://doi.org/10.1007/s00239-004-2597-8
      keywords:
         Maximum likelihood
         Functional divergence
         Codon model
         ECP
         EDN
         Globins
         Evolutionary Biology
         Microbiology
         Plant Sciences
         Plant Genetics and Genomics
         Animal Genetics and Genomics
         Cell Biology
      image:
         https://media.springernature.com/lw1200/springer-static/image/art%3A10.1007%2Fs00239-004-2597-8/MediaObjects/fig1.jpg
         https://media.springernature.com/lw1200/springer-static/image/art%3A10.1007%2Fs00239-004-2597-8/MediaObjects/fig2.jpg
         https://media.springernature.com/lw1200/springer-static/image/art%3A10.1007%2Fs00239-004-2597-8/MediaObjects/fig3.jpg
      isPartOf:
         name:Journal of Molecular Evolution
         issn:
            1432-1432
            0022-2844
         volumeNumber:59
         type:
            Periodical
            PublicationVolume
      publisher:
         name:Springer-Verlag
         logo:
            url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
            type:ImageObject
         type:Organization
      author:
            name:Joseph P. Bielawski
            affiliation:
                  name:University College London
                  address:
                     name:Department of Biology, University College London, London, UK
                     type:PostalAddress
                  type:Organization
                  name:Dalhousie University
                  address:
                     name:Department of Biology, Dalhousie University, Halifax, Canada
                     type:PostalAddress
                  type:Organization
            email:[email protected]
            type:Person
            name:Ziheng Yang
            affiliation:
                  name:University College London
                  address:
                     name:Department of Biology, University College London, London, UK
                     type:PostalAddress
                  type:Organization
            type:Person
      isAccessibleForFree:
      hasPart:
         isAccessibleForFree:
         cssSelector:.main-content
         type:WebPageElement
["Periodical","PublicationVolume"]:
      name:Journal of Molecular Evolution
      issn:
         1432-1432
         0022-2844
      volumeNumber:59
Organization:
      name:Springer-Verlag
      logo:
         url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
         type:ImageObject
      name:University College London
      address:
         name:Department of Biology, University College London, London, UK
         type:PostalAddress
      name:Dalhousie University
      address:
         name:Department of Biology, Dalhousie University, Halifax, Canada
         type:PostalAddress
      name:University College London
      address:
         name:Department of Biology, University College London, London, UK
         type:PostalAddress
ImageObject:
      url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
Person:
      name:Joseph P. Bielawski
      affiliation:
            name:University College London
            address:
               name:Department of Biology, University College London, London, UK
               type:PostalAddress
            type:Organization
            name:Dalhousie University
            address:
               name:Department of Biology, Dalhousie University, Halifax, Canada
               type:PostalAddress
            type:Organization
      email:[email protected]
      name:Ziheng Yang
      affiliation:
            name:University College London
            address:
               name:Department of Biology, University College London, London, UK
               type:PostalAddress
            type:Organization
PostalAddress:
      name:Department of Biology, University College London, London, UK
      name:Department of Biology, Dalhousie University, Halifax, Canada
      name:Department of Biology, University College London, London, UK
WebPageElement:
      isAccessibleForFree:
      cssSelector:.main-content

External Links {🔗}(154)

Analytics and Tracking {📊}

Google Tag Manager

Libraries {📚}

Clipboard.js
Prism.js

CDN Services {📦}

Crossref

4.04s.

LINK . SPRINGER . COM {}