PopNet: A Markov Clustering Approach to Study Population Genetic Structure.
Identifieur interne : 002264 ( Ncbi/Curation ); précédent : 002263; suivant : 002265PopNet: A Markov Clustering Approach to Study Population Genetic Structure.
Auteurs : Javi Zhang [Canada] ; Asis Khan [États-Unis] ; Andrea Kennard [États-Unis] ; Michael E. Grigg [États-Unis] ; John Parkinson [Canada]Source :
- Molecular biology and evolution [ 1537-1719 ] ; 2017.
Abstract
With the advent of low cost, high-throughput genome sequencing technology, population genomic data sets are being generated for hundreds of species of pathogenic, industrial, and agricultural importance. The challenge is how best to analyze and visually display these complex data sets to yield intuitive representations capable of capturing complex evolutionary relationships. Here we present PopNet, a novel computational method that identifies regions of shared ancestry in the chromosomes of related strains through clustering patterns of genetic variation. These relationships are subsequently visualized within a network by a novel implementation of chromosome painting. We apply PopNet to three diverse populations that feature differential rates of recombination and demonstrate its ability to capture evolutionary relationships as well as associate traits to specific loci. Compared with existing tools, PopNet provides substantial advances by both removing the need to predefine a single reference genome that can bias interpretation of population structure, as well as its ability to visualize multiple evolutionary relationships, such as recombination events and shared ancestry, across hundreds of strains.
DOI: 10.1093/molbev/msx110
PubMed: 28383661
Links toward previous steps (curation, corpus...)
- to stream PubMed, to step Corpus: Pour aller vers cette notice dans l'étape Curation :000020
- to stream PubMed, to step Curation: Pour aller vers cette notice dans l'étape Curation :000020
- to stream PubMed, to step Checkpoint: Pour aller vers cette notice dans l'étape Curation :000020
- to stream Ncbi, to step Merge: Pour aller vers cette notice dans l'étape Curation :002264
Links to Exploration step
pubmed:28383661Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">PopNet: A Markov Clustering Approach to Study Population Genetic Structure.</title>
<author><name sortKey="Zhang, Javi" sort="Zhang, Javi" uniqKey="Zhang J" first="Javi" last="Zhang">Javi Zhang</name>
<affiliation wicri:level="4"><nlm:affiliation>Department of Biochemistry, University of Toronto, Toronto, ON, Canada.</nlm:affiliation>
<country xml:lang="fr">Canada</country>
<wicri:regionArea>Department of Biochemistry, University of Toronto, Toronto, ON</wicri:regionArea>
<orgName type="university">Université de Toronto</orgName>
<placeName><settlement type="city">Toronto</settlement>
<region type="state">Ontario</region>
</placeName>
</affiliation>
</author>
<author><name sortKey="Khan, Asis" sort="Khan, Asis" uniqKey="Khan A" first="Asis" last="Khan">Asis Khan</name>
<affiliation wicri:level="2"><nlm:affiliation>Molecular Parasitology Section, Laboratory of Parasitic Diseases, NIAID, National Institutes of Health, Bethesda, MD.</nlm:affiliation>
<country xml:lang="fr">États-Unis</country>
<placeName><region type="state">Maryland</region>
</placeName>
<wicri:cityArea>Molecular Parasitology Section, Laboratory of Parasitic Diseases, NIAID, National Institutes of Health, Bethesda</wicri:cityArea>
</affiliation>
</author>
<author><name sortKey="Kennard, Andrea" sort="Kennard, Andrea" uniqKey="Kennard A" first="Andrea" last="Kennard">Andrea Kennard</name>
<affiliation wicri:level="2"><nlm:affiliation>Molecular Parasitology Section, Laboratory of Parasitic Diseases, NIAID, National Institutes of Health, Bethesda, MD.</nlm:affiliation>
<country xml:lang="fr">États-Unis</country>
<placeName><region type="state">Maryland</region>
</placeName>
<wicri:cityArea>Molecular Parasitology Section, Laboratory of Parasitic Diseases, NIAID, National Institutes of Health, Bethesda</wicri:cityArea>
</affiliation>
</author>
<author><name sortKey="Grigg, Michael E" sort="Grigg, Michael E" uniqKey="Grigg M" first="Michael E" last="Grigg">Michael E. Grigg</name>
<affiliation wicri:level="2"><nlm:affiliation>Molecular Parasitology Section, Laboratory of Parasitic Diseases, NIAID, National Institutes of Health, Bethesda, MD.</nlm:affiliation>
<country xml:lang="fr">États-Unis</country>
<placeName><region type="state">Maryland</region>
</placeName>
<wicri:cityArea>Molecular Parasitology Section, Laboratory of Parasitic Diseases, NIAID, National Institutes of Health, Bethesda</wicri:cityArea>
</affiliation>
</author>
<author><name sortKey="Parkinson, John" sort="Parkinson, John" uniqKey="Parkinson J" first="John" last="Parkinson">John Parkinson</name>
<affiliation wicri:level="4"><nlm:affiliation>Department of Biochemistry, University of Toronto, Toronto, ON, Canada.</nlm:affiliation>
<country xml:lang="fr">Canada</country>
<wicri:regionArea>Department of Biochemistry, University of Toronto, Toronto, ON</wicri:regionArea>
<orgName type="university">Université de Toronto</orgName>
<placeName><settlement type="city">Toronto</settlement>
<region type="state">Ontario</region>
</placeName>
</affiliation>
</author>
</titleStmt>
<publicationStmt><idno type="wicri:source">PubMed</idno>
<date when="2017">2017</date>
<idno type="RBID">pubmed:28383661</idno>
<idno type="pmid">28383661</idno>
<idno type="doi">10.1093/molbev/msx110</idno>
<idno type="wicri:Area/PubMed/Corpus">000020</idno>
<idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000020</idno>
<idno type="wicri:Area/PubMed/Curation">000020</idno>
<idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Curation">000020</idno>
<idno type="wicri:Area/PubMed/Checkpoint">000020</idno>
<idno type="wicri:explorRef" wicri:stream="Checkpoint" wicri:step="PubMed">000020</idno>
<idno type="wicri:Area/Ncbi/Merge">002264</idno>
<idno type="wicri:Area/Ncbi/Curation">002264</idno>
</publicationStmt>
<sourceDesc><biblStruct><analytic><title xml:lang="en">PopNet: A Markov Clustering Approach to Study Population Genetic Structure.</title>
<author><name sortKey="Zhang, Javi" sort="Zhang, Javi" uniqKey="Zhang J" first="Javi" last="Zhang">Javi Zhang</name>
<affiliation wicri:level="4"><nlm:affiliation>Department of Biochemistry, University of Toronto, Toronto, ON, Canada.</nlm:affiliation>
<country xml:lang="fr">Canada</country>
<wicri:regionArea>Department of Biochemistry, University of Toronto, Toronto, ON</wicri:regionArea>
<orgName type="university">Université de Toronto</orgName>
<placeName><settlement type="city">Toronto</settlement>
<region type="state">Ontario</region>
</placeName>
</affiliation>
</author>
<author><name sortKey="Khan, Asis" sort="Khan, Asis" uniqKey="Khan A" first="Asis" last="Khan">Asis Khan</name>
<affiliation wicri:level="2"><nlm:affiliation>Molecular Parasitology Section, Laboratory of Parasitic Diseases, NIAID, National Institutes of Health, Bethesda, MD.</nlm:affiliation>
<country xml:lang="fr">États-Unis</country>
<placeName><region type="state">Maryland</region>
</placeName>
<wicri:cityArea>Molecular Parasitology Section, Laboratory of Parasitic Diseases, NIAID, National Institutes of Health, Bethesda</wicri:cityArea>
</affiliation>
</author>
<author><name sortKey="Kennard, Andrea" sort="Kennard, Andrea" uniqKey="Kennard A" first="Andrea" last="Kennard">Andrea Kennard</name>
<affiliation wicri:level="2"><nlm:affiliation>Molecular Parasitology Section, Laboratory of Parasitic Diseases, NIAID, National Institutes of Health, Bethesda, MD.</nlm:affiliation>
<country xml:lang="fr">États-Unis</country>
<placeName><region type="state">Maryland</region>
</placeName>
<wicri:cityArea>Molecular Parasitology Section, Laboratory of Parasitic Diseases, NIAID, National Institutes of Health, Bethesda</wicri:cityArea>
</affiliation>
</author>
<author><name sortKey="Grigg, Michael E" sort="Grigg, Michael E" uniqKey="Grigg M" first="Michael E" last="Grigg">Michael E. Grigg</name>
<affiliation wicri:level="2"><nlm:affiliation>Molecular Parasitology Section, Laboratory of Parasitic Diseases, NIAID, National Institutes of Health, Bethesda, MD.</nlm:affiliation>
<country xml:lang="fr">États-Unis</country>
<placeName><region type="state">Maryland</region>
</placeName>
<wicri:cityArea>Molecular Parasitology Section, Laboratory of Parasitic Diseases, NIAID, National Institutes of Health, Bethesda</wicri:cityArea>
</affiliation>
</author>
<author><name sortKey="Parkinson, John" sort="Parkinson, John" uniqKey="Parkinson J" first="John" last="Parkinson">John Parkinson</name>
<affiliation wicri:level="4"><nlm:affiliation>Department of Biochemistry, University of Toronto, Toronto, ON, Canada.</nlm:affiliation>
<country xml:lang="fr">Canada</country>
<wicri:regionArea>Department of Biochemistry, University of Toronto, Toronto, ON</wicri:regionArea>
<orgName type="university">Université de Toronto</orgName>
<placeName><settlement type="city">Toronto</settlement>
<region type="state">Ontario</region>
</placeName>
</affiliation>
</author>
</analytic>
<series><title level="j">Molecular biology and evolution</title>
<idno type="eISSN">1537-1719</idno>
<imprint><date when="2017" type="published">2017</date>
</imprint>
</series>
</biblStruct>
</sourceDesc>
</fileDesc>
<profileDesc><textClass></textClass>
</profileDesc>
</teiHeader>
<front><div type="abstract" xml:lang="en">With the advent of low cost, high-throughput genome sequencing technology, population genomic data sets are being generated for hundreds of species of pathogenic, industrial, and agricultural importance. The challenge is how best to analyze and visually display these complex data sets to yield intuitive representations capable of capturing complex evolutionary relationships. Here we present PopNet, a novel computational method that identifies regions of shared ancestry in the chromosomes of related strains through clustering patterns of genetic variation. These relationships are subsequently visualized within a network by a novel implementation of chromosome painting. We apply PopNet to three diverse populations that feature differential rates of recombination and demonstrate its ability to capture evolutionary relationships as well as associate traits to specific loci. Compared with existing tools, PopNet provides substantial advances by both removing the need to predefine a single reference genome that can bias interpretation of population structure, as well as its ability to visualize multiple evolutionary relationships, such as recombination events and shared ancestry, across hundreds of strains.</div>
</front>
</TEI>
</record>
Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Wicri/Canada/explor/ParkinsonCanadaV1/Data/Ncbi/Curation
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 002264 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/Ncbi/Curation/biblio.hfd -nk 002264 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien |wiki= Wicri/Canada |area= ParkinsonCanadaV1 |flux= Ncbi |étape= Curation |type= RBID |clé= pubmed:28383661 |texte= PopNet: A Markov Clustering Approach to Study Population Genetic Structure. }}
Pour générer des pages wiki
HfdIndexSelect -h $EXPLOR_AREA/Data/Ncbi/Curation/RBID.i -Sk "pubmed:28383661" \ | HfdSelect -Kh $EXPLOR_AREA/Data/Ncbi/Curation/biblio.hfd \ | NlmPubMed2Wicri -a ParkinsonCanadaV1
This area was generated with Dilib version V0.6.29. |