Benchmark of software tools for prokaryotic chromosomal interaction domain identification.
Identifieur interne : 000044 ( Main/Exploration ); précédent : 000043; suivant : 000045Benchmark of software tools for prokaryotic chromosomal interaction domain identification.
Auteurs : Mikhail D. Magnitov [Russie] ; Veronika S. Kuznetsova [Russie] ; Sergey V. Ulianov [Russie] ; Sergey V. Razin [Russie] ; Alexander V. TyakhtSource :
- Bioinformatics (Oxford, England) [ 1367-4811 ] ; 2020.
Abstract
MOTIVATION
The application of genome-wide chromosome conformation capture (3C) methods to prokaryotes provided insights into the spatial organization of their genomes and identified patterns conserved across the tree of life, such as chromatin compartments and contact domains. Prokaryotic genomes vary in GC content and the density of restriction sites along the chromosome, suggesting that these properties should be considered when planning experiments and choosing appropriate software for data processing. Diverse algorithms are available for the analysis of eukaryotic chromatin contact maps, but their potential application to prokaryotic data has not yet been evaluated.
RESULTS
Here, we present a comparative analysis of domain calling algorithms using available single-microbe experimental data. We evaluated the algorithms' intra-dataset reproducibility, concordance with other tools and sensitivity to coverage and resolution of contact maps. Using RNA-seq as an example, we showed how orthogonal biological data can be utilized to validate the reliability and significance of annotated domains. We also suggest that in silico simulations of contact maps can be used to choose optimal restriction enzymes and estimate theoretical map resolutions before the experiment. Our results provide guidelines for researchers investigating microbes and microbial communities using high-throughput 3C assays such as Hi-C and 3C-seq.
AVAILABILITY AND IMPLEMENTATION
The code of the analysis is available at https://github.com/magnitov/prokaryotic_cids.
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
DOI: 10.1093/bioinformatics/btaa555
PubMed: 32492116
PubMed Central: PMC7653553
Affiliations:
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Magnitov</name><affiliation><nlm:affiliation>Center for Precision Genome Editing and Genetic Technologies for Biomedicine.</nlm:affiliation><wicri:noCountry code="no comma">Center for Precision Genome Editing and Genetic Technologies for Biomedicine.</wicri:noCountry></affiliation><affiliation wicri:level="1"><nlm:affiliation>Group of Genome Spatial Organization, Institute of Gene Biology, Russian Academy of Sciences, Moscow 119334, Russia.</nlm:affiliation><country xml:lang="fr">Russie</country><wicri:regionArea>Group of Genome Spatial Organization, Institute of Gene Biology, Russian Academy of Sciences, Moscow 119334</wicri:regionArea><placeName><settlement type="city">Moscou</settlement><region>District fédéral central</region></placeName></affiliation><affiliation wicri:level="1"><nlm:affiliation>Department of Biological and Medical Physics, Moscow Institute of Physics and Technology (National Research University), Dolgoprudny 141700, Russia.</nlm:affiliation><country xml:lang="fr">Russie</country><wicri:regionArea>Department of Biological and Medical Physics, Moscow Institute of Physics and Technology (National Research University), Dolgoprudny 141700</wicri:regionArea><wicri:noRegion>Dolgoprudny 141700</wicri:noRegion></affiliation></author><author><name sortKey="Kuznetsova, Veronika S" sort="Kuznetsova, Veronika S" uniqKey="Kuznetsova V" first="Veronika S" last="Kuznetsova">Veronika S. Kuznetsova</name><affiliation wicri:level="1"><nlm:affiliation>Department of Biological and Medical Physics, Moscow Institute of Physics and Technology (National Research University), Dolgoprudny 141700, Russia.</nlm:affiliation><country xml:lang="fr">Russie</country><wicri:regionArea>Department of Biological and Medical Physics, Moscow Institute of Physics and Technology (National Research University), Dolgoprudny 141700</wicri:regionArea><wicri:noRegion>Dolgoprudny 141700</wicri:noRegion></affiliation><affiliation><nlm:affiliation>Group of Bioinformatics.</nlm:affiliation><wicri:noCountry code="no comma">Group of Bioinformatics.</wicri:noCountry></affiliation></author><author><name sortKey="Ulianov, Sergey V" sort="Ulianov, Sergey V" uniqKey="Ulianov S" first="Sergey V" last="Ulianov">Sergey V. Ulianov</name><affiliation wicri:level="1"><nlm:affiliation>Laboratory of Structural and Functional Organization of Chromosomes, Institute of Gene Biology, Russian Academy of Sciences, Moscow 119334, Russia.</nlm:affiliation><country xml:lang="fr">Russie</country><wicri:regionArea>Laboratory of Structural and Functional Organization of Chromosomes, Institute of Gene Biology, Russian Academy of Sciences, Moscow 119334</wicri:regionArea><placeName><settlement type="city">Moscou</settlement><region>District fédéral central</region></placeName></affiliation><affiliation wicri:level="4"><nlm:affiliation>Department of Biology, Moscow State University, Moscow 119234, Russia.</nlm:affiliation><country xml:lang="fr">Russie</country><wicri:regionArea>Department of Biology, Moscow State University, Moscow 119234</wicri:regionArea><placeName><settlement type="city">Moscou</settlement><region>District fédéral central</region></placeName><orgName type="university">Université d'État de Moscou</orgName><placeName><settlement type="city">Moscou</settlement><region>District fédéral central</region></placeName></affiliation></author><author><name sortKey="Razin, Sergey V" sort="Razin, Sergey V" uniqKey="Razin S" first="Sergey V" last="Razin">Sergey V. Razin</name><affiliation wicri:level="1"><nlm:affiliation>Laboratory of Structural and Functional Organization of Chromosomes, Institute of Gene Biology, Russian Academy of Sciences, Moscow 119334, Russia.</nlm:affiliation><country xml:lang="fr">Russie</country><wicri:regionArea>Laboratory of Structural and Functional Organization of Chromosomes, Institute of Gene Biology, Russian Academy of Sciences, Moscow 119334</wicri:regionArea><placeName><settlement type="city">Moscou</settlement><region>District fédéral central</region></placeName></affiliation><affiliation wicri:level="4"><nlm:affiliation>Department of Biology, Moscow State University, Moscow 119234, Russia.</nlm:affiliation><country xml:lang="fr">Russie</country><wicri:regionArea>Department of Biology, Moscow State University, Moscow 119234</wicri:regionArea><placeName><settlement type="city">Moscou</settlement><region>District fédéral central</region></placeName><orgName type="university">Université d'État de Moscou</orgName><placeName><settlement type="city">Moscou</settlement><region>District fédéral central</region></placeName></affiliation></author><author><name sortKey="Tyakht, Alexander V" sort="Tyakht, Alexander V" uniqKey="Tyakht A" first="Alexander V" last="Tyakht">Alexander V. Tyakht</name><affiliation><nlm:affiliation>Center for Precision Genome Editing and Genetic Technologies for Biomedicine.</nlm:affiliation><wicri:noCountry code="no comma">Center for Precision Genome Editing and Genetic Technologies for Biomedicine.</wicri:noCountry></affiliation><affiliation><nlm:affiliation>Group of Bioinformatics.</nlm:affiliation><wicri:noCountry code="no comma">Group of Bioinformatics.</wicri:noCountry></affiliation></author></analytic><series><title level="j">Bioinformatics (Oxford, England)</title><idno type="eISSN">1367-4811</idno><imprint><date when="2020" type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p><b>MOTIVATION</b></p><p>The application of genome-wide chromosome conformation capture (3C) methods to prokaryotes provided insights into the spatial organization of their genomes and identified patterns conserved across the tree of life, such as chromatin compartments and contact domains. Prokaryotic genomes vary in GC content and the density of restriction sites along the chromosome, suggesting that these properties should be considered when planning experiments and choosing appropriate software for data processing. Diverse algorithms are available for the analysis of eukaryotic chromatin contact maps, but their potential application to prokaryotic data has not yet been evaluated.</p></div><div type="abstract" xml:lang="en"><p><b>RESULTS</b></p><p>Here, we present a comparative analysis of domain calling algorithms using available single-microbe experimental data. We evaluated the algorithms' intra-dataset reproducibility, concordance with other tools and sensitivity to coverage and resolution of contact maps. Using RNA-seq as an example, we showed how orthogonal biological data can be utilized to validate the reliability and significance of annotated domains. We also suggest that in silico simulations of contact maps can be used to choose optimal restriction enzymes and estimate theoretical map resolutions before the experiment. Our results provide guidelines for researchers investigating microbes and microbial communities using high-throughput 3C assays such as Hi-C and 3C-seq.</p></div><div type="abstract" xml:lang="en"><p><b>AVAILABILITY AND IMPLEMENTATION</b></p><p>The code of the analysis is available at https://github.com/magnitov/prokaryotic_cids.</p></div><div type="abstract" xml:lang="en"><p><b>SUPPLEMENTARY INFORMATION</b></p><p>Supplementary data are available at Bioinformatics online.</p></div></front></TEI><pubmed><MedlineCitation Status="In-Data-Review" Owner="NLM"><PMID Version="1">32492116</PMID><DateRevised><Year>2020</Year><Month>11</Month><Day>17</Day></DateRevised><Article PubModel="Print"><Journal><ISSN IssnType="Electronic">1367-4811</ISSN><JournalIssue CitedMedium="Internet"><Volume>36</Volume><Issue>17</Issue><PubDate><Year>2020</Year><Month>Nov</Month><Day>01</Day></PubDate></JournalIssue><Title>Bioinformatics (Oxford, England)</Title><ISOAbbreviation>Bioinformatics</ISOAbbreviation></Journal><ArticleTitle>Benchmark of software tools for prokaryotic chromosomal interaction domain identification.</ArticleTitle><Pagination><MedlinePgn>4560-4567</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1093/bioinformatics/btaa555</ELocationID><Abstract><AbstractText Label="MOTIVATION" NlmCategory="BACKGROUND">The application of genome-wide chromosome conformation capture (3C) methods to prokaryotes provided insights into the spatial organization of their genomes and identified patterns conserved across the tree of life, such as chromatin compartments and contact domains. Prokaryotic genomes vary in GC content and the density of restriction sites along the chromosome, suggesting that these properties should be considered when planning experiments and choosing appropriate software for data processing. Diverse algorithms are available for the analysis of eukaryotic chromatin contact maps, but their potential application to prokaryotic data has not yet been evaluated.</AbstractText><AbstractText Label="RESULTS" NlmCategory="RESULTS">Here, we present a comparative analysis of domain calling algorithms using available single-microbe experimental data. We evaluated the algorithms' intra-dataset reproducibility, concordance with other tools and sensitivity to coverage and resolution of contact maps. Using RNA-seq as an example, we showed how orthogonal biological data can be utilized to validate the reliability and significance of annotated domains. We also suggest that in silico simulations of contact maps can be used to choose optimal restriction enzymes and estimate theoretical map resolutions before the experiment. Our results provide guidelines for researchers investigating microbes and microbial communities using high-throughput 3C assays such as Hi-C and 3C-seq.</AbstractText><AbstractText Label="AVAILABILITY AND IMPLEMENTATION" NlmCategory="METHODS">The code of the analysis is available at https://github.com/magnitov/prokaryotic_cids.</AbstractText><AbstractText Label="SUPPLEMENTARY INFORMATION" NlmCategory="BACKGROUND">Supplementary data are available at Bioinformatics online.</AbstractText><CopyrightInformation>© The Author(s) 2020. Published by Oxford University Press.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Magnitov</LastName><ForeName>Mikhail D</ForeName><Initials>MD</Initials><AffiliationInfo><Affiliation>Center for Precision Genome Editing and Genetic Technologies for Biomedicine.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Group of Genome Spatial Organization, Institute of Gene Biology, Russian Academy of Sciences, Moscow 119334, Russia.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Department of Biological and Medical Physics, Moscow Institute of Physics and Technology (National Research University), Dolgoprudny 141700, Russia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Kuznetsova</LastName><ForeName>Veronika S</ForeName><Initials>VS</Initials><AffiliationInfo><Affiliation>Department of Biological and Medical Physics, Moscow Institute of Physics and Technology (National Research University), Dolgoprudny 141700, Russia.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Group of Bioinformatics.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Ulianov</LastName><ForeName>Sergey V</ForeName><Initials>SV</Initials><AffiliationInfo><Affiliation>Laboratory of Structural and Functional Organization of Chromosomes, Institute of Gene Biology, Russian Academy of Sciences, Moscow 119334, Russia.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Department of Biology, Moscow State University, Moscow 119234, Russia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Razin</LastName><ForeName>Sergey V</ForeName><Initials>SV</Initials><AffiliationInfo><Affiliation>Laboratory of Structural and Functional Organization of Chromosomes, Institute of Gene Biology, Russian Academy of Sciences, Moscow 119334, Russia.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Department of Biology, Moscow State University, Moscow 119234, Russia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Tyakht</LastName><ForeName>Alexander V</ForeName><Initials>AV</Initials><AffiliationInfo><Affiliation>Center for Precision Genome Editing and Genetic Technologies for Biomedicine.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Group of Bioinformatics.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Bioinformatics</MedlineTA><NlmUniqueID>9808944</NlmUniqueID><ISSNLinking>1367-4803</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2020</Year><Month>03</Month><Day>21</Day></PubMedPubDate><PubMedPubDate 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Tyakht</name></noCountry><country name="Russie"><region name="District fédéral central"><name sortKey="Magnitov, Mikhail D" sort="Magnitov, Mikhail D" uniqKey="Magnitov M" first="Mikhail D" last="Magnitov">Mikhail D. Magnitov</name></region><name sortKey="Kuznetsova, Veronika S" sort="Kuznetsova, Veronika S" uniqKey="Kuznetsova V" first="Veronika S" last="Kuznetsova">Veronika S. Kuznetsova</name><name sortKey="Magnitov, Mikhail D" sort="Magnitov, Mikhail D" uniqKey="Magnitov M" first="Mikhail D" last="Magnitov">Mikhail D. Magnitov</name><name sortKey="Razin, Sergey V" sort="Razin, Sergey V" uniqKey="Razin S" first="Sergey V" last="Razin">Sergey V. Razin</name><name sortKey="Razin, Sergey V" sort="Razin, Sergey V" uniqKey="Razin S" first="Sergey V" last="Razin">Sergey V. Razin</name><name sortKey="Ulianov, Sergey V" sort="Ulianov, Sergey V" uniqKey="Ulianov S" first="Sergey V" last="Ulianov">Sergey V. Ulianov</name><name sortKey="Ulianov, Sergey V" sort="Ulianov, Sergey V" uniqKey="Ulianov S" first="Sergey V" last="Ulianov">Sergey V. Ulianov</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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