Phage–bacteria relationships and CRISPR elements revealed by a metagenomic survey of the rumen microbiome
Identifieur interne : 000637 ( Istex/Corpus ); précédent : 000636; suivant : 000638Phage–bacteria relationships and CRISPR elements revealed by a metagenomic survey of the rumen microbiome
Auteurs : Margret E. Berg Miller ; Carl J. Yeoman ; Nicholas Chia ; Susannah G. Tringe ; Florent E. Angly ; Robert A. Edwards ; Harry J. Flint ; Raphael Lamed ; Edward A. Bayer ; Bryan A. WhiteSource :
- Environmental Microbiology [ 1462-2912 ] ; 2012-01.
Abstract
Viruses are the most abundant biological entities on the planet and play an important role in balancing microbes within an ecosystem and facilitating horizontal gene transfer. Although bacteriophages are abundant in rumen environments, little is known about the types of viruses present or their interaction with the rumen microbiome. We undertook random pyrosequencing of virus‐enriched metagenomes (viromes) isolated from bovine rumen fluid and analysed the resulting data using comparative metagenomics. A high level of diversity was observed with up to 28 000 different viral genotypes obtained from each environment. The majority (∼78%) of sequences did not match any previously described virus. Prophages outnumbered lytic phages approximately 2:1 with the most abundant bacteriophage and prophage types being associated with members of the dominant rumen phyla (Firmicutes and Proteobacteria). Metabolic profiling based on SEED subsystems revealed an enrichment of sequences with putative functional roles in DNA and protein metabolism, but a surprisingly low proportion of sequences assigned to carbohydrate and amino acid metabolism. We expanded our analysis to include previously described metagenomic data and 14 reference genomes. Clustered regularly interspaced short palindromic repeats (CRISPR) were detected in most of the microbial genomes, suggesting previous interactions between viral and microbial communities.
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DOI: 10.1111/j.1462-2920.2011.02593.x
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We expanded our analysis to include previously described metagenomic data and 14 reference genomes. Clustered regularly interspaced short palindromic repeats (CRISPR) were detected in most of the microbial genomes, suggesting previous interactions between viral and microbial communities.</div></front></TEI><istex><corpusName>wiley</corpusName><author><json:item><name>Margret E. Berg Miller</name><affiliations><json:string>Institute for Genomic Biology, University of Illinois at Urbana‐Champaign, 1206 W. Gregory Dr, Urbana, IL 61801, USA.</json:string></affiliations></json:item><json:item><name>Carl J. Yeoman</name><affiliations><json:string>Institute for Genomic Biology, University of Illinois at Urbana‐Champaign, 1206 W. Gregory Dr, Urbana, IL 61801, USA.</json:string></affiliations></json:item><json:item><name>Nicholas Chia</name><affiliations><json:string>Institute for Genomic Biology, University of Illinois at Urbana‐Champaign, 1206 W. 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Prophages outnumbered lytic phages approximately 2:1 with the most abundant bacteriophage and prophage types being associated with members of the dominant rumen phyla (Firmicutes and Proteobacteria). Metabolic profiling based on SEED subsystems revealed an enrichment of sequences with putative functional roles in DNA and protein metabolism, but a surprisingly low proportion of sequences assigned to carbohydrate and amino acid metabolism. We expanded our analysis to include previously described metagenomic data and 14 reference genomes. 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(+1) 217 333 2091; Fax (+1) 217 333 1884.</p></note><affiliation>Institute for Genomic Biology, University of Illinois at Urbana‐Champaign, 1206 W. Gregory Dr, Urbana, IL 61801, USA.</affiliation></author></analytic><monogr><title level="j">Environmental Microbiology</title><idno type="pISSN">1462-2912</idno><idno type="eISSN">1462-2920</idno><idno type="DOI">10.1111/(ISSN)1462-2920</idno><imprint><publisher>Blackwell Publishing Ltd</publisher><pubPlace>Oxford, UK</pubPlace><date type="published" when="2012-01"></date><biblScope unit="volume">14</biblScope><biblScope unit="issue">1</biblScope><biblScope unit="page" from="207">207</biblScope><biblScope unit="page" to="227">227</biblScope></imprint></monogr><idno type="istex">08705BF8F57E6F36D4BE299E678D547AB1D3A814</idno><idno type="DOI">10.1111/j.1462-2920.2011.02593.x</idno><idno type="ArticleID">EMI2593</idno></biblStruct></sourceDesc></fileDesc><profileDesc><creation><date>2012</date></creation><langUsage><language ident="en">en</language></langUsage><abstract xml:lang="en"><p>Viruses are the most abundant biological entities on the planet and play an important role in balancing microbes within an ecosystem and facilitating horizontal gene transfer. Although bacteriophages are abundant in rumen environments, little is known about the types of viruses present or their interaction with the rumen microbiome. We undertook random pyrosequencing of virus‐enriched metagenomes (viromes) isolated from bovine rumen fluid and analysed the resulting data using comparative metagenomics. A high level of diversity was observed with up to 28 000 different viral genotypes obtained from each environment. The majority (∼78%) of sequences did not match any previously described virus. Prophages outnumbered lytic phages approximately 2:1 with the most abundant bacteriophage and prophage types being associated with members of the dominant rumen phyla (Firmicutes and Proteobacteria). Metabolic profiling based on SEED subsystems revealed an enrichment of sequences with putative functional roles in DNA and protein metabolism, but a surprisingly low proportion of sequences assigned to carbohydrate and amino acid metabolism. We expanded our analysis to include previously described metagenomic data and 14 reference genomes. Clustered regularly interspaced short palindromic repeats (CRISPR) were detected in most of the microbial genomes, suggesting previous interactions between viral and microbial communities.</p></abstract></profileDesc><revisionDesc><change when="2012-01">Published</change></revisionDesc></teiHeader></istex:fulltextTEI><json:item><original>false</original><mimetype>text/plain</mimetype><extension>txt</extension><uri>https://api.istex.fr/document/08705BF8F57E6F36D4BE299E678D547AB1D3A814/fulltext/txt</uri></json:item></fulltext><metadata><istex:metadataXml wicri:clean="Wiley, elements deleted: body"><istex:xmlDeclaration>version="1.0" encoding="UTF-8" standalone="yes"</istex:xmlDeclaration><istex:document><component version="2.0" type="serialArticle" xml:lang="en"><header><publicationMeta level="product"><publisherInfo><publisherName>Blackwell Publishing Ltd</publisherName><publisherLoc>Oxford, UK</publisherLoc></publisherInfo><doi origin="wiley" registered="yes">10.1111/(ISSN)1462-2920</doi><issn type="print">1462-2912</issn><issn type="electronic">1462-2920</issn><idGroup><id type="product" value="EMI"></id><id type="publisherDivision" value="ST"></id></idGroup><titleGroup><title type="main" sort="ENVIRONMENTAL MICROBIOLOGY">Environmental Microbiology</title></titleGroup></publicationMeta><publicationMeta level="part" position="01101"><doi origin="wiley">10.1111/emi.2012.14.issue-1</doi><titleGroup><title type="specialIssueTitle">OMICS Driven Microbial Ecology</title></titleGroup><numberingGroup><numbering type="journalVolume" number="14">14</numbering><numbering type="journalIssue">1</numbering></numberingGroup><coverDate startDate="2012-01">January 2012</coverDate></publicationMeta><publicationMeta level="unit" type="article" position="17" status="forIssue"><doi origin="wiley">10.1111/j.1462-2920.2011.02593.x</doi><idGroup><id type="unit" value="EMI2593"></id></idGroup><countGroup><count type="pageTotal" number="21"></count></countGroup><titleGroup><title type="tocHeading1">Research articles</title></titleGroup><copyright>© 2011 Society for Applied Microbiology and Blackwell Publishing Ltd</copyright><eventGroup><event type="xmlConverted" agent="Converter:BPG_TO_WML3G version:3.1.5 mode:FullText" date="2012-06-13"></event><event type="publishedOnlineEarlyUnpaginated" date="2011-10-17"></event><event type="publishedOnlineFinalForm" date="2012-01-02"></event><event type="firstOnline" date="2011-10-17"></event><event type="xmlConverted" agent="Converter:WILEY_ML3G_TO_WILEY_ML3GV2 version:3.8.8" date="2014-01-24"></event><event type="xmlConverted" agent="Converter:WML3G_To_WML3G version:4.3.4 mode:FullText" date="2015-02-25"></event></eventGroup><numberingGroup><numbering type="pageFirst" number="207">207</numbering><numbering type="pageLast" number="227">227</numbering></numberingGroup><correspondenceTo> E‐mail <email>bwhite44@illinois.edu</email>; Tel. (+1) 217 333 2091; Fax (+1) 217 333 1884. </correspondenceTo><linkGroup><link type="toTypesetVersion" href="file:EMI.EMI2593.pdf"></link></linkGroup></publicationMeta><contentMeta><unparsedEditorialHistory>Received 11 May, 2011; accepted 17 August, 2011.</unparsedEditorialHistory><countGroup><count type="figureTotal" number="6"></count><count type="tableTotal" number="5"></count><count type="formulaTotal" number="0"></count><count type="referenceTotal" number="66"></count><count type="wordTotal" number="13151"></count><count type="linksPubMed" number="0"></count><count type="linksCrossRef" number="0"></count></countGroup><titleGroup><title type="main">Phage–bacteria relationships and CRISPR elements revealed by a metagenomic survey of the rumen microbiome</title><title type="shortAuthors">M. E. Berg Miller <i>et al</i>.</title><title type="short">Rumen viral metagenome</title></titleGroup><creators><creator creatorRole="author" xml:id="cr1" affiliationRef="#a1"><personName><givenNames>Margret E.</givenNames><familyName>Berg Miller</familyName></personName></creator><creator creatorRole="author" xml:id="cr2" affiliationRef="#a1"><personName><givenNames>Carl J.</givenNames><familyName>Yeoman</familyName></personName></creator><creator creatorRole="author" xml:id="cr3" affiliationRef="#a1 #a2"><personName><givenNames>Nicholas</givenNames><familyName>Chia</familyName></personName></creator><creator creatorRole="author" xml:id="cr4" affiliationRef="#a3"><personName><givenNames>Susannah G.</givenNames><familyName>Tringe</familyName></personName></creator><creator creatorRole="author" xml:id="cr5" affiliationRef="#a4"><personName><givenNames>Florent E.</givenNames><familyName>Angly</familyName></personName></creator><creator creatorRole="author" xml:id="cr6" affiliationRef="#a5"><personName><givenNames>Robert A.</givenNames><familyName>Edwards</familyName></personName></creator><creator creatorRole="author" xml:id="cr7" affiliationRef="#a6"><personName><givenNames>Harry J.</givenNames><familyName>Flint</familyName></personName></creator><creator creatorRole="author" xml:id="cr8" affiliationRef="#a7"><personName><givenNames>Raphael</givenNames><familyName>Lamed</familyName></personName></creator><creator creatorRole="author" xml:id="cr9" affiliationRef="#a8"><personName><givenNames>Edward A.</givenNames><familyName>Bayer</familyName></personName></creator><creator creatorRole="author" xml:id="cr10" affiliationRef="#a1" corresponding="yes"><personName><givenNames>Bryan A.</givenNames><familyName>White</familyName></personName></creator></creators><affiliationGroup><affiliation xml:id="a1" countryCode="US"><unparsedAffiliation>Institute for Genomic Biology, University of Illinois at Urbana‐Champaign, 1206 W. Gregory Dr, Urbana, IL 61801, USA.</unparsedAffiliation></affiliation><affiliation xml:id="a2" countryCode="US"><unparsedAffiliation>Department of Physics, University of Illinois at Urbana‐Champaign, 1110 West Green St., Urbana, IL 61801, USA.</unparsedAffiliation></affiliation><affiliation xml:id="a3"><unparsedAffiliation>DOE Joint Genome Institute, 2800 Mitchell Drive, Walnut Creek, CA 94598, USA.</unparsedAffiliation></affiliation><affiliation xml:id="a4" countryCode="AU"><unparsedAffiliation>Australian Centre for Ecogenomics, University of Queensland, St. Lucia, Brisbane, Australia.</unparsedAffiliation></affiliation><affiliation xml:id="a5"><unparsedAffiliation>Department of Computer Science, San Diego State University, 5500 Campanile Drive, San Diego, CA 92182, USA.</unparsedAffiliation></affiliation><affiliation xml:id="a6"><unparsedAffiliation>Microbial Ecology Group, Rowett Institute of Nutrition and Health, University of Aberdeen, Aberdeen, UK.</unparsedAffiliation></affiliation><affiliation xml:id="a7" countryCode="IL"><unparsedAffiliation>Department of Molecular Microbiology and Biotechnology, Tel Aviv University, Ramat Aviv, Israel.</unparsedAffiliation></affiliation><affiliation xml:id="a8" countryCode="IL"><unparsedAffiliation>Department of Biological Chemistry, The Weizmann Institute of Science, Rehovot, Israel</unparsedAffiliation></affiliation></affiliationGroup><supportingInformation><p><b>Fig. S1.</b> Monte Carlo simulations for cross‐contigs between rumen viromes. The per cent shared viral genotypes and percent permuted rank abundance are plotted. The colours represent the likelihood score for a given position. The black dot on each plot represents the location of the best percent shared and percent permuted. (A) Cull‐7664 versusLact‐6993; (B) Cull‐7664 versus Dry‐7887; (C) Lact‐6993 versus Dry‐7887.</p><p><b>Fig. S2.</b> Principal component analysis carried out in MG‐RAST using normalized and centred data from the organismal or functional classifications of our rumen viromes and 10 publicly available ocean viromes. The red and green dots represent the rumen and ocean viromes respectively. A. M5NR database, <i>E</i>‐value ≤ 0.001. B. SEED subsystems, <i>E</i>‐value ≤ 1e‐5.</p><p><b>Fig. S3.</b> The distribution of significant matches of virome sequence reads from each cow to the GenBank non‐redundant (NR) database based on BLASTX sequence similarities (<i>E</i>‐value ≤ 0.001). The data were generated by the Joint Genome Institute using the MEGAN metagenome analysis software.</p><p><b>Fig. S4.</b> The distribution of significant matches of virome sequence reads from each cow to a non‐redundant viral database (NR_Viral_DB) based on a TBLASTX sequence similarities (<i>E</i>‐value < 0.001).</p><p><b>Fig. S5.</b> Venn Diagram showing the shared and unique hits to the NR_Viral_DB for three rumen viromes (TBLASTX; <i>E</i>‐value ≤ 0.001).</p><p><b>Fig. S6.</b> Distribution of sequences with similarity to the rumen viral metagenome (virome) based on TBLASTX sequence similarities of microbial reference genomes (A) and genome bins (B) to sequence reads from the rumen virome (<i>E</i>‐value ≤ 0.001). Identities of the virome sequences were determined by TBLASTX comparison (<i>E</i>‐value ≤ 0.001) to the NR_Viral_DB. Sequences classified as ‘other’ had significant similarity to a virome sequence that did not match any sequences in the NR_Viral_DB. The distribution of viruses, prophages, and other sequences seen here are a proportion of the total number of sequences from each genome that were similar to the rumen virome sequences.</p><p><b>Table S1.</b> Percentage of sequences (mean ± SD) with similarity to SEED subsystems (<i>E</i>‐value ≤ 1e‐5).</p><p><b>Table S2.</b> BLASTX comparison of the rumen virome to the Carbohydrate Active Enzyme (CAZy) database.</p><p><b>Table S3.</b> Putative mobile elements detected in rumen microbial genomes and genome bins.</p><p><b>Table S4.</b> Comparison of putative mobile elements from rumen microbial genomes and genome bins to the rumen virome (TBLASTX; <i>E</i> ≤ 0.001).</p><p><b>Table S5.</b> CRISPR‐associated (Cas) proteins detected in rumen microbial genomes and genome bins by RAST and GenBank.</p><p><b>Table S6.</b> CRISPR‐associated (Cas) proteins detected in the rumen viral and microbial metagenomes by MG‐RAST or BLASTX comparisons to the NR database.</p><p><b>Table S7.</b> Comparison of CRISPR spacer sequences from microbial reference genomes to three nucleotide databases (BLASTN; <i>E</i>‐value < 0.001).</p><p><b>Table S8.</b> CRISPR spacer sequences from the rumen metagenome predicted open reading frames (ORFs) generated by Hess and colleagues (2011) compared with three nucleotide databases (BLASTN; <i>E</i>‐value ≤ 0.001, sequence identity > 90%).</p><supportingInfoItem><mediaResource alt="supporting info item" href="urn-x:wiley:14622912:media:emi2593:EMI_2593_sm_fS1"></mediaResource><caption>Supporting info item</caption></supportingInfoItem><supportingInfoItem><mediaResource alt="supporting info item" href="urn-x:wiley:14622912:media:emi2593:EMI_2593_sm_fS2"></mediaResource><caption>Supporting info item</caption></supportingInfoItem><supportingInfoItem><mediaResource alt="supporting info item" href="urn-x:wiley:14622912:media:emi2593:EMI_2593_sm_fS3"></mediaResource><caption>Supporting info item</caption></supportingInfoItem><supportingInfoItem><mediaResource alt="supporting info item" href="urn-x:wiley:14622912:media:emi2593:EMI_2593_sm_fS4"></mediaResource><caption>Supporting info item</caption></supportingInfoItem><supportingInfoItem><mediaResource alt="supporting info item" href="urn-x:wiley:14622912:media:emi2593:EMI_2593_sm_fS5"></mediaResource><caption>Supporting info item</caption></supportingInfoItem><supportingInfoItem><mediaResource alt="supporting info item" href="urn-x:wiley:14622912:media:emi2593:EMI_2593_sm_fS6"></mediaResource><caption>Supporting info item</caption></supportingInfoItem><supportingInfoItem><mediaResource alt="supporting info item" href="urn-x:wiley:14622912:media:emi2593:EMI_2593_sm_tS1-8"></mediaResource><caption>Supporting info item</caption></supportingInfoItem></supportingInformation><abstractGroup><abstract type="main" xml:lang="en"><title type="main">Summary</title><p>Viruses are the most abundant biological entities on the planet and play an important role in balancing microbes within an ecosystem and facilitating horizontal gene transfer. Although bacteriophages are abundant in rumen environments, little is known about the types of viruses present or their interaction with the rumen microbiome. We undertook random pyrosequencing of virus‐enriched metagenomes (viromes) isolated from bovine rumen fluid and analysed the resulting data using comparative metagenomics. A high level of diversity was observed with up to 28 000 different viral genotypes obtained from each environment. The majority (∼78%) of sequences did not match any previously described virus. Prophages outnumbered lytic phages approximately 2:1 with the most abundant bacteriophage and prophage types being associated with members of the dominant rumen phyla (<i>Firmicutes</i> and <i>Proteobacteria</i>). Metabolic profiling based on SEED subsystems revealed an enrichment of sequences with putative functional roles in DNA and protein metabolism, but a surprisingly low proportion of sequences assigned to carbohydrate and amino acid metabolism. We expanded our analysis to include previously described metagenomic data and 14 reference genomes. Clustered regularly interspaced short palindromic repeats (CRISPR) were detected in most of the microbial genomes, suggesting previous interactions between viral and microbial communities.</p></abstract></abstractGroup></contentMeta></header></component></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Phage–bacteria relationships and CRISPR elements revealed by a metagenomic survey of the rumen microbiome</title></titleInfo><titleInfo type="abbreviated" lang="en"><title>Rumen viral metagenome</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Phage–bacteria relationships and CRISPR elements revealed by a metagenomic survey of the rumen microbiome</title></titleInfo><name type="personal"><namePart type="given">Margret E.</namePart><namePart type="family">Berg Miller</namePart><affiliation>Institute for Genomic Biology, University of Illinois at Urbana‐Champaign, 1206 W. Gregory Dr, Urbana, IL 61801, USA.</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Carl J.</namePart><namePart type="family">Yeoman</namePart><affiliation>Institute for Genomic Biology, University of Illinois at Urbana‐Champaign, 1206 W. Gregory Dr, Urbana, IL 61801, USA.</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Nicholas</namePart><namePart type="family">Chia</namePart><affiliation>Institute for Genomic Biology, University of Illinois at Urbana‐Champaign, 1206 W. Gregory Dr, Urbana, IL 61801, USA.</affiliation><affiliation>Department of Physics, University of Illinois at Urbana‐Champaign, 1110 West Green St., Urbana, IL 61801, USA.</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Susannah G.</namePart><namePart type="family">Tringe</namePart><affiliation>DOE Joint Genome Institute, 2800 Mitchell Drive, Walnut Creek, CA 94598, USA.</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Florent E.</namePart><namePart type="family">Angly</namePart><affiliation>Australian Centre for Ecogenomics, University of Queensland, St. Lucia, Brisbane, Australia.</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Robert A.</namePart><namePart type="family">Edwards</namePart><affiliation>Department of Computer Science, San Diego State University, 5500 Campanile Drive, San Diego, CA 92182, USA.</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Harry J.</namePart><namePart type="family">Flint</namePart><affiliation>Microbial Ecology Group, Rowett Institute of Nutrition and Health, University of Aberdeen, Aberdeen, UK.</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Raphael</namePart><namePart type="family">Lamed</namePart><affiliation>Department of Molecular Microbiology and Biotechnology, Tel Aviv University, Ramat Aviv, Israel.</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Edward A.</namePart><namePart type="family">Bayer</namePart><affiliation>Department of Biological Chemistry, The Weizmann Institute of Science, Rehovot, Israel</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Bryan A.</namePart><namePart type="family">White</namePart><affiliation>Institute for Genomic Biology, University of Illinois at Urbana‐Champaign, 1206 W. Gregory Dr, Urbana, IL 61801, USA.</affiliation><description>Correspondence: E‐mail ; Tel. (+1) 217 333 2091; Fax (+1) 217 333 1884.</description><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="article" displayLabel="article"></genre><originInfo><publisher>Blackwell Publishing Ltd</publisher><place><placeTerm type="text">Oxford, UK</placeTerm></place><dateIssued encoding="w3cdtf">2012-01</dateIssued><edition>Received 11 May, 2011; accepted 17 August, 2011.</edition><copyrightDate encoding="w3cdtf">2012</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType><extent unit="figures">6</extent><extent unit="tables">5</extent><extent unit="references">66</extent><extent unit="words">13151</extent></physicalDescription><abstract lang="en">Viruses are the most abundant biological entities on the planet and play an important role in balancing microbes within an ecosystem and facilitating horizontal gene transfer. Although bacteriophages are abundant in rumen environments, little is known about the types of viruses present or their interaction with the rumen microbiome. We undertook random pyrosequencing of virus‐enriched metagenomes (viromes) isolated from bovine rumen fluid and analysed the resulting data using comparative metagenomics. A high level of diversity was observed with up to 28 000 different viral genotypes obtained from each environment. The majority (∼78%) of sequences did not match any previously described virus. Prophages outnumbered lytic phages approximately 2:1 with the most abundant bacteriophage and prophage types being associated with members of the dominant rumen phyla (Firmicutes and Proteobacteria). Metabolic profiling based on SEED subsystems revealed an enrichment of sequences with putative functional roles in DNA and protein metabolism, but a surprisingly low proportion of sequences assigned to carbohydrate and amino acid metabolism. We expanded our analysis to include previously described metagenomic data and 14 reference genomes. Clustered regularly interspaced short palindromic repeats (CRISPR) were detected in most of the microbial genomes, suggesting previous interactions between viral and microbial communities.</abstract><relatedItem type="host"><titleInfo><title>Environmental Microbiology</title></titleInfo><genre type="journal">journal</genre><note type="content"> Fig. S1. Monte Carlo simulations for cross‐contigs between rumen viromes. The per cent shared viral genotypes and percent permuted rank abundance are plotted. The colours represent the likelihood score for a given position. The black dot on each plot represents the location of the best percent shared and percent permuted. (A) Cull‐7664 versusLact‐6993; (B) Cull‐7664 versus Dry‐7887; (C) Lact‐6993 versus Dry‐7887. Fig. S2. Principal component analysis carried out in MG‐RAST using normalized and centred data from the organismal or functional classifications of our rumen viromes and 10 publicly available ocean viromes. The red and green dots represent the rumen and ocean viromes respectively. A. M5NR database, E‐value ≤ 0.001. B. SEED subsystems, E‐value ≤ 1e‐5. Fig. S3. The distribution of significant matches of virome sequence reads from each cow to the GenBank non‐redundant (NR) database based on BLASTX sequence similarities (E‐value ≤ 0.001). The data were generated by the Joint Genome Institute using the MEGAN metagenome analysis software. Fig. S4. The distribution of significant matches of virome sequence reads from each cow to a non‐redundant viral database (NR_Viral_DB) based on a TBLASTX sequence similarities (E‐value < 0.001). Fig. S5. Venn Diagram showing the shared and unique hits to the NR_Viral_DB for three rumen viromes (TBLASTX; E‐value ≤ 0.001). Fig. S6. Distribution of sequences with similarity to the rumen viral metagenome (virome) based on TBLASTX sequence similarities of microbial reference genomes (A) and genome bins (B) to sequence reads from the rumen virome (E‐value ≤ 0.001). Identities of the virome sequences were determined by TBLASTX comparison (E‐value ≤ 0.001) to the NR_Viral_DB. Sequences classified as ‘other’ had significant similarity to a virome sequence that did not match any sequences in the NR_Viral_DB. The distribution of viruses, prophages, and other sequences seen here are a proportion of the total number of sequences from each genome that were similar to the rumen virome sequences. Table S1. Percentage of sequences (mean ± SD) with similarity to SEED subsystems (E‐value ≤ 1e‐5). Table S2. BLASTX comparison of the rumen virome to the Carbohydrate Active Enzyme (CAZy) database. Table S3. Putative mobile elements detected in rumen microbial genomes and genome bins. Table S4. Comparison of putative mobile elements from rumen microbial genomes and genome bins to the rumen virome (TBLASTX; E ≤ 0.001). Table S5. CRISPR‐associated (Cas) proteins detected in rumen microbial genomes and genome bins by RAST and GenBank. Table S6. CRISPR‐associated (Cas) proteins detected in the rumen viral and microbial metagenomes by MG‐RAST or BLASTX comparisons to the NR database. Table S7. Comparison of CRISPR spacer sequences from microbial reference genomes to three nucleotide databases (BLASTN; E‐value < 0.001). Table S8. CRISPR spacer sequences from the rumen metagenome predicted open reading frames (ORFs) generated by Hess and colleagues (2011) compared with three nucleotide databases (BLASTN; E‐value ≤ 0.001, sequence identity > 90%). Fig. S1. Monte Carlo simulations for cross‐contigs between rumen viromes. The per cent shared viral genotypes and percent permuted rank abundance are plotted. The colours represent the likelihood score for a given position. The black dot on each plot represents the location of the best percent shared and percent permuted. (A) Cull‐7664 versusLact‐6993; (B) Cull‐7664 versus Dry‐7887; (C) Lact‐6993 versus Dry‐7887. Fig. S2. Principal component analysis carried out in MG‐RAST using normalized and centred data from the organismal or functional classifications of our rumen viromes and 10 publicly available ocean viromes. The red and green dots represent the rumen and ocean viromes respectively. A. M5NR database, E‐value ≤ 0.001. B. SEED subsystems, E‐value ≤ 1e‐5. Fig. S3. The distribution of significant matches of virome sequence reads from each cow to the GenBank non‐redundant (NR) database based on BLASTX sequence similarities (E‐value ≤ 0.001). The data were generated by the Joint Genome Institute using the MEGAN metagenome analysis software. Fig. S4. The distribution of significant matches of virome sequence reads from each cow to a non‐redundant viral database (NR_Viral_DB) based on a TBLASTX sequence similarities (E‐value < 0.001). Fig. S5. Venn Diagram showing the shared and unique hits to the NR_Viral_DB for three rumen viromes (TBLASTX; E‐value ≤ 0.001). Fig. S6. Distribution of sequences with similarity to the rumen viral metagenome (virome) based on TBLASTX sequence similarities of microbial reference genomes (A) and genome bins (B) to sequence reads from the rumen virome (E‐value ≤ 0.001). Identities of the virome sequences were determined by TBLASTX comparison (E‐value ≤ 0.001) to the NR_Viral_DB. Sequences classified as ‘other’ had significant similarity to a virome sequence that did not match any sequences in the NR_Viral_DB. The distribution of viruses, prophages, and other sequences seen here are a proportion of the total number of sequences from each genome that were similar to the rumen virome sequences. Table S1. Percentage of sequences (mean ± SD) with similarity to SEED subsystems (E‐value ≤ 1e‐5). Table S2. BLASTX comparison of the rumen virome to the Carbohydrate Active Enzyme (CAZy) database. Table S3. Putative mobile elements detected in rumen microbial genomes and genome bins. Table S4. Comparison of putative mobile elements from rumen microbial genomes and genome bins to the rumen virome (TBLASTX; E ≤ 0.001). Table S5. CRISPR‐associated (Cas) proteins detected in rumen microbial genomes and genome bins by RAST and GenBank. Table S6. CRISPR‐associated (Cas) proteins detected in the rumen viral and microbial metagenomes by MG‐RAST or BLASTX comparisons to the NR database. Table S7. Comparison of CRISPR spacer sequences from microbial reference genomes to three nucleotide databases (BLASTN; E‐value < 0.001). Table S8. CRISPR spacer sequences from the rumen metagenome predicted open reading frames (ORFs) generated by Hess and colleagues (2011) compared with three nucleotide databases (BLASTN; E‐value ≤ 0.001, sequence identity > 90%). Fig. S1. Monte Carlo simulations for cross‐contigs between rumen viromes. The per cent shared viral genotypes and percent permuted rank abundance are plotted. The colours represent the likelihood score for a given position. The black dot on each plot represents the location of the best percent shared and percent permuted. (A) Cull‐7664 versusLact‐6993; (B) Cull‐7664 versus Dry‐7887; (C) Lact‐6993 versus Dry‐7887. Fig. S2. Principal component analysis carried out in MG‐RAST using normalized and centred data from the organismal or functional classifications of our rumen viromes and 10 publicly available ocean viromes. The red and green dots represent the rumen and ocean viromes respectively. A. M5NR database, E‐value ≤ 0.001. B. SEED subsystems, E‐value ≤ 1e‐5. Fig. S3. The distribution of significant matches of virome sequence reads from each cow to the GenBank non‐redundant (NR) database based on BLASTX sequence similarities (E‐value ≤ 0.001). The data were generated by the Joint Genome Institute using the MEGAN metagenome analysis software. Fig. S4. The distribution of significant matches of virome sequence reads from each cow to a non‐redundant viral database (NR_Viral_DB) based on a TBLASTX sequence similarities (E‐value < 0.001). Fig. S5. Venn Diagram showing the shared and unique hits to the NR_Viral_DB for three rumen viromes (TBLASTX; E‐value ≤ 0.001). Fig. S6. Distribution of sequences with similarity to the rumen viral metagenome (virome) based on TBLASTX sequence similarities of microbial reference genomes (A) and genome bins (B) to sequence reads from the rumen virome (E‐value ≤ 0.001). Identities of the virome sequences were determined by TBLASTX comparison (E‐value ≤ 0.001) to the NR_Viral_DB. Sequences classified as ‘other’ had significant similarity to a virome sequence that did not match any sequences in the NR_Viral_DB. The distribution of viruses, prophages, and other sequences seen here are a proportion of the total number of sequences from each genome that were similar to the rumen virome sequences. Table S1. Percentage of sequences (mean ± SD) with similarity to SEED subsystems (E‐value ≤ 1e‐5). Table S2. BLASTX comparison of the rumen virome to the Carbohydrate Active Enzyme (CAZy) database. Table S3. Putative mobile elements detected in rumen microbial genomes and genome bins. Table S4. Comparison of putative mobile elements from rumen microbial genomes and genome bins to the rumen virome (TBLASTX; E ≤ 0.001). Table S5. CRISPR‐associated (Cas) proteins detected in rumen microbial genomes and genome bins by RAST and GenBank. Table S6. CRISPR‐associated (Cas) proteins detected in the rumen viral and microbial metagenomes by MG‐RAST or BLASTX comparisons to the NR database. Table S7. Comparison of CRISPR spacer sequences from microbial reference genomes to three nucleotide databases (BLASTN; E‐value < 0.001). Table S8. CRISPR spacer sequences from the rumen metagenome predicted open reading frames (ORFs) generated by Hess and colleagues (2011) compared with three nucleotide databases (BLASTN; E‐value ≤ 0.001, sequence identity > 90%). Fig. S1. Monte Carlo simulations for cross‐contigs between rumen viromes. The per cent shared viral genotypes and percent permuted rank abundance are plotted. The colours represent the likelihood score for a given position. The black dot on each plot represents the location of the best percent shared and percent permuted. (A) Cull‐7664 versusLact‐6993; (B) Cull‐7664 versus Dry‐7887; (C) Lact‐6993 versus Dry‐7887. Fig. S2. Principal component analysis carried out in MG‐RAST using normalized and centred data from the organismal or functional classifications of our rumen viromes and 10 publicly available ocean viromes. The red and green dots represent the rumen and ocean viromes respectively. A. M5NR database, E‐value ≤ 0.001. B. SEED subsystems, E‐value ≤ 1e‐5. Fig. S3. The distribution of significant matches of virome sequence reads from each cow to the GenBank non‐redundant (NR) database based on BLASTX sequence similarities (E‐value ≤ 0.001). The data were generated by the Joint Genome Institute using the MEGAN metagenome analysis software. Fig. S4. The distribution of significant matches of virome sequence reads from each cow to a non‐redundant viral database (NR_Viral_DB) based on a TBLASTX sequence similarities (E‐value < 0.001). Fig. S5. Venn Diagram showing the shared and unique hits to the NR_Viral_DB for three rumen viromes (TBLASTX; E‐value ≤ 0.001). Fig. S6. Distribution of sequences with similarity to the rumen viral metagenome (virome) based on TBLASTX sequence similarities of microbial reference genomes (A) and genome bins (B) to sequence reads from the rumen virome (E‐value ≤ 0.001). Identities of the virome sequences were determined by TBLASTX comparison (E‐value ≤ 0.001) to the NR_Viral_DB. Sequences classified as ‘other’ had significant similarity to a virome sequence that did not match any sequences in the NR_Viral_DB. The distribution of viruses, prophages, and other sequences seen here are a proportion of the total number of sequences from each genome that were similar to the rumen virome sequences. Table S1. Percentage of sequences (mean ± SD) with similarity to SEED subsystems (E‐value ≤ 1e‐5). Table S2. BLASTX comparison of the rumen virome to the Carbohydrate Active Enzyme (CAZy) database. Table S3. Putative mobile elements detected in rumen microbial genomes and genome bins. Table S4. Comparison of putative mobile elements from rumen microbial genomes and genome bins to the rumen virome (TBLASTX; E ≤ 0.001). Table S5. CRISPR‐associated (Cas) proteins detected in rumen microbial genomes and genome bins by RAST and GenBank. Table S6. CRISPR‐associated (Cas) proteins detected in the rumen viral and microbial metagenomes by MG‐RAST or BLASTX comparisons to the NR database. Table S7. Comparison of CRISPR spacer sequences from microbial reference genomes to three nucleotide databases (BLASTN; E‐value < 0.001). Table S8. CRISPR spacer sequences from the rumen metagenome predicted open reading frames (ORFs) generated by Hess and colleagues (2011) compared with three nucleotide databases (BLASTN; E‐value ≤ 0.001, sequence identity > 90%). Fig. S1. Monte Carlo simulations for cross‐contigs between rumen viromes. The per cent shared viral genotypes and percent permuted rank abundance are plotted. The colours represent the likelihood score for a given position. The black dot on each plot represents the location of the best percent shared and percent permuted. (A) Cull‐7664 versusLact‐6993; (B) Cull‐7664 versus Dry‐7887; (C) Lact‐6993 versus Dry‐7887. Fig. S2. Principal component analysis carried out in MG‐RAST using normalized and centred data from the organismal or functional classifications of our rumen viromes and 10 publicly available ocean viromes. The red and green dots represent the rumen and ocean viromes respectively. A. M5NR database, E‐value ≤ 0.001. B. SEED subsystems, E‐value ≤ 1e‐5. Fig. S3. The distribution of significant matches of virome sequence reads from each cow to the GenBank non‐redundant (NR) database based on BLASTX sequence similarities (E‐value ≤ 0.001). The data were generated by the Joint Genome Institute using the MEGAN metagenome analysis software. Fig. S4. The distribution of significant matches of virome sequence reads from each cow to a non‐redundant viral database (NR_Viral_DB) based on a TBLASTX sequence similarities (E‐value < 0.001). Fig. S5. Venn Diagram showing the shared and unique hits to the NR_Viral_DB for three rumen viromes (TBLASTX; E‐value ≤ 0.001). Fig. S6. Distribution of sequences with similarity to the rumen viral metagenome (virome) based on TBLASTX sequence similarities of microbial reference genomes (A) and genome bins (B) to sequence reads from the rumen virome (E‐value ≤ 0.001). Identities of the virome sequences were determined by TBLASTX comparison (E‐value ≤ 0.001) to the NR_Viral_DB. Sequences classified as ‘other’ had significant similarity to a virome sequence that did not match any sequences in the NR_Viral_DB. The distribution of viruses, prophages, and other sequences seen here are a proportion of the total number of sequences from each genome that were similar to the rumen virome sequences. Table S1. Percentage of sequences (mean ± SD) with similarity to SEED subsystems (E‐value ≤ 1e‐5). Table S2. BLASTX comparison of the rumen virome to the Carbohydrate Active Enzyme (CAZy) database. Table S3. Putative mobile elements detected in rumen microbial genomes and genome bins. Table S4. Comparison of putative mobile elements from rumen microbial genomes and genome bins to the rumen virome (TBLASTX; E ≤ 0.001). Table S5. CRISPR‐associated (Cas) proteins detected in rumen microbial genomes and genome bins by RAST and GenBank. Table S6. CRISPR‐associated (Cas) proteins detected in the rumen viral and microbial metagenomes by MG‐RAST or BLASTX comparisons to the NR database. Table S7. Comparison of CRISPR spacer sequences from microbial reference genomes to three nucleotide databases (BLASTN; E‐value < 0.001). Table S8. CRISPR spacer sequences from the rumen metagenome predicted open reading frames (ORFs) generated by Hess and colleagues (2011) compared with three nucleotide databases (BLASTN; E‐value ≤ 0.001, sequence identity > 90%). Fig. S1. Monte Carlo simulations for cross‐contigs between rumen viromes. The per cent shared viral genotypes and percent permuted rank abundance are plotted. The colours represent the likelihood score for a given position. The black dot on each plot represents the location of the best percent shared and percent permuted. (A) Cull‐7664 versusLact‐6993; (B) Cull‐7664 versus Dry‐7887; (C) Lact‐6993 versus Dry‐7887. Fig. S2. Principal component analysis carried out in MG‐RAST using normalized and centred data from the organismal or functional classifications of our rumen viromes and 10 publicly available ocean viromes. The red and green dots represent the rumen and ocean viromes respectively. A. M5NR database, E‐value ≤ 0.001. B. SEED subsystems, E‐value ≤ 1e‐5. Fig. S3. The distribution of significant matches of virome sequence reads from each cow to the GenBank non‐redundant (NR) database based on BLASTX sequence similarities (E‐value ≤ 0.001). The data were generated by the Joint Genome Institute using the MEGAN metagenome analysis software. Fig. S4. The distribution of significant matches of virome sequence reads from each cow to a non‐redundant viral database (NR_Viral_DB) based on a TBLASTX sequence similarities (E‐value < 0.001). Fig. S5. Venn Diagram showing the shared and unique hits to the NR_Viral_DB for three rumen viromes (TBLASTX; E‐value ≤ 0.001). Fig. S6. Distribution of sequences with similarity to the rumen viral metagenome (virome) based on TBLASTX sequence similarities of microbial reference genomes (A) and genome bins (B) to sequence reads from the rumen virome (E‐value ≤ 0.001). Identities of the virome sequences were determined by TBLASTX comparison (E‐value ≤ 0.001) to the NR_Viral_DB. Sequences classified as ‘other’ had significant similarity to a virome sequence that did not match any sequences in the NR_Viral_DB. The distribution of viruses, prophages, and other sequences seen here are a proportion of the total number of sequences from each genome that were similar to the rumen virome sequences. Table S1. Percentage of sequences (mean ± SD) with similarity to SEED subsystems (E‐value ≤ 1e‐5). Table S2. BLASTX comparison of the rumen virome to the Carbohydrate Active Enzyme (CAZy) database. Table S3. Putative mobile elements detected in rumen microbial genomes and genome bins. Table S4. Comparison of putative mobile elements from rumen microbial genomes and genome bins to the rumen virome (TBLASTX; E ≤ 0.001). Table S5. CRISPR‐associated (Cas) proteins detected in rumen microbial genomes and genome bins by RAST and GenBank. Table S6. CRISPR‐associated (Cas) proteins detected in the rumen viral and microbial metagenomes by MG‐RAST or BLASTX comparisons to the NR database. Table S7. Comparison of CRISPR spacer sequences from microbial reference genomes to three nucleotide databases (BLASTN; E‐value < 0.001). Table S8. CRISPR spacer sequences from the rumen metagenome predicted open reading frames (ORFs) generated by Hess and colleagues (2011) compared with three nucleotide databases (BLASTN; E‐value ≤ 0.001, sequence identity > 90%). Fig. S1. Monte Carlo simulations for cross‐contigs between rumen viromes. The per cent shared viral genotypes and percent permuted rank abundance are plotted. The colours represent the likelihood score for a given position. The black dot on each plot represents the location of the best percent shared and percent permuted. (A) Cull‐7664 versusLact‐6993; (B) Cull‐7664 versus Dry‐7887; (C) Lact‐6993 versus Dry‐7887. Fig. S2. Principal component analysis carried out in MG‐RAST using normalized and centred data from the organismal or functional classifications of our rumen viromes and 10 publicly available ocean viromes. The red and green dots represent the rumen and ocean viromes respectively. A. M5NR database, E‐value ≤ 0.001. B. SEED subsystems, E‐value ≤ 1e‐5. Fig. S3. The distribution of significant matches of virome sequence reads from each cow to the GenBank non‐redundant (NR) database based on BLASTX sequence similarities (E‐value ≤ 0.001). The data were generated by the Joint Genome Institute using the MEGAN metagenome analysis software. Fig. S4. The distribution of significant matches of virome sequence reads from each cow to a non‐redundant viral database (NR_Viral_DB) based on a TBLASTX sequence similarities (E‐value < 0.001). Fig. S5. Venn Diagram showing the shared and unique hits to the NR_Viral_DB for three rumen viromes (TBLASTX; E‐value ≤ 0.001). Fig. S6. Distribution of sequences with similarity to the rumen viral metagenome (virome) based on TBLASTX sequence similarities of microbial reference genomes (A) and genome bins (B) to sequence reads from the rumen virome (E‐value ≤ 0.001). Identities of the virome sequences were determined by TBLASTX comparison (E‐value ≤ 0.001) to the NR_Viral_DB. Sequences classified as ‘other’ had significant similarity to a virome sequence that did not match any sequences in the NR_Viral_DB. The distribution of viruses, prophages, and other sequences seen here are a proportion of the total number of sequences from each genome that were similar to the rumen virome sequences. Table S1. Percentage of sequences (mean ± SD) with similarity to SEED subsystems (E‐value ≤ 1e‐5). Table S2. BLASTX comparison of the rumen virome to the Carbohydrate Active Enzyme (CAZy) database. Table S3. Putative mobile elements detected in rumen microbial genomes and genome bins. Table S4. Comparison of putative mobile elements from rumen microbial genomes and genome bins to the rumen virome (TBLASTX; E ≤ 0.001). Table S5. CRISPR‐associated (Cas) proteins detected in rumen microbial genomes and genome bins by RAST and GenBank. Table S6. CRISPR‐associated (Cas) proteins detected in the rumen viral and microbial metagenomes by MG‐RAST or BLASTX comparisons to the NR database. Table S7. Comparison of CRISPR spacer sequences from microbial reference genomes to three nucleotide databases (BLASTN; E‐value < 0.001). Table S8. CRISPR spacer sequences from the rumen metagenome predicted open reading frames (ORFs) generated by Hess and colleagues (2011) compared with three nucleotide databases (BLASTN; E‐value ≤ 0.001, sequence identity > 90%). Fig. S1. Monte Carlo simulations for cross‐contigs between rumen viromes. The per cent shared viral genotypes and percent permuted rank abundance are plotted. The colours represent the likelihood score for a given position. The black dot on each plot represents the location of the best percent shared and percent permuted. (A) Cull‐7664 versusLact‐6993; (B) Cull‐7664 versus Dry‐7887; (C) Lact‐6993 versus Dry‐7887. Fig. S2. Principal component analysis carried out in MG‐RAST using normalized and centred data from the organismal or functional classifications of our rumen viromes and 10 publicly available ocean viromes. The red and green dots represent the rumen and ocean viromes respectively. A. M5NR database, E‐value ≤ 0.001. B. SEED subsystems, E‐value ≤ 1e‐5. Fig. S3. The distribution of significant matches of virome sequence reads from each cow to the GenBank non‐redundant (NR) database based on BLASTX sequence similarities (E‐value ≤ 0.001). The data were generated by the Joint Genome Institute using the MEGAN metagenome analysis software. Fig. S4. The distribution of significant matches of virome sequence reads from each cow to a non‐redundant viral database (NR_Viral_DB) based on a TBLASTX sequence similarities (E‐value < 0.001). Fig. S5. Venn Diagram showing the shared and unique hits to the NR_Viral_DB for three rumen viromes (TBLASTX; E‐value ≤ 0.001). Fig. S6. Distribution of sequences with similarity to the rumen viral metagenome (virome) based on TBLASTX sequence similarities of microbial reference genomes (A) and genome bins (B) to sequence reads from the rumen virome (E‐value ≤ 0.001). Identities of the virome sequences were determined by TBLASTX comparison (E‐value ≤ 0.001) to the NR_Viral_DB. Sequences classified as ‘other’ had significant similarity to a virome sequence that did not match any sequences in the NR_Viral_DB. The distribution of viruses, prophages, and other sequences seen here are a proportion of the total number of sequences from each genome that were similar to the rumen virome sequences. Table S1. Percentage of sequences (mean ± SD) with similarity to SEED subsystems (E‐value ≤ 1e‐5). Table S2. BLASTX comparison of the rumen virome to the Carbohydrate Active Enzyme (CAZy) database. Table S3. Putative mobile elements detected in rumen microbial genomes and genome bins. Table S4. Comparison of putative mobile elements from rumen microbial genomes and genome bins to the rumen virome (TBLASTX; E ≤ 0.001). Table S5. CRISPR‐associated (Cas) proteins detected in rumen microbial genomes and genome bins by RAST and GenBank. Table S6. CRISPR‐associated (Cas) proteins detected in the rumen viral and microbial metagenomes by MG‐RAST or BLASTX comparisons to the NR database. Table S7. Comparison of CRISPR spacer sequences from microbial reference genomes to three nucleotide databases (BLASTN; E‐value < 0.001). Table S8. CRISPR spacer sequences from the rumen metagenome predicted open reading frames (ORFs) generated by Hess and colleagues (2011) compared with three nucleotide databases (BLASTN; E‐value ≤ 0.001, sequence identity > 90%). Fig. S1. Monte Carlo simulations for cross‐contigs between rumen viromes. The per cent shared viral genotypes and percent permuted rank abundance are plotted. The colours represent the likelihood score for a given position. The black dot on each plot represents the location of the best percent shared and percent permuted. (A) Cull‐7664 versusLact‐6993; (B) Cull‐7664 versus Dry‐7887; (C) Lact‐6993 versus Dry‐7887. Fig. S2. Principal component analysis carried out in MG‐RAST using normalized and centred data from the organismal or functional classifications of our rumen viromes and 10 publicly available ocean viromes. The red and green dots represent the rumen and ocean viromes respectively. A. M5NR database, E‐value ≤ 0.001. B. SEED subsystems, E‐value ≤ 1e‐5. Fig. S3. The distribution of significant matches of virome sequence reads from each cow to the GenBank non‐redundant (NR) database based on BLASTX sequence similarities (E‐value ≤ 0.001). The data were generated by the Joint Genome Institute using the MEGAN metagenome analysis software. Fig. S4. The distribution of significant matches of virome sequence reads from each cow to a non‐redundant viral database (NR_Viral_DB) based on a TBLASTX sequence similarities (E‐value < 0.001). Fig. S5. Venn Diagram showing the shared and unique hits to the NR_Viral_DB for three rumen viromes (TBLASTX; E‐value ≤ 0.001). Fig. S6. Distribution of sequences with similarity to the rumen viral metagenome (virome) based on TBLASTX sequence similarities of microbial reference genomes (A) and genome bins (B) to sequence reads from the rumen virome (E‐value ≤ 0.001). Identities of the virome sequences were determined by TBLASTX comparison (E‐value ≤ 0.001) to the NR_Viral_DB. Sequences classified as ‘other’ had significant similarity to a virome sequence that did not match any sequences in the NR_Viral_DB. The distribution of viruses, prophages, and other sequences seen here are a proportion of the total number of sequences from each genome that were similar to the rumen virome sequences. Table S1. Percentage of sequences (mean ± SD) with similarity to SEED subsystems (E‐value ≤ 1e‐5). Table S2. BLASTX comparison of the rumen virome to the Carbohydrate Active Enzyme (CAZy) database. Table S3. Putative mobile elements detected in rumen microbial genomes and genome bins. Table S4. Comparison of putative mobile elements from rumen microbial genomes and genome bins to the rumen virome (TBLASTX; E ≤ 0.001). Table S5. CRISPR‐associated (Cas) proteins detected in rumen microbial genomes and genome bins by RAST and GenBank. Table S6. CRISPR‐associated (Cas) proteins detected in the rumen viral and microbial metagenomes by MG‐RAST or BLASTX comparisons to the NR database. Table S7. Comparison of CRISPR spacer sequences from microbial reference genomes to three nucleotide databases (BLASTN; E‐value < 0.001). Table S8. CRISPR spacer sequences from the rumen metagenome predicted open reading frames (ORFs) generated by Hess and colleagues (2011) compared with three nucleotide databases (BLASTN; E‐value ≤ 0.001, sequence identity > 90%). Fig. S1. Monte Carlo simulations for cross‐contigs between rumen viromes. The per cent shared viral genotypes and percent permuted rank abundance are plotted. The colours represent the likelihood score for a given position. The black dot on each plot represents the location of the best percent shared and percent permuted. (A) Cull‐7664 versusLact‐6993; (B) Cull‐7664 versus Dry‐7887; (C) Lact‐6993 versus Dry‐7887. Fig. S2. Principal component analysis carried out in MG‐RAST using normalized and centred data from the organismal or functional classifications of our rumen viromes and 10 publicly available ocean viromes. The red and green dots represent the rumen and ocean viromes respectively. A. M5NR database, E‐value ≤ 0.001. B. SEED subsystems, E‐value ≤ 1e‐5. Fig. S3. The distribution of significant matches of virome sequence reads from each cow to the GenBank non‐redundant (NR) database based on BLASTX sequence similarities (E‐value ≤ 0.001). The data were generated by the Joint Genome Institute using the MEGAN metagenome analysis software. Fig. S4. The distribution of significant matches of virome sequence reads from each cow to a non‐redundant viral database (NR_Viral_DB) based on a TBLASTX sequence similarities (E‐value < 0.001). Fig. S5. Venn Diagram showing the shared and unique hits to the NR_Viral_DB for three rumen viromes (TBLASTX; E‐value ≤ 0.001). Fig. S6. Distribution of sequences with similarity to the rumen viral metagenome (virome) based on TBLASTX sequence similarities of microbial reference genomes (A) and genome bins (B) to sequence reads from the rumen virome (E‐value ≤ 0.001). Identities of the virome sequences were determined by TBLASTX comparison (E‐value ≤ 0.001) to the NR_Viral_DB. Sequences classified as ‘other’ had significant similarity to a virome sequence that did not match any sequences in the NR_Viral_DB. The distribution of viruses, prophages, and other sequences seen here are a proportion of the total number of sequences from each genome that were similar to the rumen virome sequences. Table S1. Percentage of sequences (mean ± SD) with similarity to SEED subsystems (E‐value ≤ 1e‐5). Table S2. BLASTX comparison of the rumen virome to the Carbohydrate Active Enzyme (CAZy) database. Table S3. Putative mobile elements detected in rumen microbial genomes and genome bins. Table S4. Comparison of putative mobile elements from rumen microbial genomes and genome bins to the rumen virome (TBLASTX; E ≤ 0.001). Table S5. CRISPR‐associated (Cas) proteins detected in rumen microbial genomes and genome bins by RAST and GenBank. Table S6. CRISPR‐associated (Cas) proteins detected in the rumen viral and microbial metagenomes by MG‐RAST or BLASTX comparisons to the NR database. Table S7. Comparison of CRISPR spacer sequences from microbial reference genomes to three nucleotide databases (BLASTN; E‐value < 0.001). Table S8. CRISPR spacer sequences from the rumen metagenome predicted open reading frames (ORFs) generated by Hess and colleagues (2011) compared with three nucleotide databases (BLASTN; E‐value ≤ 0.001, sequence identity > 90%). Fig. S1. Monte Carlo simulations for cross‐contigs between rumen viromes. The per cent shared viral genotypes and percent permuted rank abundance are plotted. The colours represent the likelihood score for a given position. The black dot on each plot represents the location of the best percent shared and percent permuted. (A) Cull‐7664 versusLact‐6993; (B) Cull‐7664 versus Dry‐7887; (C) Lact‐6993 versus Dry‐7887. Fig. S2. Principal component analysis carried out in MG‐RAST using normalized and centred data from the organismal or functional classifications of our rumen viromes and 10 publicly available ocean viromes. The red and green dots represent the rumen and ocean viromes respectively. A. M5NR database, E‐value ≤ 0.001. B. SEED subsystems, E‐value ≤ 1e‐5. Fig. S3. The distribution of significant matches of virome sequence reads from each cow to the GenBank non‐redundant (NR) database based on BLASTX sequence similarities (E‐value ≤ 0.001). The data were generated by the Joint Genome Institute using the MEGAN metagenome analysis software. Fig. S4. The distribution of significant matches of virome sequence reads from each cow to a non‐redundant viral database (NR_Viral_DB) based on a TBLASTX sequence similarities (E‐value < 0.001). Fig. S5. Venn Diagram showing the shared and unique hits to the NR_Viral_DB for three rumen viromes (TBLASTX; E‐value ≤ 0.001). Fig. S6. Distribution of sequences with similarity to the rumen viral metagenome (virome) based on TBLASTX sequence similarities of microbial reference genomes (A) and genome bins (B) to sequence reads from the rumen virome (E‐value ≤ 0.001). Identities of the virome sequences were determined by TBLASTX comparison (E‐value ≤ 0.001) to the NR_Viral_DB. Sequences classified as ‘other’ had significant similarity to a virome sequence that did not match any sequences in the NR_Viral_DB. The distribution of viruses, prophages, and other sequences seen here are a proportion of the total number of sequences from each genome that were similar to the rumen virome sequences. Table S1. Percentage of sequences (mean ± SD) with similarity to SEED subsystems (E‐value ≤ 1e‐5). Table S2. BLASTX comparison of the rumen virome to the Carbohydrate Active Enzyme (CAZy) database. Table S3. Putative mobile elements detected in rumen microbial genomes and genome bins. Table S4. Comparison of putative mobile elements from rumen microbial genomes and genome bins to the rumen virome (TBLASTX; E ≤ 0.001). Table S5. CRISPR‐associated (Cas) proteins detected in rumen microbial genomes and genome bins by RAST and GenBank. Table S6. CRISPR‐associated (Cas) proteins detected in the rumen viral and microbial metagenomes by MG‐RAST or BLASTX comparisons to the NR database. Table S7. Comparison of CRISPR spacer sequences from microbial reference genomes to three nucleotide databases (BLASTN; E‐value < 0.001). Table S8. CRISPR spacer sequences from the rumen metagenome predicted open reading frames (ORFs) generated by Hess and colleagues (2011) compared with three nucleotide databases (BLASTN; E‐value ≤ 0.001, sequence identity > 90%). Fig. S1. Monte Carlo simulations for cross‐contigs between rumen viromes. The per cent shared viral genotypes and percent permuted rank abundance are plotted. The colours represent the likelihood score for a given position. The black dot on each plot represents the location of the best percent shared and percent permuted. (A) Cull‐7664 versusLact‐6993; (B) Cull‐7664 versus Dry‐7887; (C) Lact‐6993 versus Dry‐7887. Fig. S2. Principal component analysis carried out in MG‐RAST using normalized and centred data from the organismal or functional classifications of our rumen viromes and 10 publicly available ocean viromes. The red and green dots represent the rumen and ocean viromes respectively. A. M5NR database, E‐value ≤ 0.001. B. SEED subsystems, E‐value ≤ 1e‐5. Fig. S3. The distribution of significant matches of virome sequence reads from each cow to the GenBank non‐redundant (NR) database based on BLASTX sequence similarities (E‐value ≤ 0.001). The data were generated by the Joint Genome Institute using the MEGAN metagenome analysis software. Fig. S4. The distribution of significant matches of virome sequence reads from each cow to a non‐redundant viral database (NR_Viral_DB) based on a TBLASTX sequence similarities (E‐value < 0.001). Fig. S5. Venn Diagram showing the shared and unique hits to the NR_Viral_DB for three rumen viromes (TBLASTX; E‐value ≤ 0.001). Fig. S6. Distribution of sequences with similarity to the rumen viral metagenome (virome) based on TBLASTX sequence similarities of microbial reference genomes (A) and genome bins (B) to sequence reads from the rumen virome (E‐value ≤ 0.001). Identities of the virome sequences were determined by TBLASTX comparison (E‐value ≤ 0.001) to the NR_Viral_DB. Sequences classified as ‘other’ had significant similarity to a virome sequence that did not match any sequences in the NR_Viral_DB. The distribution of viruses, prophages, and other sequences seen here are a proportion of the total number of sequences from each genome that were similar to the rumen virome sequences. Table S1. Percentage of sequences (mean ± SD) with similarity to SEED subsystems (E‐value ≤ 1e‐5). Table S2. BLASTX comparison of the rumen virome to the Carbohydrate Active Enzyme (CAZy) database. Table S3. Putative mobile elements detected in rumen microbial genomes and genome bins. Table S4. Comparison of putative mobile elements from rumen microbial genomes and genome bins to the rumen virome (TBLASTX; E ≤ 0.001). Table S5. CRISPR‐associated (Cas) proteins detected in rumen microbial genomes and genome bins by RAST and GenBank. Table S6. CRISPR‐associated (Cas) proteins detected in the rumen viral and microbial metagenomes by MG‐RAST or BLASTX comparisons to the NR database. Table S7. Comparison of CRISPR spacer sequences from microbial reference genomes to three nucleotide databases (BLASTN; E‐value < 0.001). Table S8. CRISPR spacer sequences from the rumen metagenome predicted open reading frames (ORFs) generated by Hess and colleagues (2011) compared with three nucleotide databases (BLASTN; E‐value ≤ 0.001, sequence identity > 90%). Fig. S1. Monte Carlo simulations for cross‐contigs between rumen viromes. The per cent shared viral genotypes and percent permuted rank abundance are plotted. The colours represent the likelihood score for a given position. The black dot on each plot represents the location of the best percent shared and percent permuted. (A) Cull‐7664 versusLact‐6993; (B) Cull‐7664 versus Dry‐7887; (C) Lact‐6993 versus Dry‐7887. Fig. S2. Principal component analysis carried out in MG‐RAST using normalized and centred data from the organismal or functional classifications of our rumen viromes and 10 publicly available ocean viromes. The red and green dots represent the rumen and ocean viromes respectively. A. M5NR database, E‐value ≤ 0.001. B. SEED subsystems, E‐value ≤ 1e‐5. Fig. S3. The distribution of significant matches of virome sequence reads from each cow to the GenBank non‐redundant (NR) database based on BLASTX sequence similarities (E‐value ≤ 0.001). The data were generated by the Joint Genome Institute using the MEGAN metagenome analysis software. Fig. S4. The distribution of significant matches of virome sequence reads from each cow to a non‐redundant viral database (NR_Viral_DB) based on a TBLASTX sequence similarities (E‐value < 0.001). Fig. S5. Venn Diagram showing the shared and unique hits to the NR_Viral_DB for three rumen viromes (TBLASTX; E‐value ≤ 0.001). Fig. S6. Distribution of sequences with similarity to the rumen viral metagenome (virome) based on TBLASTX sequence similarities of microbial reference genomes (A) and genome bins (B) to sequence reads from the rumen virome (E‐value ≤ 0.001). Identities of the virome sequences were determined by TBLASTX comparison (E‐value ≤ 0.001) to the NR_Viral_DB. Sequences classified as ‘other’ had significant similarity to a virome sequence that did not match any sequences in the NR_Viral_DB. The distribution of viruses, prophages, and other sequences seen here are a proportion of the total number of sequences from each genome that were similar to the rumen virome sequences. Table S1. Percentage of sequences (mean ± SD) with similarity to SEED subsystems (E‐value ≤ 1e‐5). Table S2. BLASTX comparison of the rumen virome to the Carbohydrate Active Enzyme (CAZy) database. Table S3. Putative mobile elements detected in rumen microbial genomes and genome bins. Table S4. Comparison of putative mobile elements from rumen microbial genomes and genome bins to the rumen virome (TBLASTX; E ≤ 0.001). Table S5. CRISPR‐associated (Cas) proteins detected in rumen microbial genomes and genome bins by RAST and GenBank. Table S6. CRISPR‐associated (Cas) proteins detected in the rumen viral and microbial metagenomes by MG‐RAST or BLASTX comparisons to the NR database. Table S7. Comparison of CRISPR spacer sequences from microbial reference genomes to three nucleotide databases (BLASTN; E‐value < 0.001). Table S8. CRISPR spacer sequences from the rumen metagenome predicted open reading frames (ORFs) generated by Hess and colleagues (2011) compared with three nucleotide databases (BLASTN; E‐value ≤ 0.001, sequence identity > 90%). Fig. S1. Monte Carlo simulations for cross‐contigs between rumen viromes. The per cent shared viral genotypes and percent permuted rank abundance are plotted. The colours represent the likelihood score for a given position. The black dot on each plot represents the location of the best percent shared and percent permuted. (A) Cull‐7664 versusLact‐6993; (B) Cull‐7664 versus Dry‐7887; (C) Lact‐6993 versus Dry‐7887. Fig. S2. Principal component analysis carried out in MG‐RAST using normalized and centred data from the organismal or functional classifications of our rumen viromes and 10 publicly available ocean viromes. The red and green dots represent the rumen and ocean viromes respectively. A. M5NR database, E‐value ≤ 0.001. B. SEED subsystems, E‐value ≤ 1e‐5. Fig. S3. The distribution of significant matches of virome sequence reads from each cow to the GenBank non‐redundant (NR) database based on BLASTX sequence similarities (E‐value ≤ 0.001). The data were generated by the Joint Genome Institute using the MEGAN metagenome analysis software. Fig. S4. The distribution of significant matches of virome sequence reads from each cow to a non‐redundant viral database (NR_Viral_DB) based on a TBLASTX sequence similarities (E‐value < 0.001). Fig. S5. Venn Diagram showing the shared and unique hits to the NR_Viral_DB for three rumen viromes (TBLASTX; E‐value ≤ 0.001). Fig. S6. Distribution of sequences with similarity to the rumen viral metagenome (virome) based on TBLASTX sequence similarities of microbial reference genomes (A) and genome bins (B) to sequence reads from the rumen virome (E‐value ≤ 0.001). Identities of the virome sequences were determined by TBLASTX comparison (E‐value ≤ 0.001) to the NR_Viral_DB. Sequences classified as ‘other’ had significant similarity to a virome sequence that did not match any sequences in the NR_Viral_DB. The distribution of viruses, prophages, and other sequences seen here are a proportion of the total number of sequences from each genome that were similar to the rumen virome sequences. Table S1. Percentage of sequences (mean ± SD) with similarity to SEED subsystems (E‐value ≤ 1e‐5). Table S2. BLASTX comparison of the rumen virome to the Carbohydrate Active Enzyme (CAZy) database. Table S3. Putative mobile elements detected in rumen microbial genomes and genome bins. Table S4. Comparison of putative mobile elements from rumen microbial genomes and genome bins to the rumen virome (TBLASTX; E ≤ 0.001). Table S5. CRISPR‐associated (Cas) proteins detected in rumen microbial genomes and genome bins by RAST and GenBank. Table S6. CRISPR‐associated (Cas) proteins detected in the rumen viral and microbial metagenomes by MG‐RAST or BLASTX comparisons to the NR database. Table S7. Comparison of CRISPR spacer sequences from microbial reference genomes to three nucleotide databases (BLASTN; E‐value < 0.001). Table S8. CRISPR spacer sequences from the rumen metagenome predicted open reading frames (ORFs) generated by Hess and colleagues (2011) compared with three nucleotide databases (BLASTN; E‐value ≤ 0.001, sequence identity > 90%).Supporting Info Item: Supporting info item - Supporting info item - Supporting info item - Supporting info item - Supporting info item - Supporting info item - Supporting info item - </note><identifier type="ISSN">1462-2912</identifier><identifier type="eISSN">1462-2920</identifier><identifier type="DOI">10.1111/(ISSN)1462-2920</identifier><identifier type="PublisherID">EMI</identifier><part><date>2012</date><detail type="title"><title>OMICS Driven Microbial Ecology</title></detail><detail type="volume"><caption>vol.</caption><number>14</number></detail><detail type="issue"><caption>no.</caption><number>1</number></detail><extent unit="pages"><start>207</start><end>227</end><total>21</total></extent></part></relatedItem><identifier type="istex">08705BF8F57E6F36D4BE299E678D547AB1D3A814</identifier><identifier type="DOI">10.1111/j.1462-2920.2011.02593.x</identifier><identifier type="ArticleID">EMI2593</identifier><accessCondition type="use and reproduction" contentType="copyright">© 2011 Society for Applied Microbiology and Blackwell Publishing Ltd</accessCondition><recordInfo><recordContentSource>WILEY</recordContentSource><recordOrigin>Blackwell Publishing Ltd</recordOrigin></recordInfo></mods></metadata><enrichments><json:item><type>multicat</type><uri>https://api.istex.fr/document/08705BF8F57E6F36D4BE299E678D547AB1D3A814/enrichments/multicat</uri></json:item></enrichments><serie></serie></istex></record>Pour manipuler ce document sous Unix (Dilib)
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