Improving Species Level‐taxonomic Assignment from 16S rRNA Sequencing Technologies
Analysis of the bacterial community from a 16S rRNA gene sequencing technologies requires comparing the reads to a reference database. The challenging task involved in annotation relies on the currently available tools and 16S rRNA databases: SILVA, Greengenes and RDP. A successful annotation depend...
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Veröffentlicht in: | Current protocols 2023-11, Vol.3 (11), p.e930-n/a |
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creator | Bars‐Cortina, David Moratalla‐Navarro, Ferran García‐Serrano, Ainhoa Mach, Núria Riobó‐Mayo, Lois Vea‐Barbany, Jordi Rius‐Sansalvador, Blanca Murcia, Silvia Obón‐Santacana, Mireia Moreno, Victor |
description | Analysis of the bacterial community from a 16S rRNA gene sequencing technologies requires comparing the reads to a reference database. The challenging task involved in annotation relies on the currently available tools and 16S rRNA databases: SILVA, Greengenes and RDP. A successful annotation depends on the quality of the database. For instance, Greengenes and RDP have not been updated since 2013 and 2016, respectively. In addition, the nature of 16S sequencing technologies (short reads) focuses mainly on the V3‐V4 hypervariable region sequencing and hinders the species assignment, in contrast to whole shotgun metagenome sequencing.
Here, we combine the results of three standard protocols for 16S rRNA amplicon annotation that utilize homology‐based methods, and we propose a new re‐annotation strategy to enlarge the percentage of amplicon sequence variants (ASV) classified up to the species level. Following the pattern (reference) method: DADA2 pipeline and SILVA v.138.1 reference database classification (Basic Protocol 1), our method maps the ASV sequences to custom nucleotide BLAST with the SILVA v.138.1 (Basic Protocol 2), and to the 16S database of Bacteria and Archaea of NCBI RefSeq Targeted Loci Project databases (Basic Protocol 3).
This new re‐annotation workflow was tested in 16S rRNA amplicon data from 156 human fecal samples. The proposed new strategy achieved an increase of nearly eight times the proportion of ASV classified at the species level in contrast to the reference method for the database used in the present research. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC.
Basic Protocol 1: Sample inference and taxonomic profiling through DADA2 algorithm.
Basic Protocol 2: Custom BLASTN database creation and ASV taxonomical assignment.
Basic Protocol 3: ASV taxonomical assignment using NCBI RefSeq Targeted Loci Project database.
Basic Protocol 4: Definitive selection of lineages among the three methods. |
doi_str_mv | 10.1002/cpz1.930 |
format | Article |
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Here, we combine the results of three standard protocols for 16S rRNA amplicon annotation that utilize homology‐based methods, and we propose a new re‐annotation strategy to enlarge the percentage of amplicon sequence variants (ASV) classified up to the species level. Following the pattern (reference) method: DADA2 pipeline and SILVA v.138.1 reference database classification (Basic Protocol 1), our method maps the ASV sequences to custom nucleotide BLAST with the SILVA v.138.1 (Basic Protocol 2), and to the 16S database of Bacteria and Archaea of NCBI RefSeq Targeted Loci Project databases (Basic Protocol 3).
This new re‐annotation workflow was tested in 16S rRNA amplicon data from 156 human fecal samples. The proposed new strategy achieved an increase of nearly eight times the proportion of ASV classified at the species level in contrast to the reference method for the database used in the present research. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC.
Basic Protocol 1: Sample inference and taxonomic profiling through DADA2 algorithm.
Basic Protocol 2: Custom BLASTN database creation and ASV taxonomical assignment.
Basic Protocol 3: ASV taxonomical assignment using NCBI RefSeq Targeted Loci Project database.
Basic Protocol 4: Definitive selection of lineages among the three methods.</description><identifier>ISSN: 2691-1299</identifier><identifier>EISSN: 2691-1299</identifier><identifier>DOI: 10.1002/cpz1.930</identifier><identifier>PMID: 37988265</identifier><language>eng</language><publisher>Wiley</publisher><subject>16S rRNA gene amplicons ; Biodiversity ; Computer Science ; DADA2 ; Humanities and Social Sciences ; Life Sciences ; Medicin och hälsovetenskap ; metagenomics ; Methods and statistics ; Microbiology and Parasitology ; microbiota composition ; Quantitative Methods ; species‐classification ; Systematics, Phylogenetics and taxonomy</subject><ispartof>Current protocols, 2023-11, Vol.3 (11), p.e930-n/a</ispartof><rights>2023 The Authors. Current Protocols published by Wiley Periodicals LLC.</rights><rights>Attribution - NonCommercial</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4540-d95da94aec9364248f22e3bdec3ca2b22421027df4fd456c48161e34d0abb0dd3</citedby><cites>FETCH-LOGICAL-c4540-d95da94aec9364248f22e3bdec3ca2b22421027df4fd456c48161e34d0abb0dd3</cites><orcidid>0000-0002-2818-5487 ; 0000-0001-9864-2906 ; 0000-0002-8001-6314</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fcpz1.930$$EPDF$$P50$$Gwiley$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fcpz1.930$$EHTML$$P50$$Gwiley$$Hfree_for_read</linktohtml><link.rule.ids>230,314,552,780,784,885,1417,27924,27925,45574,45575</link.rule.ids><backlink>$$Uhttps://hal.inrae.fr/hal-04327324$$DView record in HAL$$Hfree_for_read</backlink><backlink>$$Uhttp://kipublications.ki.se/Default.aspx?queryparsed=id:237988265$$DView record from Swedish Publication Index$$Hfree_for_read</backlink></links><search><creatorcontrib>Bars‐Cortina, David</creatorcontrib><creatorcontrib>Moratalla‐Navarro, Ferran</creatorcontrib><creatorcontrib>García‐Serrano, Ainhoa</creatorcontrib><creatorcontrib>Mach, Núria</creatorcontrib><creatorcontrib>Riobó‐Mayo, Lois</creatorcontrib><creatorcontrib>Vea‐Barbany, Jordi</creatorcontrib><creatorcontrib>Rius‐Sansalvador, Blanca</creatorcontrib><creatorcontrib>Murcia, Silvia</creatorcontrib><creatorcontrib>Obón‐Santacana, Mireia</creatorcontrib><creatorcontrib>Moreno, Victor</creatorcontrib><title>Improving Species Level‐taxonomic Assignment from 16S rRNA Sequencing Technologies</title><title>Current protocols</title><description>Analysis of the bacterial community from a 16S rRNA gene sequencing technologies requires comparing the reads to a reference database. The challenging task involved in annotation relies on the currently available tools and 16S rRNA databases: SILVA, Greengenes and RDP. A successful annotation depends on the quality of the database. For instance, Greengenes and RDP have not been updated since 2013 and 2016, respectively. In addition, the nature of 16S sequencing technologies (short reads) focuses mainly on the V3‐V4 hypervariable region sequencing and hinders the species assignment, in contrast to whole shotgun metagenome sequencing.
Here, we combine the results of three standard protocols for 16S rRNA amplicon annotation that utilize homology‐based methods, and we propose a new re‐annotation strategy to enlarge the percentage of amplicon sequence variants (ASV) classified up to the species level. Following the pattern (reference) method: DADA2 pipeline and SILVA v.138.1 reference database classification (Basic Protocol 1), our method maps the ASV sequences to custom nucleotide BLAST with the SILVA v.138.1 (Basic Protocol 2), and to the 16S database of Bacteria and Archaea of NCBI RefSeq Targeted Loci Project databases (Basic Protocol 3).
This new re‐annotation workflow was tested in 16S rRNA amplicon data from 156 human fecal samples. The proposed new strategy achieved an increase of nearly eight times the proportion of ASV classified at the species level in contrast to the reference method for the database used in the present research. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC.
Basic Protocol 1: Sample inference and taxonomic profiling through DADA2 algorithm.
Basic Protocol 2: Custom BLASTN database creation and ASV taxonomical assignment.
Basic Protocol 3: ASV taxonomical assignment using NCBI RefSeq Targeted Loci Project database.
Basic Protocol 4: Definitive selection of lineages among the three methods.</description><subject>16S rRNA gene amplicons</subject><subject>Biodiversity</subject><subject>Computer Science</subject><subject>DADA2</subject><subject>Humanities and Social Sciences</subject><subject>Life Sciences</subject><subject>Medicin och hälsovetenskap</subject><subject>metagenomics</subject><subject>Methods and statistics</subject><subject>Microbiology and Parasitology</subject><subject>microbiota composition</subject><subject>Quantitative Methods</subject><subject>species‐classification</subject><subject>Systematics, Phylogenetics and taxonomy</subject><issn>2691-1299</issn><issn>2691-1299</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><sourceid>WIN</sourceid><sourceid>D8T</sourceid><recordid>eNp1kU1u2zAQRokiQW0kAXoELduFHHJIyeLSMJofwEiL2tl0Q1DkyFYiiapoO0lXPULOmJOUgpO6WXjFweC9D-B8hHxidMQohXPT_mYjyekHMoRUspiBlEf_zQNy5v0dDWjCOBPwkQz4WGYZpMmQLK7rtnPbsllG8xZNiT6a4Rarlz_Pa_3oGleXJpp4Xy6bGpt1VHSujlg6j7ofN5Nojr822JjeXqBZNa5yyxBxSo4LXXk8e31PyO3F18X0Kp59u7yeTmaxEYmgsZWJ1VJoNJKnAkRWACDPLRpuNOQAAhiFsS1EYUWSGpGxlCEXluo8p9byExLvcv0DtptctV1Z6-5JOV2q19V9mFBlHNhYBF4e5MMV7F56E-HtUsH9snNXunonXk1mqt9RwWHMQWxZYD_v2BAaDuTXqi69warSDbqNV5BJSNNQBd2jpnPed1j8y2ZU9fWqvl4V6t3_9qGs8Okgp6bff7Ke_wuMCKWn</recordid><startdate>202311</startdate><enddate>202311</enddate><creator>Bars‐Cortina, David</creator><creator>Moratalla‐Navarro, Ferran</creator><creator>García‐Serrano, Ainhoa</creator><creator>Mach, Núria</creator><creator>Riobó‐Mayo, Lois</creator><creator>Vea‐Barbany, Jordi</creator><creator>Rius‐Sansalvador, Blanca</creator><creator>Murcia, Silvia</creator><creator>Obón‐Santacana, Mireia</creator><creator>Moreno, Victor</creator><general>Wiley</general><scope>24P</scope><scope>WIN</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>1XC</scope><scope>BXJBU</scope><scope>IHQJB</scope><scope>VOOES</scope><scope>ADTPV</scope><scope>AOWAS</scope><scope>D8T</scope><scope>ZZAVC</scope><orcidid>https://orcid.org/0000-0002-2818-5487</orcidid><orcidid>https://orcid.org/0000-0001-9864-2906</orcidid><orcidid>https://orcid.org/0000-0002-8001-6314</orcidid></search><sort><creationdate>202311</creationdate><title>Improving Species Level‐taxonomic Assignment from 16S rRNA Sequencing Technologies</title><author>Bars‐Cortina, David ; Moratalla‐Navarro, Ferran ; García‐Serrano, Ainhoa ; Mach, Núria ; Riobó‐Mayo, Lois ; Vea‐Barbany, Jordi ; Rius‐Sansalvador, Blanca ; Murcia, Silvia ; Obón‐Santacana, Mireia ; Moreno, Victor</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4540-d95da94aec9364248f22e3bdec3ca2b22421027df4fd456c48161e34d0abb0dd3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>16S rRNA gene amplicons</topic><topic>Biodiversity</topic><topic>Computer Science</topic><topic>DADA2</topic><topic>Humanities and Social Sciences</topic><topic>Life Sciences</topic><topic>Medicin och hälsovetenskap</topic><topic>metagenomics</topic><topic>Methods and statistics</topic><topic>Microbiology and Parasitology</topic><topic>microbiota composition</topic><topic>Quantitative Methods</topic><topic>species‐classification</topic><topic>Systematics, Phylogenetics and taxonomy</topic><toplevel>online_resources</toplevel><creatorcontrib>Bars‐Cortina, David</creatorcontrib><creatorcontrib>Moratalla‐Navarro, Ferran</creatorcontrib><creatorcontrib>García‐Serrano, Ainhoa</creatorcontrib><creatorcontrib>Mach, Núria</creatorcontrib><creatorcontrib>Riobó‐Mayo, Lois</creatorcontrib><creatorcontrib>Vea‐Barbany, Jordi</creatorcontrib><creatorcontrib>Rius‐Sansalvador, Blanca</creatorcontrib><creatorcontrib>Murcia, Silvia</creatorcontrib><creatorcontrib>Obón‐Santacana, Mireia</creatorcontrib><creatorcontrib>Moreno, Victor</creatorcontrib><collection>Wiley Online Library Open Access</collection><collection>Wiley Online Library Free Content</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>Hyper Article en Ligne (HAL)</collection><collection>HAL-SHS: Archive ouverte en Sciences de l'Homme et de la Société</collection><collection>HAL-SHS: Archive ouverte en Sciences de l'Homme et de la Société (Open Access)</collection><collection>Hyper Article en Ligne (HAL) (Open Access)</collection><collection>SwePub</collection><collection>SwePub Articles</collection><collection>SWEPUB Freely available online</collection><collection>SwePub Articles full text</collection><jtitle>Current protocols</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bars‐Cortina, David</au><au>Moratalla‐Navarro, Ferran</au><au>García‐Serrano, Ainhoa</au><au>Mach, Núria</au><au>Riobó‐Mayo, Lois</au><au>Vea‐Barbany, Jordi</au><au>Rius‐Sansalvador, Blanca</au><au>Murcia, Silvia</au><au>Obón‐Santacana, Mireia</au><au>Moreno, Victor</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Improving Species Level‐taxonomic Assignment from 16S rRNA Sequencing Technologies</atitle><jtitle>Current protocols</jtitle><date>2023-11</date><risdate>2023</risdate><volume>3</volume><issue>11</issue><spage>e930</spage><epage>n/a</epage><pages>e930-n/a</pages><issn>2691-1299</issn><eissn>2691-1299</eissn><abstract>Analysis of the bacterial community from a 16S rRNA gene sequencing technologies requires comparing the reads to a reference database. The challenging task involved in annotation relies on the currently available tools and 16S rRNA databases: SILVA, Greengenes and RDP. A successful annotation depends on the quality of the database. For instance, Greengenes and RDP have not been updated since 2013 and 2016, respectively. In addition, the nature of 16S sequencing technologies (short reads) focuses mainly on the V3‐V4 hypervariable region sequencing and hinders the species assignment, in contrast to whole shotgun metagenome sequencing.
Here, we combine the results of three standard protocols for 16S rRNA amplicon annotation that utilize homology‐based methods, and we propose a new re‐annotation strategy to enlarge the percentage of amplicon sequence variants (ASV) classified up to the species level. Following the pattern (reference) method: DADA2 pipeline and SILVA v.138.1 reference database classification (Basic Protocol 1), our method maps the ASV sequences to custom nucleotide BLAST with the SILVA v.138.1 (Basic Protocol 2), and to the 16S database of Bacteria and Archaea of NCBI RefSeq Targeted Loci Project databases (Basic Protocol 3).
This new re‐annotation workflow was tested in 16S rRNA amplicon data from 156 human fecal samples. The proposed new strategy achieved an increase of nearly eight times the proportion of ASV classified at the species level in contrast to the reference method for the database used in the present research. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC.
Basic Protocol 1: Sample inference and taxonomic profiling through DADA2 algorithm.
Basic Protocol 2: Custom BLASTN database creation and ASV taxonomical assignment.
Basic Protocol 3: ASV taxonomical assignment using NCBI RefSeq Targeted Loci Project database.
Basic Protocol 4: Definitive selection of lineages among the three methods.</abstract><pub>Wiley</pub><pmid>37988265</pmid><doi>10.1002/cpz1.930</doi><tpages>19</tpages><orcidid>https://orcid.org/0000-0002-2818-5487</orcidid><orcidid>https://orcid.org/0000-0001-9864-2906</orcidid><orcidid>https://orcid.org/0000-0002-8001-6314</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | 16S rRNA gene amplicons Biodiversity Computer Science DADA2 Humanities and Social Sciences Life Sciences Medicin och hälsovetenskap metagenomics Methods and statistics Microbiology and Parasitology microbiota composition Quantitative Methods species‐classification Systematics, Phylogenetics and taxonomy |
title | Improving Species Level‐taxonomic Assignment from 16S rRNA Sequencing Technologies |
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