Functional Basis of Microorganism Classification
Correctly identifying nearest "neighbors" of a given microorganism is important in industrial and clinical applications where close relationships imply similar treatment. Microbial classification based on similarity of physiological and genetic organism traits (polyphasic similarity) is ex...
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description | Correctly identifying nearest "neighbors" of a given microorganism is important in industrial and clinical applications where close relationships imply similar treatment. Microbial classification based on similarity of physiological and genetic organism traits (polyphasic similarity) is experimentally difficult and, arguably, subjective. Evolutionary relatedness, inferred from phylogenetic markers, facilitates classification but does not guarantee functional identity between members of the same taxon or lack of similarity between different taxa. Using over thirteen hundred sequenced bacterial genomes, we built a novel function-based microorganism classification scheme, functional-repertoire similarity-based organism network (FuSiON; flattened to fusion). Our scheme is phenetic, based on a network of quantitatively defined organism relationships across the known prokaryotic space. It correlates significantly with the current taxonomy, but the observed discrepancies reveal both (1) the inconsistency of functional diversity levels among different taxa and (2) an (unsurprising) bias towards prioritizing, for classification purposes, relatively minor traits of particular interest to humans. Our dynamic network-based organism classification is independent of the arbitrary pairwise organism similarity cut-offs traditionally applied to establish taxonomic identity. Instead, it reveals natural, functionally defined organism groupings and is thus robust in handling organism diversity. Additionally, fusion can use organism meta-data to highlight the specific environmental factors that drive microbial diversification. Our approach provides a complementary view to cladistic assignments and holds important clues for further exploration of microbial lifestyles. Fusion is a more practical fit for biomedical, industrial, and ecological applications, as many of these rely on understanding the functional capabilities of the microbes in their environment and are less concerned with phylogenetic descent. |
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Microbial classification based on similarity of physiological and genetic organism traits (polyphasic similarity) is experimentally difficult and, arguably, subjective. Evolutionary relatedness, inferred from phylogenetic markers, facilitates classification but does not guarantee functional identity between members of the same taxon or lack of similarity between different taxa. Using over thirteen hundred sequenced bacterial genomes, we built a novel function-based microorganism classification scheme, functional-repertoire similarity-based organism network (FuSiON; flattened to fusion). Our scheme is phenetic, based on a network of quantitatively defined organism relationships across the known prokaryotic space. It correlates significantly with the current taxonomy, but the observed discrepancies reveal both (1) the inconsistency of functional diversity levels among different taxa and (2) an (unsurprising) bias towards prioritizing, for classification purposes, relatively minor traits of particular interest to humans. Our dynamic network-based organism classification is independent of the arbitrary pairwise organism similarity cut-offs traditionally applied to establish taxonomic identity. Instead, it reveals natural, functionally defined organism groupings and is thus robust in handling organism diversity. Additionally, fusion can use organism meta-data to highlight the specific environmental factors that drive microbial diversification. Our approach provides a complementary view to cladistic assignments and holds important clues for further exploration of microbial lifestyles. 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This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: Zhu C, Delmont TO, Vogel TM, Bromberg Y (2015) Functional Basis of Microorganism Classification. 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Microbial classification based on similarity of physiological and genetic organism traits (polyphasic similarity) is experimentally difficult and, arguably, subjective. Evolutionary relatedness, inferred from phylogenetic markers, facilitates classification but does not guarantee functional identity between members of the same taxon or lack of similarity between different taxa. Using over thirteen hundred sequenced bacterial genomes, we built a novel function-based microorganism classification scheme, functional-repertoire similarity-based organism network (FuSiON; flattened to fusion). Our scheme is phenetic, based on a network of quantitatively defined organism relationships across the known prokaryotic space. It correlates significantly with the current taxonomy, but the observed discrepancies reveal both (1) the inconsistency of functional diversity levels among different taxa and (2) an (unsurprising) bias towards prioritizing, for classification purposes, relatively minor traits of particular interest to humans. Our dynamic network-based organism classification is independent of the arbitrary pairwise organism similarity cut-offs traditionally applied to establish taxonomic identity. Instead, it reveals natural, functionally defined organism groupings and is thus robust in handling organism diversity. Additionally, fusion can use organism meta-data to highlight the specific environmental factors that drive microbial diversification. Our approach provides a complementary view to cladistic assignments and holds important clues for further exploration of microbial lifestyles. Fusion is a more practical fit for biomedical, industrial, and ecological applications, as many of these rely on understanding the functional capabilities of the microbes in their environment and are less concerned with phylogenetic descent.</description><subject>Bacteria - classification</subject><subject>Bacteria - genetics</subject><subject>Classification</subject><subject>Classification - methods</subject><subject>Computational Biology - methods</subject><subject>Electric power</subject><subject>Engineering Sciences</subject><subject>Gene expression</subject><subject>Genetic aspects</subject><subject>Genetic research</subject><subject>Genome, Bacterial - physiology</subject><subject>Genomes</subject><subject>Microbial colonies</subject><subject>Microbiological research</subject><subject>Microorganisms</subject><subject>Organisms</subject><subject>Phylogenetics</subject><subject>Quantitative trait loci</subject><subject>Software</subject><subject>Taxonomy</subject><subject>Vocabularies & taxonomies</subject><issn>1553-7358</issn><issn>1553-734X</issn><issn>1553-7358</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>DOA</sourceid><recordid>eNqVkk1vEzEQhlcIREvhHyDIEQ4JM_7eC1KIKI0UQOLjbM063tTRZh3WuxX8exyyLQ035IOt8TPvzNhvUTxHmCHX-GYbh66lZrZ3VZghgBCaPSjOUUo-1Vyah_fOZ8WTlLYA-Viqx8UZUxy10XhewOXQuj7ErDR5RymkSawnH4PrYuw21Ia0mywaSinUwdGBe1o8qqlJ_tm4XxTfL99_W1xNV58_LBfz1dQpXvZTIk1cGe9qvi69oXWpHdYMnSpVKRlVCqTmXvMKFHhmsETwwEoJzgBIwy-Kl0fdfROTHYdNFjUDYQyKMhPLI7GOtLX7Luyo-2UjBfsnkPu31PXBNd4KV9ey4si0J0FOEBqFYKpK1bkdxrPW27HaUO382vm276g5ET29acO13cQbK6RkSugs8PoocP1P2tV8ZQ8xwPz2KMUNZvbVWKyLPwafersLyfmmodbH4TAjmBIAQWV0dkQ3lMcIbR1zdZfX2u-Ci62vQ47PBQcDjCH87WNMyEzvf_YbGlKyy69f_oP9dMqKI5udkVLn67shEezBj7dfZA9-tKMfc9qL--96l3RrQP4bMenZxQ</recordid><startdate>20150801</startdate><enddate>20150801</enddate><creator>Zhu, Chengsheng</creator><creator>Delmont, Tom O</creator><creator>Vogel, Timothy M</creator><creator>Bromberg, Yana</creator><general>Public Library of Science</general><general>PLOS</general><general>Public Library of Science (PLoS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>ISN</scope><scope>ISR</scope><scope>7X8</scope><scope>1XC</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0001-7053-7848</orcidid><orcidid>https://orcid.org/0000-0002-9542-3246</orcidid></search><sort><creationdate>20150801</creationdate><title>Functional Basis of Microorganism Classification</title><author>Zhu, Chengsheng ; Delmont, Tom O ; Vogel, Timothy M ; Bromberg, Yana</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c639t-aa7a368ecf3d9e8ad97c1f21c696952ab60573e73b060e281910e02950c800583</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Bacteria - classification</topic><topic>Bacteria - genetics</topic><topic>Classification</topic><topic>Classification - methods</topic><topic>Computational Biology - methods</topic><topic>Electric power</topic><topic>Engineering Sciences</topic><topic>Gene expression</topic><topic>Genetic aspects</topic><topic>Genetic research</topic><topic>Genome, Bacterial - physiology</topic><topic>Genomes</topic><topic>Microbial colonies</topic><topic>Microbiological research</topic><topic>Microorganisms</topic><topic>Organisms</topic><topic>Phylogenetics</topic><topic>Quantitative trait loci</topic><topic>Software</topic><topic>Taxonomy</topic><topic>Vocabularies & taxonomies</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhu, Chengsheng</creatorcontrib><creatorcontrib>Delmont, Tom O</creatorcontrib><creatorcontrib>Vogel, Timothy M</creatorcontrib><creatorcontrib>Bromberg, Yana</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Canada</collection><collection>Gale In Context: Science</collection><collection>MEDLINE - Academic</collection><collection>Hyper Article en Ligne (HAL)</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PLoS computational biology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhu, Chengsheng</au><au>Delmont, Tom O</au><au>Vogel, Timothy M</au><au>Bromberg, Yana</au><au>Orengo, Christine A.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Functional Basis of Microorganism Classification</atitle><jtitle>PLoS computational biology</jtitle><addtitle>PLoS Comput Biol</addtitle><date>2015-08-01</date><risdate>2015</risdate><volume>11</volume><issue>8</issue><spage>e1004472</spage><epage>e1004472</epage><pages>e1004472-e1004472</pages><issn>1553-7358</issn><issn>1553-734X</issn><eissn>1553-7358</eissn><abstract>Correctly identifying nearest "neighbors" of a given microorganism is important in industrial and clinical applications where close relationships imply similar treatment. Microbial classification based on similarity of physiological and genetic organism traits (polyphasic similarity) is experimentally difficult and, arguably, subjective. Evolutionary relatedness, inferred from phylogenetic markers, facilitates classification but does not guarantee functional identity between members of the same taxon or lack of similarity between different taxa. Using over thirteen hundred sequenced bacterial genomes, we built a novel function-based microorganism classification scheme, functional-repertoire similarity-based organism network (FuSiON; flattened to fusion). Our scheme is phenetic, based on a network of quantitatively defined organism relationships across the known prokaryotic space. It correlates significantly with the current taxonomy, but the observed discrepancies reveal both (1) the inconsistency of functional diversity levels among different taxa and (2) an (unsurprising) bias towards prioritizing, for classification purposes, relatively minor traits of particular interest to humans. Our dynamic network-based organism classification is independent of the arbitrary pairwise organism similarity cut-offs traditionally applied to establish taxonomic identity. Instead, it reveals natural, functionally defined organism groupings and is thus robust in handling organism diversity. Additionally, fusion can use organism meta-data to highlight the specific environmental factors that drive microbial diversification. Our approach provides a complementary view to cladistic assignments and holds important clues for further exploration of microbial lifestyles. 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subjects | Bacteria - classification Bacteria - genetics Classification Classification - methods Computational Biology - methods Electric power Engineering Sciences Gene expression Genetic aspects Genetic research Genome, Bacterial - physiology Genomes Microbial colonies Microbiological research Microorganisms Organisms Phylogenetics Quantitative trait loci Software Taxonomy Vocabularies & taxonomies |
title | Functional Basis of Microorganism Classification |
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