Metagenomic Annotation Networks: Construction and Applications
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{"title"=>"Metagenomic annotation networks: Construction and applications", "type"=>"journal", "authors"=>[{"first_name"=>"Gregory", "last_name"=>"Vey", "scopus_author_id"=>"36125723200"}, {"first_name"=>"Gabriel", "last_name"=>"Moreno-Hagelsieb", "scopus_author_id"=>"6602462541"}], "year"=>2012, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "isbn"=>"1932-6203 (Electronic)\r1932-6203 (Linking)", "pui"=>"365398609", "sgr"=>"84864683903", "doi"=>"10.1371/journal.pone.0041283", "scopus"=>"2-s2.0-84864683903", "pmid"=>"22879885"}, "id"=>"3bbf9ec0-3ea0-35ad-9244-89a0a81ef4af", "abstract"=>"The derivation and comparison of biological interaction networks are vital for understanding the functional capacity and hierarchical organization of integrated microbial communities. In the current work we present metagenomic annotation networks as a novel taxonomy-free approach for understanding the functional architecture of metagenomes. Specifically, metagenomic operon predictions are exploited to derive functional interactions that are translated and categorized according to their associated functional annotations. The result is a collection of discrete networks of weighted annotation linkages. These networks are subsequently examined for the occurrence of annotation modules that portray the functional and organizational characteristics of various microbial communities. A variety of network perspectives and annotation categories are applied to recover a diverse range of modules with different degrees of annotative cohesiveness. Applications to biocatalyst discovery and human health issues are discussed, as well as the limitations of the current implementation.", "link"=>"http://www.mendeley.com/research/metagenomic-annotation-networks-construction-applications", "reader_count"=>94, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>5, "Researcher"=>21, "Student > Doctoral Student"=>5, "Student > Ph. D. Student"=>31, "Student > Postgraduate"=>5, "Student > Master"=>11, "Other"=>2, "Student > Bachelor"=>7, "Professor"=>6}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>5, "Researcher"=>21, "Student > Doctoral Student"=>5, "Student > Ph. D. Student"=>31, "Student > Postgraduate"=>5, "Student > Master"=>11, "Other"=>2, "Student > Bachelor"=>7, "Professor"=>6}, "reader_count_by_subject_area"=>{"Unspecified"=>4, "Engineering"=>2, "Environmental Science"=>1, "Biochemistry, Genetics and Molecular Biology"=>6, "Mathematics"=>2, "Agricultural and Biological Sciences"=>73, "Medicine and Dentistry"=>2, "Computer Science"=>3, "Economics, Econometrics and Finance"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>2}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>2}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>73}, "Computer Science"=>{"Computer Science"=>3}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>6}, "Mathematics"=>{"Mathematics"=>2}, "Unspecified"=>{"Unspecified"=>4}, "Environmental Science"=>{"Environmental Science"=>1}}, "reader_count_by_country"=>{"United States"=>7, "Switzerland"=>1, "Portugal"=>1, "India"=>1, "Canada"=>1, "Netherlands"=>1, "Sweden"=>1, "Norway"=>1, "Ireland"=>1, "Denmark"=>1, "Brazil"=>3, "Mexico"=>1, "France"=>1, "Estonia"=>1}, "group_count"=>5}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/595451"], "description"=>"<p>The general features of the highest scoring functional module from each network are shown including the source network, the score assigned by MINE, the number of nodes and edges that compose the module, and the member annotations derived from the nodes. Member annotations are sorted in ascending lexicographical order and delimited using the semicolon symbol. For all top ranked modules each member is unique.</p>", "links"=>[], "tags"=>["ranked"], "article_id"=>265950, "categories"=>["Biological Sciences", "Genetics", "Microbiology"], "users"=>["Gregory Vey", "Gabriel Moreno-Hagelsieb"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0041283.t002", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Top_Ranked_Functional_Modules_/265950", "title"=>"Top Ranked Functional Modules.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-08-07 01:39:10"}
  • {"files"=>["https://ndownloader.figshare.com/files/594607"], "description"=>"<p>The relative proportions (%) of various data source types that were used are shown categorized according to IMG/M microbiome taxa at the class level. Panel A shows the proportions (%) with respect to the total number of data sets while Panel B shows the proportions (%) with respect to the total number of genes.</p>", "links"=>[], "tags"=>["genetics and genomics", "microbiology", "Computational biology"], "article_id"=>265107, "categories"=>["Biological Sciences", "Genetics", "Microbiology"], "users"=>["Gregory Vey", "Gabriel Moreno-Hagelsieb"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0041283.g001", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Data_Source_Diversity_/265107", "title"=>"Data Source Diversity.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-08-07 01:25:07"}
  • {"files"=>["https://ndownloader.figshare.com/files/311118", "https://ndownloader.figshare.com/files/311138"], "description"=>"<div><p>The derivation and comparison of biological interaction networks are vital for understanding the functional capacity and hierarchical organization of integrated microbial communities. In the current work we present metagenomic annotation networks as a novel taxonomy-free approach for understanding the functional architecture of metagenomes. Specifically, metagenomic operon predictions are exploited to derive functional interactions that are translated and categorized according to their associated functional annotations. The result is a collection of discrete networks of weighted annotation linkages. These networks are subsequently examined for the occurrence of annotation modules that portray the functional and organizational characteristics of various microbial communities. A variety of network perspectives and annotation categories are applied to recover a diverse range of modules with different degrees of annotative cohesiveness. Applications to biocatalyst discovery and human health issues are discussed, as well as the limitations of the current implementation.</p> </div>", "links"=>[], "tags"=>["metagenomic", "annotation", "applications"], "article_id"=>121370, "categories"=>["Biological Sciences", "Genetics", "Microbiology"], "users"=>["Gregory Vey", "Gabriel Moreno-Hagelsieb"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0041283.s001", "https://dx.doi.org/10.1371/journal.pone.0041283.s002"], "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Metagenomic_Annotation_Networks_Construction_and_Applications/121370", "title"=>"Metagenomic Annotation Networks: Construction and Applications", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2012-08-07 00:22:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/595121"], "description"=>"<p>The annotative themes for the top ranked MetaCyc module are depicted where the numeric values indicate the number of annotations belonging to a thematic category. Specifically, amino acid categories are represented vertically and metabolic categories are represented horizontally. Note, the vertical themes are encapsulatory while the horizontal themes are mutually exclusive. A variety of functional perspectives can be simultaneously visualized by way of the interacting and overlapping thematic sets.</p>", "links"=>[], "tags"=>["genetics and genomics", "microbiology", "Computational biology"], "article_id"=>265620, "categories"=>["Biological Sciences", "Genetics", "Microbiology"], "users"=>["Gregory Vey", "Gabriel Moreno-Hagelsieb"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0041283.g005", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Thematic_Set_Diagram_/265620", "title"=>"Thematic Set Diagram.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-08-07 01:33:40"}
  • {"files"=>["https://ndownloader.figshare.com/files/594961"], "description"=>"<p>The target perspective networks for cellulase functional interactions are shown where large node diameter represents high node degree within each respective network. Panel A shows a network constructed using MetaCyc annotations with a highly connected central hub having the annotation PWY-1001: cellulose biosynthesis. The highlighted nodes represent the top ranking module which is enlarged in Panel B. Panel C shows a network constructed using COG annotations and features a highly connected central hub with the annotation COG1363: cellulase M and related proteins. The highlighted nodes represent the top ranking module which is enlarged in Panel D.</p>", "links"=>[], "tags"=>["cellulase"], "article_id"=>265455, "categories"=>["Biological Sciences", "Genetics", "Microbiology"], "users"=>["Gregory Vey", "Gabriel Moreno-Hagelsieb"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0041283.g004", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Metagenomic_Cellulase_Networks_/265455", "title"=>"Metagenomic Cellulase Networks.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-08-07 01:30:55"}
  • {"files"=>["https://ndownloader.figshare.com/files/594777"], "description"=>"<p>Four polyketide target perspective networks were constructed with progressively increasing target stringency and each network was translated into each of the four annotation categories. The proportion of nodes and edges in each polyketide network was compared to its corresponding overall network. Panel A shows that coverage for nodes decreased for all annotation categories with increasing target stringency and Panel B shows that coverage for edges also decreased for all annotation categories with increasing target stringency.</p>", "links"=>[], "tags"=>["stringency"], "article_id"=>265277, "categories"=>["Biological Sciences", "Genetics", "Microbiology"], "users"=>["Gregory Vey", "Gabriel Moreno-Hagelsieb"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0041283.g003", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Target_Stringency_versus_Network_Coverage_/265277", "title"=>"Target Stringency versus Network Coverage.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-08-07 01:27:57"}
  • {"files"=>["https://ndownloader.figshare.com/files/595227"], "description"=>"<p>The source perspective networks for human gut functional interactions are shown where large node diameter represents high node degree within each respective network. Panel A shows a network constructed using KEGG annotations where the highlighted nodes represent the top ranking module which is enlarged in Panel B. Panel C shows a network constructed using TIGRFAM annotations where the highlighted nodes represent the top ranking module which is enlarged in Panel D.</p>", "links"=>[], "tags"=>["genetics and genomics", "microbiology", "Computational biology"], "article_id"=>265720, "categories"=>["Biological Sciences", "Genetics", "Microbiology"], "users"=>["Gregory Vey", "Gabriel Moreno-Hagelsieb"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0041283.g006", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Human_Gut_Networks_/265720", "title"=>"Human Gut Networks.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-08-07 01:35:20"}
  • {"files"=>["https://ndownloader.figshare.com/files/595344"], "description"=>"<p>The comparative networks for human gut functional interactions are shown where large node diameter represents high node degree within each respective network. Specifically, two networks were constructed using TIGRFAM annotations and compared for mutual versus exclusive nodes. Panel A shows the intersection of the networks where the highlighted nodes represent the top ranking module which is enlarged in Panel B. Panel C shows the difference of the networks where the highlighted nodes represent the top ranking module which is enlarged in Panel D.</p>", "links"=>[], "tags"=>["genetics and genomics", "microbiology", "Computational biology"], "article_id"=>265843, "categories"=>["Biological Sciences", "Genetics", "Microbiology"], "users"=>["Gregory Vey", "Gabriel Moreno-Hagelsieb"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0041283.g007", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparative_Gut_Networks_/265843", "title"=>"Comparative Gut Networks.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-08-07 01:37:23"}
  • {"files"=>["https://ndownloader.figshare.com/files/594692"], "description"=>"<p>Operonic genes are predicted on the basis of co-direction and intergenic proximity using scaffolds containing more than one gene (Panel A). Operons and their constituent genes can be filtered according to the presence or absence of a target annotation such that at least one member of an operon is required to possess a target descriptor (Panel B). Note that the filter step is optional and can applied to obtain target perspective networks while being omitted in the construction of source perspective networks. Each gene in a given operon is mined for its various types of functional annotations where any particular type has a domain of existing values (Panel C). For each operon, the obtained functional annotations are used to infer bidirectional functional interactions for annotations having the same type but different values (Panel D). Note that interactions are inferred directly for immediately adjacent gene pairs and also transitively for downstream members within the same operon.</p>", "links"=>[], "tags"=>["genetics and genomics", "microbiology", "Computational biology"], "article_id"=>265188, "categories"=>["Biological Sciences", "Genetics", "Microbiology"], "users"=>["Gregory Vey", "Gabriel Moreno-Hagelsieb"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0041283.g002", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Network_Construction_Workflow_/265188", "title"=>"Network Construction Workflow.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-08-07 01:26:28"}
  • {"files"=>["https://ndownloader.figshare.com/files/595477"], "description"=>"<p>The general features of each metagenomic functional network are shown including the type of network, the category of annotations used to construct the network, the network perspective, the number of nodes and edges that compose the network, and the number of predicted functional modules contained within the network.</p>", "links"=>[], "tags"=>["genetics and genomics", "microbiology", "Computational biology"], "article_id"=>265971, "categories"=>["Biological Sciences", "Genetics", "Microbiology"], "users"=>["Gregory Vey", "Gabriel Moreno-Hagelsieb"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0041283.t001", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Summary_of_Network_Features_/265971", "title"=>"Summary of Network Features.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-08-07 01:39:31"}

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Relative Metric

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