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• {"link"=>{"url"=>"http://www.citeulike.org/user/accopeland/article/12616034"}, "post_time"=>"2013-09-11 21:20:21", "tag"=>["16s", "clustering"], "linkout"=>{"type"=>"DOI", "url"=>"http://dx.doi.org/10.1371/journal.pone.0062623"}, "username"=>"accopeland", "article_id"=>"12616034"}

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{"title"=>"CLUSTOM: A Novel Method for Clustering 16S rRNA Next Generation Sequences by Overlap Minimization", "type"=>"journal", "authors"=>[{"first_name"=>"Kyuin", "last_name"=>"Hwang", "scopus_author_id"=>"55674299000"}, {"first_name"=>"Jeongsu", "last_name"=>"Oh", "scopus_author_id"=>"23478094100"}, {"first_name"=>"Tae Kyung", "last_name"=>"Kim", "scopus_author_id"=>"53363557200"}, {"first_name"=>"Byung Kwon", "last_name"=>"Kim", "scopus_author_id"=>"57199939761"}, {"first_name"=>"Dong Su", "last_name"=>"Yu", "scopus_author_id"=>"56527561300"}, {"first_name"=>"Bo Kyeng", "last_name"=>"Hou", "scopus_author_id"=>"22950841300"}, {"first_name"=>"Gustavo", "last_name"=>"Caetano-Anollés", "scopus_author_id"=>"56269658000"}, {"first_name"=>"Soon Gyu", "last_name"=>"Hong", "scopus_author_id"=>"7405764719"}, {"first_name"=>"Kyung Mo", "last_name"=>"Kim", "scopus_author_id"=>"36101192800"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "pui"=>"368847445", "sgr"=>"84877068120", "doi"=>"10.1371/journal.pone.0062623", "scopus"=>"2-s2.0-84877068120", "pmid"=>"23650520"}, "id"=>"9d4f3449-49c9-32c1-acad-1cabbbb6c771", "abstract"=>"The recent nucleic acid sequencing revolution driven by shotgun and high-throughput technologies has led to a rapid increase in the number of sequences for microbial communities. The availability of 16S ribosomal RNA (rRNA) gene sequences from a multitude of natural environments now offers a unique opportunity to study microbial diversity and community structure. The large volume of sequencing data however makes it time consuming to assign individual sequences to phylotypes by searching them against public databases. Since ribosomal sequences have diverged across prokaryotic species, they can be grouped into clusters that represent operational taxonomic units. However, available clustering programs suffer from overlap of sequence spaces in adjacent clusters. In natural environments, gene sequences are homogenous within species but divergent between species. This evolutionary constraint results in an uneven distribution of genetic distances of genes in sequence space. To cluster 16S rRNA sequences more accurately, it is therefore essential to select core sequences that are located at the centers of the distributions represented by the genetic distance of sequences in taxonomic units. Based on this idea, we here describe a novel sequence clustering algorithm named CLUSTOM that minimizes the overlaps between adjacent clusters. The performance of this algorithm was evaluated in a comparative exercise with existing programs, using the reference sequences of the SILVA database as well as published pyrosequencing datasets. The test revealed that our algorithm achieves higher accuracy than ESPRIT-Tree and mothur, few of the best clustering algorithms. Results indicate that the concept of an uneven distribution of sequence distances can effectively and successfully cluster 16S rRNA gene sequences. The algorithm of CLUSTOM has been implemented both as a web and as a standalone command line application, which are available at http://clustom.kribb.re.kr.", "link"=>"http://www.mendeley.com/research/clustom-novel-method-clustering-16s-rrna-next-generation-sequences-overlap-minimization", "reader_count"=>77, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>2, "Researcher"=>28, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>18, "Student > Postgraduate"=>1, "Student > Master"=>9, "Other"=>2, "Student > Bachelor"=>11, "Lecturer"=>1, "Professor"=>3}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>2, "Researcher"=>28, "Student > Doctoral Student"=>2, "Student > Ph. D. 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• http://doi.org/10.1111/mec.12909
• http://doi.org/10.1089/cmb.2015.0214
• http://doi.org/10.5194/bg-15-7419-2018
• http://doi.org/10.1371/journal.pone.0151064
• http://doi.org/10.1093/bioinformatics/btu085
• http://doi.org/10.5141/ecoenv.2015.049
• http://doi.org/10.7845/kjm.2016.6005
• http://doi.org/10.3389/fmicb.2018.02909
• http://doi.org/10.1128/CMR.00049-14
• http://doi.org/10.1038/srep09743
• http://doi.org/10.4137/BBI.S12462

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• 4783016
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• 4649890

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• {"id"=>"335394376945631232", "text"=>"CLUSTOM: A Novel Method for Clustering 16S rRNA #NGS reads by Overlap Minimization http://t.co/Lel0ZrFoPZ #metagenomics #bioinformatics", "created_at"=>"2013-05-17T14:00:25Z", "user"=>"genetics_blog", "user_name"=>"Stephen Turner", "user_profile_image"=>"http://a0.twimg.com/profile_images/1356990961/pic2-square-cropped-350x350_normal.jpg"}
• {"id"=>"335413441508544512", "text"=>"RT @genetics_blog: CLUSTOM: A Novel Method for Clustering 16S rRNA #NGS reads by Overlap Minimization http://t.co/Lel0ZrFoPZ #metagenomics …", "created_at"=>"2013-05-17T15:16:10Z", "user"=>"OmicsOmicsBlog", "user_name"=>"Keith Robison", "user_profile_image"=>"http://a0.twimg.com/profile_images/2899746265/80f20d2462b64c949da5202c3c44d13e_normal.jpeg"}
• {"id"=>"339474988870557696", "text"=>"CLUSTOM: A Novel Method for Clustering 16S rRNA Next Generation Sequences by Overlap Minimization http://t.co/jkjHWueqzj via @PLOSONE", "created_at"=>"2013-05-28T20:15:19Z", "user"=>"Dr_Bik", "user_name"=>"Holly Bik", "user_profile_image"=>"http://a0.twimg.com/profile_images/1389336556/0167_normal.png"}
• {"id"=>"339477267463946240", "text"=>"RT @Dr_Bik: CLUSTOM: A Novel Method for Clustering 16S rRNA Next Generation Sequences by Overlap Minimization http://t.co/jkjHWueqzj via @P…", "created_at"=>"2013-05-28T20:24:22Z", "user"=>"JChrisPires", "user_name"=>"J. Chris Pires", "user_profile_image"=>"http://a0.twimg.com/profile_images/1416048607/jcp_2011_normal.jpg"}
• {"id"=>"339570129031544832", "text"=>"RT @Dr_Bik: CLUSTOM: A Novel Method for Clustering 16S rRNA Next Generation Sequences by Overlap Minimization http://t.co/jkjHWueqzj via @P…", "created_at"=>"2013-05-29T02:33:22Z", "user"=>"GuiomeNicolas", "user_name"=>"Guillaume Nicolas", "user_profile_image"=>"http://a0.twimg.com/profile_images/1999642272/IMG_1153_normal.JPG"}
• {"id"=>"364416311058640896", "text"=>"CLUSTOM: A Novel Method for Clustering 16S rRNA Next Generation Sequences by Overlap Minimization http://t.co/IkhmxwjfUy via @PLOSOne", "created_at"=>"2013-08-05T16:03:13Z", "user"=>"Dr_Bik", "user_name"=>"Holly Bik", "user_profile_image"=>"http://a0.twimg.com/profile_images/1389336556/0167_normal.png"}
• {"id"=>"364417562068525056", "text"=>"RT @Dr_Bik: CLUSTOM: A Novel Method for Clustering 16S rRNA Next Generation Sequences by Overlap Minimization http://t.co/IkhmxwjfUy via @P…", "created_at"=>"2013-08-05T16:08:12Z", "user"=>"Coralbark1", "user_name"=>"Sheila Mapes", "user_profile_image"=>"http://a0.twimg.com/profile_images/1229101248/SDC10019_normal.JPG"}
• {"id"=>"364418136579121152", "text"=>"RT @Dr_Bik: CLUSTOM: A Novel Method for Clustering 16S rRNA Next Generation Sequences by Overlap Minimization http://t.co/IkhmxwjfUy via @P…", "created_at"=>"2013-08-05T16:10:29Z", "user"=>"Nod_of_Science", "user_name"=>"Fouad B.", "user_profile_image"=>"http://a0.twimg.com/profile_images/2827403464/172202a1ef66eae01d19bfad530baa19_normal.png"}
• {"id"=>"364429878143090688", "text"=>"RT @Dr_Bik: CLUSTOM: A Novel Method for Clustering 16S rRNA Next Generation Sequences by Overlap Minimization http://t.co/IkhmxwjfUy via @P…", "created_at"=>"2013-08-05T16:57:08Z", "user"=>"jcoolon", "user_name"=>"Joseph Coolon", "user_profile_image"=>"http://a0.twimg.com/profile_images/378800000021416267/4773310d19a9e831eaf90d9f25c6c231_normal.jpeg"}
• {"id"=>"364438174350577664", "text"=>"#microbiome CLUSTOM: A Novel Method for Clustering 16S rRNA Sequences by Overlap Minimization http://t.co/Sqs6OJKoNr v @Dr_Bik #PLOSOne", "created_at"=>"2013-08-05T17:30:06Z", "user"=>"SahaSurya", "user_name"=>"Surya Saha", "user_profile_image"=>"http://a0.twimg.com/profile_images/2373458683/geyyrbspp0bgqxx2tc56_normal.jpeg"}
• {"id"=>"364440255245864960", "text"=>"RT @SahaSurya: #microbiome CLUSTOM: A Novel Method for Clustering 16S rRNA Sequences by Overlap Minimization http://t.co/Sqs6OJKoNr v @Dr_B…", "created_at"=>"2013-08-05T17:38:22Z", "user"=>"MikeyJ", "user_name"=>"Mike Cox", "user_profile_image"=>"http://a0.twimg.com/profile_images/325403957/avatarsid_normal.jpg"}
• {"id"=>"458925815518871553", "text"=>"CLUSTOM http://t.co/h53bNGvD5r 塩基配列クラスタリングの新しいアルゴリズムだそうだ。", "created_at"=>"2014-04-23T11:10:15Z", "user"=>"windowmoon", "user_name"=>"windowmoon", "user_profile_image"=>"http://pbs.twimg.com/profile_images/435807472121438208/krJEK1Sw_normal.png"}

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• {"files"=>["https://ndownloader.figshare.com/files/1050944"], "description"=>"<p>The diagram summarizes the flow of the CLUSTOM algorithm. There are five main steps that are processed sequentially. (A) <i>Random sample</i>: Here we assume that the input sequences (individual letters) are clustered into three OTUs, labeled in green, red, and black. A sequence subset is randomly extracted from these sequences. (B) <i>k-mer threshold determination</i>: Individual dots indicate pairs of the randomly sampled sequences. Distances of <i>k</i>-mer and Needleman-Wunsch (NW) between sequences <i>i</i> and <i>j</i> are denoted by <i>k_i,j</i> and <i>d_i,j</i>, respectively. The user-defined distance threshold and its corresponding <i>k</i>-mer threshold are respectively denoted by α and β, respectively. (C) <i>Initial clustering</i>: If <i>k</i>-mer distances of any two of the input sequences are smaller than the <i>k</i>-mer threshold (β), they are connected in a network. The larger letters in bold indicate the seed sequences of initial clusters that are bound by circles. (D) <i>Refinement</i>: Seed sequences with NW distances smaller than the user-defined threshold (α) are used to construct a refined network following the procedures in (C). The larger letters in bold indicate the refined seed sequences. (E) <i>Recovery</i>: Each of the final clusters (circles) consists of the refined seed, its neighbors, and sequences that are directly connected to the refined seed or the neighbors in the <i>initial clustering</i> step.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "metagenomics", "Sequence analysis", "ecology", "microbial ecology", "Evolutionary biology", "Evolutionary systematics", "phylogenetics", "genetics", "Molecular genetics", "population genetics", "microbiology", "algorithms", "Computer applications", "Web-based applications", "Applied mathematics", "algorithmic"], "article_id"=>695763, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences"], "users"=>["Kyuin Hwang", "Jeongsu Oh", "Tae-Kyung Kim", "Byung Kwon Kim", "Dong Su Yu", "Bo Kyeng Hou", "Gustavo Caetano-Anollés", "Soon Gyu Hong", "Kyung Mo Kim"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0062623.g001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Schematic_representation_of_the_algorithmic_workflow_/695763", "title"=>"Schematic representation of the algorithmic workflow.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-05-01 01:36:03"}
• {"files"=>["https://ndownloader.figshare.com/files/1050946"], "description"=>"<p>Plots show the uneven distribution of 16S sequences visualized in sequence space. Coordinates are expressed in units of NW distances of sequence pairs. (A) PCA analysis of 1,000 sequences randomly sampled from the 16S–SILVA dataset showing the relationships of NW distances. (B) PCA analysis of the sequences of ten species that were the most abundantly present in the set of the 1,000 sequences displayed in (A).</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "metagenomics", "Sequence analysis", "ecology", "microbial ecology", "Evolutionary biology", "Evolutionary systematics", "phylogenetics", "genetics", "Molecular genetics", "population genetics", "microbiology", "algorithms", "Computer applications", "Web-based applications", "Applied mathematics", "Cartography", "16s"], "article_id"=>695765, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences"], "users"=>["Kyuin Hwang", "Jeongsu Oh", "Tae-Kyung Kim", "Byung Kwon Kim", "Dong Su Yu", "Bo Kyeng Hou", "Gustavo Caetano-Anollés", "Soon Gyu Hong", "Kyung Mo Kim"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0062623.g002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Genetic_cartography_of_16S_sequence_distances_/695765", "title"=>"Genetic cartography of 16S sequence distances.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-05-01 01:36:05"}
• {"files"=>["https://ndownloader.figshare.com/files/1050947"], "description"=>"<p>We simulated three variable regions of the 454–HMP dataset (V1–3, V3–5, and V6–9) to determine optimal sizes of <i>k</i>-mer and random sample. (A) From each region, 10 K reads were randomly sampled 10 times and NW distances calculated. <i>k</i>-mer distances between the pairs of the 10 K reads were calculated for each of <i>k</i>-mer sizes ranging from 3 to 15. The extent of linear regression between the two distance variables was plotted using the mean (upper <i>x</i> axis) and standard deviation (lower <i>x</i> axis) of the square of the Person product-moment correlation coefficients (R-Square) at every point of the <i>k</i>-mer sizes. (B) For individual 16S regions, we randomly sampled sequences of 100 to 2,000 reads in increments of 100 reads. The <i>k</i>-mer and NW distances were calculated for all possible sequence pairs at every point of the random sample sizes. The random sampling followed by the distance calculation was repeated 10 times. For ten random samples per sample size, the mean (upper <i>x</i> axis) and standard deviation (lower <i>x</i> axis) of R-Square were calculated and displayed.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "metagenomics", "Sequence analysis", "ecology", "microbial ecology", "Evolutionary biology", "Evolutionary systematics", "phylogenetics", "genetics", "Molecular genetics", "population genetics", "microbiology", "algorithms", "Computer applications", "Web-based applications", "Applied mathematics"], "article_id"=>695766, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences"], "users"=>["Kyuin Hwang", "Jeongsu Oh", "Tae-Kyung Kim", "Byung Kwon Kim", "Dong Su Yu", "Bo Kyeng Hou", "Gustavo Caetano-Anollés", "Soon Gyu Hong", "Kyung Mo Kim"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0062623.g003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Parameter_optimization_/695766", "title"=>"Parameter optimization.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-05-01 01:36:06"}
• {"files"=>["https://ndownloader.figshare.com/files/1050948"], "description"=>"<p>The clustering results of CLUSTOM, NW-DOTUR-AL, ESPRIT-Tree, NAST-mothur-AL and NW-mothur-AL were evaluated using the 16S–SILVA sequences. (A–B): The number of clustered OTUs was examined at the species (3% dissimilarity; A) and genus (5%; B) levels for every OTU size (number of sequences in a cluster). (C–D): The precision and recall statistics as well as their harmonic mean (<i>F₂</i>) <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0062623#pone.0062623-Wittkop1\" target=\"_blank\">[25]</a> were used to evaluate clustering results of the five programs at the two taxonomic levels.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "metagenomics", "Sequence analysis", "ecology", "microbial ecology", "Evolutionary biology", "Evolutionary systematics", "phylogenetics", "genetics", "Molecular genetics", "population genetics", "microbiology", "algorithms", "Computer applications", "Web-based applications", "Applied mathematics"], "article_id"=>695767, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences"], "users"=>["Kyuin Hwang", "Jeongsu Oh", "Tae-Kyung Kim", "Byung Kwon Kim", "Dong Su Yu", "Bo Kyeng Hou", "Gustavo Caetano-Anollés", "Soon Gyu Hong", "Kyung Mo Kim"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0062623.g004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Taxonomy_based_accuracy_evaluation_/695767", "title"=>"Taxonomy-based accuracy evaluation.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-05-01 01:36:07"}
• {"files"=>["https://ndownloader.figshare.com/files/1050949"], "description"=>"<p>We ran CLUSTOM, NW-DOTUR-AL, ESPRIT-Tree, NAST-mothur-AL and NW-mothur-AL using the <i>R. ramose</i> (complex community), Seawater (intermediate), and <i>S. stuposa</i> (simple) datasets of 454–SPONGE at the species (16S distance of 3%) and genus (5%) levels. The clustering accuracy of the five programs was evaluated for each of dataset and taxonomic level with the following statistics: (A) total numbers of OTUs; (B) false conjunction rates; and (C) false disjunction rates. The definition and formulas of the false rates are described in MATERIALS AND METHODS.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "metagenomics", "Sequence analysis", "ecology", "microbial ecology", "Evolutionary biology", "Evolutionary systematics", "phylogenetics", "genetics", "Molecular genetics", "population genetics", "microbiology", "algorithms", "Computer applications", "Web-based applications", "Applied mathematics"], "article_id"=>695768, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences"], "users"=>["Kyuin Hwang", "Jeongsu Oh", "Tae-Kyung Kim", "Byung Kwon Kim", "Dong Su Yu", "Bo Kyeng Hou", "Gustavo Caetano-Anollés", "Soon Gyu Hong", "Kyung Mo Kim"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0062623.g005"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Distance_based_accuracy_evaluation_/695768", "title"=>"Distance-based accuracy evaluation.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-05-01 01:36:08"}
• {"files"=>["https://ndownloader.figshare.com/files/1050951"], "description"=>"<p>The running times of CLUSTOM were measured using the 454–HMP dataset with a varying number of sequences (10 K, 20 K, 50 K, 100 K, and 200 K). The CLUSTOM algorithm exhibits a polynomial computational complexity of <i>O(N²)</i> when calculating <i>k</i>-mer distances between every pair of input sequences.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "metagenomics", "Sequence analysis", "ecology", "microbial ecology", "Evolutionary biology", "Evolutionary systematics", "phylogenetics", "genetics", "Molecular genetics", "population genetics", "microbiology", "algorithms", "Computer applications", "Web-based applications", "Applied mathematics"], "article_id"=>695770, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences"], "users"=>["Kyuin Hwang", "Jeongsu Oh", "Tae-Kyung Kim", "Byung Kwon Kim", "Dong Su Yu", "Bo Kyeng Hou", "Gustavo Caetano-Anollés", "Soon Gyu Hong", "Kyung Mo Kim"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0062623.g006"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Efficiency_test_/695770", "title"=>"Efficiency test.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-05-01 01:36:10"}

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Relative Metric

{"start_date"=>"2013-01-01T00:00:00Z", "end_date"=>"2013-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Physical sciences", "average_usage"=>[254, 431, 547, 651, 748, 842, 932, 1017, 1098, 1178, 1259, 1336, 1404]}, {"subject_area"=>"/Physical sciences/Mathematics", "average_usage"=>[259, 431, 541, 639, 727, 816, 898, 980, 1061, 1136, 1214, 1294, 1356]}]}

F1000Prime