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Mendeley | Further Information

{"title"=>"Sequence similarity network reveals common ancestry of multidomain proteins", "type"=>"journal", "authors"=>[{"first_name"=>"Nan", "last_name"=>"Song", "scopus_author_id"=>"8889228200"}, {"first_name"=>"Jacob M.", "last_name"=>"Joseph", "scopus_author_id"=>"24343986900"}, {"first_name"=>"George B.", "last_name"=>"Davis", "scopus_author_id"=>"35458759100"}, {"first_name"=>"Dannie", "last_name"=>"Durand", "scopus_author_id"=>"7201541121"}], "year"=>2008, "source"=>"PLoS Computational Biology", "identifiers"=>{"pui"=>"351811549", "sgr"=>"44949207428", "issn"=>"1553734X", "pmid"=>"18475320", "scopus"=>"2-s2.0-44949207428", "doi"=>"10.1371/journal.pcbi.1000063", "isbn"=>"1553-7358 (Electronic)\\r1553-734X (Linking)"}, "id"=>"00ea8673-c1fc-35fb-a77b-14e32fee5645", "abstract"=>"We address the problem of homology identification in complex multidomain families with varied domain architectures. The challenge is to distinguish sequence pairs that share common ancestry from pairs that share an inserted domain but are otherwise unrelated. This distinction is essential for accuracy in gene annotation, function prediction, and comparative genomics. There are two major obstacles to multidomain homology identification: lack of a formal definition and lack of curated benchmarks for evaluating the performance of new methods. We offer preliminary solutions to both problems: 1) an extension of the traditional model of homology to include domain insertions; and 2) a manually curated benchmark of well-studied families in mouse and human. We further present Neighborhood Correlation, a novel method that exploits the local structure of the sequence similarity network to identify homologs with great accuracy based on the observation that gene duplication and domain shuffling leave distinct patterns in the sequence similarity network. In a rigorous, empirical comparison using our curated data, Neighborhood Correlation outperforms sequence similarity, alignment length, and domain architecture comparison. Neighborhood Correlation is well suited for automated, genome-scale analyses. It is easy to compute, does not require explicit knowledge of domain architecture, and classifies both single and multidomain homologs with high accuracy. Homolog predictions obtained with our method, as well as our manually curated benchmark and a web-based visualization tool for exploratory analysis of the network neighborhood structure, are available at http://www.neighborhoodcorrelation.org. Our work represents a departure from the prevailing view that the concept of homology cannot be applied to genes that have undergone domain shuffling. In contrast to current approaches that either focus on the homology of individual domains or consider only families with identical domain architectures, we show that homology can be rationally defined for multidomain families with diverse architectures by considering the genomic context of the genes that encode them. Our study demonstrates the utility of mining network structure for evolutionary information, suggesting this is a fertile approach for investigating evolutionary processes in the post-genomic era.", "link"=>"http://www.mendeley.com/research/sequence-similarity-network-reveals-common-ancestry-multidomain-proteins", "reader_count"=>121, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>12, "Researcher"=>33, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>36, "Student > Postgraduate"=>3, "Student > Master"=>13, "Other"=>1, "Student > Bachelor"=>9, "Lecturer"=>3, "Professor"=>8}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>12, "Researcher"=>33, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>36, "Student > Postgraduate"=>3, "Student > Master"=>13, "Other"=>1, "Student > Bachelor"=>9, "Lecturer"=>3, "Professor"=>8}, "reader_count_by_subject_area"=>{"Engineering"=>3, "Unspecified"=>1, "Biochemistry, Genetics and Molecular Biology"=>12, "Mathematics"=>3, "Agricultural and Biological Sciences"=>83, "Medicine and Dentistry"=>1, "Pharmacology, Toxicology and Pharmaceutical Science"=>1, "Chemistry"=>2, "Social Sciences"=>1, "Computer Science"=>13, "Immunology and Microbiology"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>3}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Chemistry"=>{"Chemistry"=>2}, "Social Sciences"=>{"Social Sciences"=>1}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>83}, "Computer Science"=>{"Computer Science"=>13}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>12}, "Mathematics"=>{"Mathematics"=>3}, "Unspecified"=>{"Unspecified"=>1}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>1}}, "reader_count_by_country"=>{"United States"=>5, "Japan"=>2, "United Kingdom"=>4, "Switzerland"=>2, "Spain"=>1, "Sweden"=>1, "Korea (South)"=>1, "Belgium"=>1, "Norway"=>1, "Luxembourg"=>1, "Brazil"=>2, "South Africa"=>1, "Italy"=>1, "Germany"=>4}, "group_count"=>5}

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• http://doi.org/10.1093/bioinformatics/btp207
• http://doi.org/10.1016/j.compbiomed.2013.06.001
• http://doi.org/10.1093/database/bax006
• http://doi.org/10.1093/bioinformatics/btw686
• http://doi.org/10.4137/GEI.S37925
• http://doi.org/10.1038/nrg2404
• http://doi.org/10.1186/s12859-019-2660-5
• http://doi.org/10.1021/acs.jproteome.5b01031
• http://doi.org/10.1093/bioinformatics/btt379
• http://doi.org/10.1093/molbev/msx283
• http://doi.org/10.1093/bioinformatics/btt049
• http://doi.org/10.1093/bioinformatics/bts098
• http://doi.org/10.1109/TCBB.2011.22
• http://doi.org/10.1186/1471-2105-12-90
• http://doi.org/10.1093/molbev/mst109
• http://doi.org/10.1186/s12859-015-0570-8
• http://doi.org/10.1039/c003507f
• http://doi.org/10.1371/journal.pone.0078383
• http://doi.org/10.1093/molbev/mst228
• http://doi.org/10.1073/pnas.1706849114
• http://doi.org/10.1186/1471-2105-10-S15-S5
• http://doi.org/10.1016/j.tim.2015.12.003
• http://doi.org/10.1007/s10822-009-9273-4
• http://doi.org/10.1093/gbe/evu168
• http://doi.org/10.1109/TCBB.2010.34
• http://doi.org/10.1021/es500918w
• http://doi.org/10.1093/bioinformatics/bts050
• http://doi.org/10.1093/nar/gkq699
• http://doi.org/10.1039/C0MB00182A
• http://doi.org/10.1186/s12918-016-0296-x
• http://doi.org/10.3390/genes2030516
• http://doi.org/10.1002/bies.080085
• http://doi.org/10.1186/s12862-016-0684-2
• http://doi.org/10.1186/s13059-017-1319-7
• http://doi.org/10.3390/genes2030578
• http://doi.org/10.1186/s12915-015-0125-5

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• {"month"=>"1", "year"=>"2015", "pdf_views"=>"9", "xml_views"=>"0", "html_views"=>"35"}
• {"month"=>"2", "year"=>"2015", "pdf_views"=>"4", "xml_views"=>"0", "html_views"=>"33"}
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• {"month"=>"8", "year"=>"2015", "pdf_views"=>"34", "xml_views"=>"0", "html_views"=>"41"}
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• {"month"=>"12", "year"=>"2016", "pdf_views"=>"7", "xml_views"=>"0", "html_views"=>"34"}
• {"month"=>"1", "year"=>"2017", "pdf_views"=>"7", "xml_views"=>"0", "html_views"=>"28"}
• {"month"=>"2", "year"=>"2017", "pdf_views"=>"7", "xml_views"=>"2", "html_views"=>"49"}
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• {"month"=>"10", "year"=>"2017", "pdf_views"=>"8", "xml_views"=>"2", "html_views"=>"58"}
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• {"month"=>"6", "year"=>"2018", "pdf_views"=>"4", "xml_views"=>"0", "html_views"=>"13"}
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• {"month"=>"7", "year"=>"2019", "pdf_views"=>"7", "xml_views"=>"0", "html_views"=>"4"}

Figshare

• {"files"=>["https://ndownloader.figshare.com/files/932541"], "description"=>"<p>Family and non-family matches are shown in blue and red, respectively. Matches with the Kinase <i>PRKG1B</i> and the non-Kinase <i>NCAM2</i> are indicated by magenta and green circles. Scores of matching sequences ranked by (A) Neighborhood Correlation score, (B) BLAST score, and (C) PSI-BLAST score.</p>", "links"=>[], "tags"=>["query"], "article_id"=>602992, "categories"=>["Medicine", "Evolutionary Biology", "Infectious Diseases"], "users"=>["Nan Song", "Jacob M. Joseph", "George B. Davis", "Dannie Durand"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000063.g005", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Rank_plots_for_the_query_sequence_PDGFRB_/602992", "title"=>"Rank plots for the query sequence <i>PDGFRB</i>.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2008-05-16 00:49:52"}
• {"files"=>["https://ndownloader.figshare.com/files/459099", "https://ndownloader.figshare.com/files/459179", "https://ndownloader.figshare.com/files/459288", "https://ndownloader.figshare.com/files/459341", "https://ndownloader.figshare.com/files/459413", "https://ndownloader.figshare.com/files/459477", "https://ndownloader.figshare.com/files/459551", "https://ndownloader.figshare.com/files/459573"], "description"=>"<div><p>We address the problem of homology identification in complex multidomain families with varied domain architectures. The challenge is to distinguish sequence pairs that share common ancestry from pairs that share an inserted domain but are otherwise unrelated. This distinction is essential for accuracy in gene annotation, function prediction, and comparative genomics. There are two major obstacles to multidomain homology identification: lack of a formal definition and lack of curated benchmarks for evaluating the performance of new methods. We offer preliminary solutions to both problems: 1) an extension of the traditional model of homology to include domain insertions; and 2) a manually curated benchmark of well-studied families in mouse and human. We further present Neighborhood Correlation, a novel method that exploits the local structure of the sequence similarity network to identify homologs with great accuracy based on the observation that gene duplication and domain shuffling leave distinct patterns in the sequence similarity network. In a rigorous, empirical comparison using our curated data, Neighborhood Correlation outperforms sequence similarity, alignment length, and domain architecture comparison. Neighborhood Correlation is well suited for automated, genome-scale analyses. It is easy to compute, does not require explicit knowledge of domain architecture, and classifies both single and multidomain homologs with high accuracy. Homolog predictions obtained with our method, as well as our manually curated benchmark and a web-based visualization tool for exploratory analysis of the network neighborhood structure, are available at <a href=\"http://www.neighborhoodcorrelation.org\">http://www.neighborhoodcorrelation.org</a>. Our work represents a departure from the prevailing view that the concept of homology cannot be applied to genes that have undergone domain shuffling. In contrast to current approaches that either focus on the homology of individual domains or consider only families with identical domain architectures, we show that homology can be rationally defined for multidomain families with diverse architectures by considering the genomic context of the genes that encode them. Our study demonstrates the utility of mining network structure for evolutionary information, suggesting this is a fertile approach for investigating evolutionary processes in the post-genomic era.</p></div>", "links"=>[], "tags"=>["reveals", "ancestry", "multidomain", "proteins"], "article_id"=>150459, "categories"=>["Medicine", "Evolutionary Biology", "Cancer"], "users"=>["Nan Song", "Jacob M. Joseph", "George B. Davis", "Dannie Durand"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1000063.s001", "https://dx.doi.org/10.1371/journal.pcbi.1000063.s002", "https://dx.doi.org/10.1371/journal.pcbi.1000063.s003", "https://dx.doi.org/10.1371/journal.pcbi.1000063.s004", "https://dx.doi.org/10.1371/journal.pcbi.1000063.s005", "https://dx.doi.org/10.1371/journal.pcbi.1000063.s006", "https://dx.doi.org/10.1371/journal.pcbi.1000063.s007", "https://dx.doi.org/10.1371/journal.pcbi.1000063.s008"], "stats"=>{"downloads"=>11, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Sequence_Similarity_Network_Reveals_Common_Ancestry_of_Multidomain_Proteins/150459", "title"=>"Sequence Similarity Network Reveals Common Ancestry of Multidomain Proteins", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2008-05-16 00:07:39"}
• {"files"=>["https://ndownloader.figshare.com/files/932444"], "description"=>"<p>Network neighborhoods in which nodes represent sequences. Edges connect pairs with significant sequence similarity. Edge weights reflecting degree of sequence similarity are not shown. (A) The neighborhoods of the homologous pair, <i>PDGFRB</i> and <i>PRKG1B</i>. <i>PDGFRB</i> and <i>PRKG1B</i> share 779 neighbors, mostly Kinases (turquoise nodes). These are strong matches due to a shared kinase domain. <i>PDGFRB</i> has 183 unique neighbors, mostly due to weak matches with Ig domains (green nodes). <i>PRKG1B</i> has 142 unique neighbors due to weak matches with the cNMP-binding domain (red nodes). Other matching sequences are shown in yellow. (B) <i>PDGFRB</i> and <i>NCAM2</i>, a domain-only match, have 232 matches in common. <i>PDGFRB</i> has 730 unique neighbors and <i>NCAM2</i> has 240, mostly due to Fn3 domains (dark blue nodes).</p>", "links"=>[], "tags"=>["differences", "evolutionary"], "article_id"=>602892, "categories"=>["Medicine", "Evolutionary Biology", "Infectious Diseases"], "users"=>["Nan Song", "Jacob M. Joseph", "George B. Davis", "Dannie Durand"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000063.g004", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Differences_in_neighborhood_structure_of_the_sequence_similarity_network_reflect_differences_in_evolutionary_history_/602892", "title"=>"Differences in neighborhood structure of the sequence similarity network reflect differences in evolutionary history.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2008-05-16 00:48:12"}
• {"files"=>["https://ndownloader.figshare.com/files/932605"], "description"=>"<p>Family and non-family matches are shown in blue and red, respectively. (A) Neighborhood Correlation scores, (B) BLAST scores, and (C) PSI-BLAST scores.</p>", "links"=>[], "tags"=>["scores", "non-family", "pairs", "kinase"], "article_id"=>603049, "categories"=>["Medicine", "Evolutionary Biology", "Infectious Diseases"], "users"=>["Nan Song", "Jacob M. Joseph", "George B. Davis", "Dannie Durand"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000063.g006", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Distribution_of_scores_for_all_family_and_non_family_pairs_in_the_Kinase_family_/603049", "title"=>"Distribution of scores for all family and non-family pairs in the Kinase family.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2008-05-16 00:50:49"}
• {"files"=>["https://ndownloader.figshare.com/files/932269"], "description"=>"<p>(A) A hypothetical genome with two chromosomes. (B) Both chromosomes are copied through duplication or speciation, resulting in two identical copies. (C) Following sequence divergence, similarity is only retained in coding regions. (D) Two instances of the orange domain are inserted in <i>g</i>₂ and <i>g</i>₃’, respectively. A yellow domain is inserted in <i>g</i>₂’. (E) Conserved genomic context shows that genes <i>g</i>₂ are <i>g</i>₂’ are homologous genes, although they contain unrelated domains. Similarly, genes <i>g</i>₂ and <i>g</i>₃’ contain homologous domains, but are not homologous genes.</p>", "links"=>[], "tags"=>["multidomain", "sequences", "genomic"], "article_id"=>602714, "categories"=>["Medicine", "Evolutionary Biology", "Infectious Diseases"], "users"=>["Nan Song", "Jacob M. Joseph", "George B. Davis", "Dannie Durand"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000063.g003", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Evolutionary_history_of_multidomain_sequences_in_genomic_context_/602714", "title"=>"Evolutionary history of multidomain sequences in genomic context.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2008-05-16 00:45:14"}
• {"files"=>["https://ndownloader.figshare.com/files/932702"], "description"=>"<p><i>ROC-100k</i> curves of Neighborhood Correlation (blue), BLAST (red), PSI-BLAST (magenta), DAC (purple) and alignment coverage (<i>α</i>≥0.3: green, <i>α</i>≥0.6: yellow, <i>α</i> ≥0.8: orange).</p>", "links"=>[], "tags"=>["curves", "kinase", "classification", "methods"], "article_id"=>603143, "categories"=>["Medicine", "Evolutionary Biology", "Infectious Diseases"], "users"=>["Nan Song", "Jacob M. Joseph", "George B. Davis", "Dannie Durand"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000063.g007", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_ROC_100k_curves_for_the_Kinase_family_for_all_classification_methods_tested_/603143", "title"=>"<i>ROC-100k</i> curves for the Kinase family for all classification methods tested.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2008-05-16 00:52:23"}
• {"files"=>["https://ndownloader.figshare.com/files/933088"], "description"=>"<p>The maximum value in each row is shown in bold. The significance of the difference of the <i>ROC-100k</i> score for each method compared with that of Neighborhood Correlation is expressed as a p-value. Dashes indicate <i>ROC-100k</i> scores that are not significantly different at the 0.001 level.</p>", "links"=>[], "tags"=>["scores"], "article_id"=>603539, "categories"=>["Medicine", "Evolutionary Biology", "Infectious Diseases"], "users"=>["Nan Song", "Jacob M. Joseph", "George B. Davis", "Dannie Durand"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000063.t003", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_ROC_100k_scores_for_Neighborhood_Correlation_BLAST_PSI_BLAST_and_Domain_Architecture_Comparison_for_all_families_/603539", "title"=>"<i>ROC-100k</i> scores for Neighborhood Correlation, BLAST, PSI-BLAST, and Domain Architecture Comparison for all families.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2008-05-16 00:58:59"}
• {"files"=>["https://ndownloader.figshare.com/files/932963"], "description"=>"<p><i>ROC-100k</i> scores for BLAST alone, and combined with alignment coverage at thresholds of <i>α</i>≥0.3, <i>α</i>≥0.6, and <i>α</i>≥0.8.</p>", "links"=>[], "tags"=>["scores", "alignment", "thresholds"], "article_id"=>603412, "categories"=>["Medicine", "Evolutionary Biology", "Infectious Diseases"], "users"=>["Nan Song", "Jacob M. Joseph", "George B. Davis", "Dannie Durand"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000063.t004", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_ROC_100k_scores_for_BLAST_alone_and_combined_with_alignment_coverage_at_thresholds_of_945_0_3_945_0_6_and_945_0_8_/603412", "title"=>"<i>ROC-100k</i> scores for BLAST alone, and combined with alignment coverage at thresholds of <i>α</i>≥0.3, <i>α</i>≥0.6, and <i>α</i>≥0.8.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2008-05-16 00:56:52"}
• {"files"=>["https://ndownloader.figshare.com/files/933034"], "description"=>"<p><i>k</i>: the number of sequences.</p>", "links"=>[], "tags"=>["computational biology/comparative sequence analysis", "computational biology/evolutionary modeling", "computational biology/genomics", "computational biology/protein homology detection", "evolutionary biology/bioinformatics", "evolutionary biology/evolutionary and comparative genetics", "evolutionary biology/genomics"], "article_id"=>603472, "categories"=>["Medicine", "Evolutionary Biology", "Infectious Diseases"], "users"=>["Nan Song", "Jacob M. Joseph", "George B. Davis", "Dannie Durand"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000063.t001", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Test_family_statistics_/603472", "title"=>"Test family statistics.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2008-05-16 00:57:52"}
• {"files"=>["https://ndownloader.figshare.com/files/932801"], "description"=>"<p>Histograms calculated with the optimal alignment length only (FF: blue, FO: red) and with combined non-conflicting alignments (FF: turquoise, FO: brown) (A) FOX, (B) PDE, and (C) Laminin.</p>", "links"=>[], "tags"=>["distributions"], "article_id"=>603251, "categories"=>["Medicine", "Evolutionary Biology", "Infectious Diseases"], "users"=>["Nan Song", "Jacob M. Joseph", "George B. Davis", "Dannie Durand"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000063.g008", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Alignment_coverage_distributions_for_representative_families_/603251", "title"=>"Alignment coverage distributions for representative families.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2008-05-16 00:54:11"}
• {"files"=>["https://ndownloader.figshare.com/files/932915"], "description"=>"<p>(A) Venn diagram representing pairs with <i>NC</i>≥0.6 that share a KOG annotation (turquoise), pairs with <i>NC</i>≥0.6 that do not share a KOG annotation (blue), and pairs with <i>NC</i><0.6 that share a KOG annotation (yellow). (B) Pairs with <i>NC</i>≥0.6 that share a Pfam domain (turquoise), pairs with <i>NC</i>≥0.6 that do not share a Pfam domain (blue), and pairs with <i>NC</i><0.6 that share a Pfam domain (green).</p>", "links"=>[], "tags"=>["computational biology/comparative sequence analysis", "computational biology/evolutionary modeling", "computational biology/genomics", "computational biology/protein homology detection", "evolutionary biology/bioinformatics", "evolutionary biology/evolutionary and comparative genetics", "evolutionary biology/genomics"], "article_id"=>603355, "categories"=>["Medicine", "Evolutionary Biology", "Infectious Diseases"], "users"=>["Nan Song", "Jacob M. Joseph", "George B. Davis", "Dannie Durand"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000063.g009", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_Neighborhood_Correlation_with_other_classifications_/603355", "title"=>"Comparison of Neighborhood Correlation with other classifications.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2008-05-16 00:55:55"}
• {"files"=>["https://ndownloader.figshare.com/files/932995"], "description"=>"<p>Precision and recall for predictions using optimal and combined alignments.</p>", "links"=>[], "tags"=>["predictions", "optimal"], "article_id"=>603448, "categories"=>["Medicine", "Evolutionary Biology", "Infectious Diseases"], "users"=>["Nan Song", "Jacob M. Joseph", "George B. Davis", "Dannie Durand"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000063.t005", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Precision_and_recall_for_predictions_using_optimal_and_combined_alignments_/603448", "title"=>"Precision and recall for predictions using optimal and combined alignments.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2008-05-16 00:57:28"}
• {"files"=>["https://ndownloader.figshare.com/files/933060"], "description"=>"<p>Functional properties of the 20 test families.</p>", "links"=>[], "tags"=>["20"], "article_id"=>603507, "categories"=>["Medicine", "Evolutionary Biology", "Infectious Diseases"], "users"=>["Nan Song", "Jacob M. Joseph", "George B. Davis", "Dannie Durand"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000063.t002", "stats"=>{"downloads"=>11, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Functional_properties_of_the_20_test_families_/603507", "title"=>"Functional properties of the 20 test families.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2008-05-16 00:58:27"}
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PMC Usage Stats | Further Information

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