Non-Negative Matrix Factorization for Learning Alignment-Specific Models of Protein Evolution
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{"title"=>"Non-negative matrix factorization for learning alignment-specific models of protein evolution", "type"=>"journal", "authors"=>[{"first_name"=>"Ben", "last_name"=>"Murrell", "scopus_author_id"=>"54403363300"}, {"first_name"=>"Thomas", "last_name"=>"Weighill", "scopus_author_id"=>"54788577000"}, {"first_name"=>"Jan", "last_name"=>"Buys", "scopus_author_id"=>"55278753400"}, {"first_name"=>"Robert", "last_name"=>"Ketteringham", "scopus_author_id"=>"54787725100"}, {"first_name"=>"Sasha", "last_name"=>"Moola", "scopus_author_id"=>"55659889900"}, {"first_name"=>"Gerdus", "last_name"=>"Benade", "scopus_author_id"=>"54787354300"}, {"first_name"=>"Lise", "last_name"=>"Buisson", "scopus_author_id"=>"54787295100"}, {"first_name"=>"Daniel", "last_name"=>"Kaliski", "scopus_author_id"=>"54787717500"}, {"first_name"=>"Tristan", "last_name"=>"Hands", "scopus_author_id"=>"54787636800"}, {"first_name"=>"Konrad", "last_name"=>"Scheffler", "scopus_author_id"=>"13103633800"}], "year"=>2011, "source"=>"PLoS ONE", "identifiers"=>{"pui"=>"363132844", "isbn"=>"1932-6203", "sgr"=>"84055216948", "issn"=>"19326203", "pmid"=>"22216138", "scopus"=>"2-s2.0-84055216948", "doi"=>"10.1371/journal.pone.0028898"}, "id"=>"c4a2c38a-6d21-396d-9cf5-4be809d19d7e", "abstract"=>"Models of protein evolution currently come in two flavors: generalist and specialist. Generalist models (e.g. PAM, JTT, WAG) adopt a one-size-fits-all approach, where a single model is estimated from a number of different protein alignments. Specialist models (e.g. mtREV, rtREV, HIVbetween) can be estimated when a large quantity of data are available for a single organism or gene, and are intended for use on that organism or gene only. Unsurprisingly, specialist models outperform generalist models, but in most instances there simply are not enough data available to estimate them. We propose a method for estimating alignment-specific models of protein evolution in which the complexity of the model is adapted to suit the richness of the data. Our method uses non-negative matrix factorization (NNMF) to learn a set of basis matrices from a general dataset containing a large number of alignments of different proteins, thus capturing the dimensions of important variation. It then learns a set of weights that are specific to the organism or gene of interest and for which only a smaller dataset is available. Thus the alignment-specific model is obtained as a weighted sum of the basis matrices. Having been constrained to vary along only as many dimensions as the data justify, the model has far fewer parameters than would be required to estimate a specialist model. We show that our NNMF procedure produces models that outperform existing methods on all but one of 50 test alignments. The basis matrices we obtain confirm the expectation that amino acid properties tend to be conserved, and allow us to quantify, on specific alignments, how the strength of conservation varies across different properties. We also apply our new models to phylogeny inference and show that the resulting phylogenies are different from, and have improved likelihood over, those inferred under standard models", "link"=>"http://www.mendeley.com/research/nonnegative-matrix-factorization-learning-alignmentspecific-models-protein-evolution-1", "reader_count"=>22, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>4, "Researcher"=>5, "Student > Ph. D. Student"=>4, "Student > Postgraduate"=>2, "Student > Master"=>1, "Student > Bachelor"=>1, "Lecturer"=>1, "Professor"=>3}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>4, "Researcher"=>5, "Student > Ph. D. Student"=>4, "Student > Postgraduate"=>2, "Student > Master"=>1, "Student > Bachelor"=>1, "Lecturer"=>1, "Professor"=>3}, "reader_count_by_subject_area"=>{"Engineering"=>4, "Unspecified"=>2, "Biochemistry, Genetics and Molecular Biology"=>3, "Mathematics"=>1, "Agricultural and Biological Sciences"=>7, "Chemical Engineering"=>1, "Social Sciences"=>1, "Computer Science"=>3}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>4}, "Social Sciences"=>{"Social Sciences"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>7}, "Computer Science"=>{"Computer Science"=>3}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>3}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>2}, "Chemical Engineering"=>{"Chemical Engineering"=>1}}, "reader_count_by_country"=>{"Canada"=>1, "Japan"=>1, "Brazil"=>1, "United Kingdom"=>1, "Spain"=>1}, "group_count"=>1}

Scopus | Further Information

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Figshare

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  • {"files"=>["https://ndownloader.figshare.com/files/700294"], "description"=>"<p>Each table entry is the number of datasets with in that range. For any dataset, the best model has . A model with has essentially no support.</p>", "links"=>[], "tags"=>["models", "gamma"], "article_id"=>370663, "categories"=>["Biochemistry", "Information And Computing Sciences", "Mathematics", "Biological Sciences", "Genetics", "Evolutionary Biology"], "users"=>["Ben Murrell", "Thomas Weighill", "Jan Buys", "Robert Ketteringham", "Sasha Moola", "Gerdus Benade", "Lise du Buisson", "Daniel Kaliski", "Tristan Hands", "Konrad Scheffler"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0028898.t004", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_for_all_models_with_gamma_rate_variation_4_categories_/370663", "title"=>"for all models with gamma rate variation (4 categories).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-20 10:35:24"}
  • {"files"=>["https://ndownloader.figshare.com/files/699585"], "description"=>"<p>Each blue dot represents an alignment in the Pandit database. The green region covers the alignments used in the training set, and the thin red region covers those in the test set.</p>", "links"=>[], "tags"=>["larger", "pandit"], "article_id"=>369958, "categories"=>["Biochemistry", "Information And Computing Sciences", "Mathematics", "Biological Sciences", "Genetics", "Evolutionary Biology"], "users"=>["Ben Murrell", "Thomas Weighill", "Jan Buys", "Robert Ketteringham", "Sasha Moola", "Gerdus Benade", "Lise du Buisson", "Daniel Kaliski", "Tristan Hands", "Konrad Scheffler"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0028898.g003", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Selecting_the_larger_Pandit_alignments_/369958", "title"=>"Selecting the larger Pandit alignments.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 10:31:41"}
  • {"files"=>["https://ndownloader.figshare.com/files/699784"], "description"=>"<p>The set of NNMF basis matrices obtained for ranks ranging from 1 to 5. Amino acids are ordered according to their Stanfel classification <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0028898#pone.0028898-Stanfel1\" target=\"_blank\">[25]</a>. Rates are indicated in grayscale, with pure white being a rate of zero and pure black being the maximum rate in the matrix.</p>", "links"=>[], "tags"=>["genetics and genomics", "Computational biology", "computer science", "Evolutionary biology", "Biochemistry", "mathematics"], "article_id"=>370145, "categories"=>["Biochemistry", "Information And Computing Sciences", "Mathematics", "Biological Sciences", "Genetics", "Evolutionary Biology"], "users"=>["Ben Murrell", "Thomas Weighill", "Jan Buys", "Robert Ketteringham", "Sasha Moola", "Gerdus Benade", "Lise du Buisson", "Daniel Kaliski", "Tristan Hands", "Konrad Scheffler"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0028898.g005", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_NNMF_basis_matrices_/370145", "title"=>"NNMF basis matrices.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 10:32:40"}
  • {"files"=>["https://ndownloader.figshare.com/files/699475"], "description"=>"<p>A schematic overview of the procedure.</p>", "links"=>[], "tags"=>["models"], "article_id"=>369853, "categories"=>["Biochemistry", "Information And Computing Sciences", "Mathematics", "Biological Sciences", "Genetics", "Evolutionary Biology"], "users"=>["Ben Murrell", "Thomas Weighill", "Jan Buys", "Robert Ketteringham", "Sasha Moola", "Gerdus Benade", "Lise du Buisson", "Daniel Kaliski", "Tristan Hands", "Konrad Scheffler"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0028898.g002", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Learning_models_of_protein_evolution_with_NNMF_/369853", "title"=>"Learning models of protein evolution with NNMF.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 10:31:08"}
  • {"files"=>["https://ndownloader.figshare.com/files/700165"], "description"=>"<p>Each table entry is the number of datasets with in that range. For any dataset, the best model has . A model with has essentially no support.</p>", "links"=>[], "tags"=>["scores"], "article_id"=>370542, "categories"=>["Biochemistry", "Information And Computing Sciences", "Mathematics", "Biological Sciences", "Genetics", "Evolutionary Biology"], "users"=>["Ben Murrell", "Thomas Weighill", "Jan Buys", "Robert Ketteringham", "Sasha Moola", "Gerdus Benade", "Lise du Buisson", "Daniel Kaliski", "Tristan Hands", "Konrad Scheffler"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0028898.t003", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_scores_without_NNMF_/370542", "title"=>"scores without NNMF.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-20 10:34:46"}
  • {"files"=>["https://ndownloader.figshare.com/files/699365"], "description"=>"<p>Non-negative matrix factorization.</p>", "links"=>[], "tags"=>["matrix"], "article_id"=>369739, "categories"=>["Biochemistry", "Information And Computing Sciences", "Mathematics", "Biological Sciences", "Genetics", "Evolutionary Biology"], "users"=>["Ben Murrell", "Thomas Weighill", "Jan Buys", "Robert Ketteringham", "Sasha Moola", "Gerdus Benade", "Lise du Buisson", "Daniel Kaliski", "Tristan Hands", "Konrad Scheffler"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0028898.g001", "stats"=>{"downloads"=>0, "page_views"=>17, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Non_negative_matrix_factorization_/369739", "title"=>"Non-negative matrix factorization.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 10:30:34"}
  • {"files"=>["https://ndownloader.figshare.com/files/700247"], "description"=>"<p>Interpretation of the matrix factorization in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0028898#pone-0028898-g001\" target=\"_blank\">Figure 1</a>.</p>", "links"=>[], "tags"=>["matrix", "factorization"], "article_id"=>370620, "categories"=>["Biochemistry", "Information And Computing Sciences", "Mathematics", "Biological Sciences", "Genetics", "Evolutionary Biology"], "users"=>["Ben Murrell", "Thomas Weighill", "Jan Buys", "Robert Ketteringham", "Sasha Moola", "Gerdus Benade", "Lise du Buisson", "Daniel Kaliski", "Tristan Hands", "Konrad Scheffler"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0028898.t001", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Interpretation_of_the_matrix_factorization_in_Figure_1_/370620", "title"=>"Interpretation of the matrix factorization in Figure 1.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-20 10:35:11"}
  • {"files"=>["https://ndownloader.figshare.com/files/700212"], "description"=>"<p>Each table entry is the number of datasets with in that range. For any dataset, the best model has . A model with has essentially no support.</p>", "links"=>[], "tags"=>["scores"], "article_id"=>370585, "categories"=>["Biochemistry", "Information And Computing Sciences", "Mathematics", "Biological Sciences", "Genetics", "Evolutionary Biology"], "users"=>["Ben Murrell", "Thomas Weighill", "Jan Buys", "Robert Ketteringham", "Sasha Moola", "Gerdus Benade", "Lise du Buisson", "Daniel Kaliski", "Tristan Hands", "Konrad Scheffler"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0028898.t002", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_scores_for_all_models_/370585", "title"=>"scores for all models.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-20 10:34:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/699916"], "description"=>"<p>The correlations between amino acid properties and the basis matrices. The horizontal black line (at −0.16867) indicates the threshold for significant negative correlation (, one tailed, ).</p>", "links"=>[], "tags"=>["matrices", "correlate", "amino"], "article_id"=>370291, "categories"=>["Biochemistry", "Information And Computing Sciences", "Mathematics", "Biological Sciences", "Genetics", "Evolutionary Biology"], "users"=>["Ben Murrell", "Thomas Weighill", "Jan Buys", "Robert Ketteringham", "Sasha Moola", "Gerdus Benade", "Lise du Buisson", "Daniel Kaliski", "Tristan Hands", "Konrad Scheffler"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0028898.g006", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_NNMF_basis_matrices_correlate_with_amino_acid_properties_/370291", "title"=>"NNMF basis matrices correlate with amino acid properties.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 10:33:26"}
  • {"files"=>["https://ndownloader.figshare.com/files/700091"], "description"=>"<p>The difference between phylogenies increases as the mean likelihood difference per site between NNMF and WAG increases. , (, randomization test with replicates). Assuming intercept of 0, slope = 38.1. Without this assumption, intercept = −0.31, slope = 38.5.</p>", "links"=>[], "tags"=>["phylogenetic"], "article_id"=>370465, "categories"=>["Biochemistry", "Information And Computing Sciences", "Mathematics", "Biological Sciences", "Genetics", "Evolutionary Biology"], "users"=>["Ben Murrell", "Thomas Weighill", "Jan Buys", "Robert Ketteringham", "Sasha Moola", "Gerdus Benade", "Lise du Buisson", "Daniel Kaliski", "Tristan Hands", "Konrad Scheffler"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0028898.g008", "stats"=>{"downloads"=>1, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Likelihood_improvement_predicts_phylogenetic_difference_/370465", "title"=>"Likelihood improvement predicts phylogenetic difference.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 10:34:22"}

PMC Usage Stats | Further Information

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  • {"unique-ip"=>"2", "full-text"=>"2", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"1"}
  • {"unique-ip"=>"2", "full-text"=>"1", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"2"}
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  • {"unique-ip"=>"1", "full-text"=>"1", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2015", "month"=>"1"}
  • {"unique-ip"=>"4", "full-text"=>"2", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"2", "year"=>"2015", "month"=>"11"}
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Relative Metric

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