Computational Comparative Study of Tuberculosis Proteomes Using a Model Learned from Signal Peptide Structures
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{"title"=>"Computational comparative study of tuberculosis proteomes using a model learned from signal peptide structures", "type"=>"journal", "authors"=>[{"first_name"=>"Jhih Siang", "last_name"=>"Lai", "scopus_author_id"=>"24829217900"}, {"first_name"=>"Cheng Wei", "last_name"=>"Cheng", "scopus_author_id"=>"56471038200"}, {"first_name"=>"Ting Yi", "last_name"=>"Sung", "scopus_author_id"=>"57154838500"}, {"first_name"=>"Wen Lian", "last_name"=>"Hsu", "scopus_author_id"=>"26324042000"}], "year"=>2012, "source"=>"PLoS ONE", "identifiers"=>{"sgr"=>"84859501651", "doi"=>"10.1371/journal.pone.0035018", "pui"=>"364591232", "pmid"=>"22496884", "scopus"=>"2-s2.0-84859501651", "issn"=>"19326203", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)"}, "id"=>"2bd340d3-2fd4-3d2c-84ca-c7c4667f955d", "abstract"=>"Secretome analysis is important in pathogen studies. A fundamental and convenient way to identify secreted proteins is to first predict signal peptides, which are essential for protein secretion. However, signal peptides are highly complex functional sequences that are easily confused with transmembrane domains. Such confusion would obviously affect the discovery of secreted proteins. Transmembrane proteins are important drug targets, but very few transmembrane protein structures have been determined experimentally; hence, prediction of the structures is essential. In the field of structure prediction, researchers do not make assumptions about organisms, so there is a need for a general signal peptide predictor.To improve signal peptide prediction without prior knowledge of the associated organisms, we present a machine-learning method, called SVMSignal, which uses biochemical properties as features, as well as features acquired from a novel encoding, to capture biochemical profile patterns for learning the structures of signal peptides directly.We tested SVMSignal and five popular methods on two benchmark datasets from the SPdb and UniProt/Swiss-Prot databases, respectively. Although SVMSignal was trained on an old dataset, it performed well, and the results demonstrate that learning the structures of signal peptides directly is a promising approach. We also utilized SVMSignal to analyze proteomes in the entire HAMAP microbial database. Finally, we conducted a comparative study of secretome analysis on seven tuberculosis-related strains selected from the HAMAP database. We identified ten potential secreted proteins, two of which are drug resistant and four are potential transmembrane proteins.SVMSignal is publicly available at http://bio-cluster.iis.sinica.edu.tw/SVMSignal. It provides user-friendly interfaces and visualizations, and the prediction results are available for download.", "link"=>"http://www.mendeley.com/research/computational-comparative-study-tuberculosis-proteomes-using-model-learned-signal-peptide-structures", "reader_count"=>34, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>2, "Researcher"=>7, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>5, "Student > Postgraduate"=>5, "Student > Master"=>7, "Other"=>1, "Student > Bachelor"=>2, "Professor"=>2}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>2, "Researcher"=>7, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>5, "Student > Postgraduate"=>5, "Student > Master"=>7, "Other"=>1, "Student > Bachelor"=>2, "Professor"=>2}, "reader_count_by_subject_area"=>{"Unspecified"=>3, "Engineering"=>1, "Biochemistry, Genetics and Molecular Biology"=>4, "Agricultural and Biological Sciences"=>15, "Medicine and Dentistry"=>4, "Social Sciences"=>2, "Computer Science"=>4, "Immunology and Microbiology"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>4}, "Social Sciences"=>{"Social Sciences"=>2}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>15}, "Computer Science"=>{"Computer Science"=>4}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>4}, "Unspecified"=>{"Unspecified"=>3}}, "reader_count_by_country"=>{"Colombia"=>1, "United States"=>1, "Brazil"=>1, "South Africa"=>1, "Spain"=>1}, "group_count"=>3}

Scopus | Further Information

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  • {"files"=>["https://ndownloader.figshare.com/files/337510", "https://ndownloader.figshare.com/files/337527", "https://ndownloader.figshare.com/files/337551", "https://ndownloader.figshare.com/files/337585", "https://ndownloader.figshare.com/files/337608"], "description"=>"<div><p>Secretome analysis is important in pathogen studies. A fundamental and convenient way to identify secreted proteins is to first predict signal peptides, which are essential for protein secretion. However, signal peptides are highly complex functional sequences that are easily confused with transmembrane domains. Such confusion would obviously affect the discovery of secreted proteins. Transmembrane proteins are important drug targets, but very few transmembrane protein structures have been determined experimentally; hence, prediction of the structures is essential. In the field of structure prediction, researchers do not make assumptions about organisms, so there is a need for a general signal peptide predictor.</p> <p>To improve signal peptide prediction without prior knowledge of the associated organisms, we present a machine-learning method, called SVMSignal, which uses biochemical properties as features, as well as features acquired from a novel encoding, to capture biochemical profile patterns for learning the structures of signal peptides directly.</p> <p>We tested SVMSignal and five popular methods on two benchmark datasets from the SPdb and UniProt/Swiss-Prot databases, respectively. Although SVMSignal was trained on an old dataset, it performed well, and the results demonstrate that learning the structures of signal peptides directly is a promising approach. We also utilized SVMSignal to analyze proteomes in the entire HAMAP microbial database. Finally, we conducted a comparative study of secretome analysis on seven tuberculosis-related strains selected from the HAMAP database. We identified ten potential secreted proteins, two of which are drug resistant and four are potential transmembrane proteins.</p> <p>SVMSignal is publicly available at <a href=\"http://bio-cluster.iis.sinica.edu.tw/SVMSignal\">http://bio-cluster.iis.sinica.edu.tw/SVMSignal</a>. It provides user-friendly interfaces and visualizations, and the prediction results are available for download.</p> </div>", "links"=>[], "tags"=>["computational", "comparative", "tuberculosis", "proteomes", "learned", "peptide", "structures"], "article_id"=>126715, "categories"=>["Biochemistry", "Biological Sciences", "Information And Computing Sciences", "Cell Biology", "Microbiology"], "users"=>["Jhih-Siang Lai", "Cheng-Wei Cheng", "Ting-Yi Sung", "Wen-Lian Hsu"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0035018.s001", "https://dx.doi.org/10.1371/journal.pone.0035018.s002", "https://dx.doi.org/10.1371/journal.pone.0035018.s003", "https://dx.doi.org/10.1371/journal.pone.0035018.s004", "https://dx.doi.org/10.1371/journal.pone.0035018.s005"], "stats"=>{"downloads"=>2, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Computational_Comparative_Study_of_Tuberculosis_Proteomes_Using_a_Model_Learned_from_Signal_Peptide_Structures/126715", "title"=>"Computational Comparative Study of Tuberculosis Proteomes Using a Model Learned from Signal Peptide Structures", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2012-04-09 01:51:55"}
  • {"files"=>["https://ndownloader.figshare.com/files/656474"], "description"=>"<p>The hierarchical architecture of SVMSignal.</p>", "links"=>[], "tags"=>["hierarchical"], "article_id"=>326969, "categories"=>["Biochemistry", "Biological Sciences", "Information And Computing Sciences", "Cell Biology", "Microbiology"], "users"=>["Jhih-Siang Lai", "Cheng-Wei Cheng", "Ting-Yi Sung", "Wen-Lian Hsu"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0035018.g001", "stats"=>{"downloads"=>0, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_hierarchical_architecture_of_SVMSignal_/326969", "title"=>"The hierarchical architecture of SVMSignal.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-04-09 01:56:09"}
  • {"files"=>["https://ndownloader.figshare.com/files/656873"], "description"=>"<p>Specificities (%) of various predictors on the non-signal peptide protein benchmark datasets.</p>", "links"=>[], "tags"=>["predictors", "non-signal", "peptide", "benchmark"], "article_id"=>327359, "categories"=>["Biochemistry", "Biological Sciences", "Information And Computing Sciences", "Cell Biology", "Microbiology"], "users"=>["Jhih-Siang Lai", "Cheng-Wei Cheng", "Ting-Yi Sung", "Wen-Lian Hsu"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0035018.t004", "stats"=>{"downloads"=>2, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Specificities_of_various_predictors_on_the_non_signal_peptide_protein_benchmark_datasets_/327359", "title"=>"Specificities (%) of various predictors on the non-signal peptide protein benchmark datasets.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-04-09 02:02:39"}
  • {"files"=>["https://ndownloader.figshare.com/files/656626"], "description"=>"<p>An example, O95994 (AGR2_HUMAN), of biochemical feature profiles in a 100-residue N-terminal subsequence predicted to contain a signal peptide.</p>", "links"=>[], "tags"=>["o95994", "biochemical", "profiles", "100-residue", "n-terminal", "subsequence"], "article_id"=>327103, "categories"=>["Biochemistry", "Biological Sciences", "Information And Computing Sciences", "Cell Biology", "Microbiology"], "users"=>["Jhih-Siang Lai", "Cheng-Wei Cheng", "Ting-Yi Sung", "Wen-Lian Hsu"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0035018.g002", "stats"=>{"downloads"=>3, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_An_example_O95994_AGR2_HUMAN_of_biochemical_feature_profiles_in_a_100_residue_N_terminal_subsequence_predicted_to_contain_a_signal_peptide_/327103", "title"=>"An example, O95994 (AGR2_HUMAN), of biochemical feature profiles in a 100-residue N-terminal subsequence predicted to contain a signal peptide.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-04-09 01:58:23"}

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Relative Metric

{"start_date"=>"2012-01-01T00:00:00Z", "end_date"=>"2012-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Biology and life sciences", "average_usage"=>[322, 550, 671, 773, 864, 955, 1048, 1135, 1223, 1308, 1387, 1465, 1534, 1602, 1673, 1744, 1813, 1885, 1955, 2026, 2093, 2160, 2228, 2290, 2349]}, {"subject_area"=>"/Biology and life sciences/Biochemistry", "average_usage"=>[316, 541, 663, 766, 856, 950, 1041, 1128, 1218, 1302, 1382, 1456, 1526, 1593, 1657, 1729, 1796, 1862, 1930, 1999, 2065, 2132, 2202, 2261, 2319]}, {"subject_area"=>"/Biology and life sciences/Organisms", "average_usage"=>[331, 557, 677, 777, 868, 960, 1050, 1136, 1223, 1307, 1390, 1466, 1536, 1603, 1673, 1741, 1814, 1889, 1954, 2028, 2096, 2164, 2233, 2305, 2362]}]}
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