An Integrative Computational Approach for Prioritization of Genomic Variants
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{"title"=>"An integrative computational approach for prioritization of genomic variants", "type"=>"journal", "authors"=>[{"first_name"=>"Inna", "last_name"=>"Dubchak", "scopus_author_id"=>"7004361127"}, {"first_name"=>"Sandhya", "last_name"=>"Balasubramanian", "scopus_author_id"=>"55838510174"}, {"first_name"=>"Sheng", "last_name"=>"Wang", "scopus_author_id"=>"55789487300"}, {"first_name"=>"Cem", "last_name"=>"Meyden", "scopus_author_id"=>"56449287100"}, {"first_name"=>"Dinanath", "last_name"=>"Sulakhe", "scopus_author_id"=>"11140588000"}, {"first_name"=>"Alexander", "last_name"=>"Poliakov", "scopus_author_id"=>"12781332100"}, {"first_name"=>"Daniela", "last_name"=>"Börnigen", "scopus_author_id"=>"55508921300"}, {"first_name"=>"Bingqing", "last_name"=>"Xie", "scopus_author_id"=>"55010372700"}, {"first_name"=>"Andrew", "last_name"=>"Taylor", "scopus_author_id"=>"55991109700"}, {"first_name"=>"Jianzhu", "last_name"=>"Ma", "scopus_author_id"=>"57199108500"}, {"first_name"=>"Alex R.", "last_name"=>"Paciorkowski", "scopus_author_id"=>"16745258800"}, {"first_name"=>"Ghayda M.", "last_name"=>"Mirzaa", "scopus_author_id"=>"6504064624"}, {"first_name"=>"Paul", "last_name"=>"Dave", "scopus_author_id"=>"55498428500"}, {"first_name"=>"Gady", "last_name"=>"Agam", "scopus_author_id"=>"7005725869"}, {"first_name"=>"Jinbo", "last_name"=>"Xu", "scopus_author_id"=>"8892484800"}, {"first_name"=>"Lihadh", "last_name"=>"Al-Gazali", "scopus_author_id"=>"7005896722"}, {"first_name"=>"Christopher E.", "last_name"=>"Mason", "scopus_author_id"=>"7202125538"}, {"first_name"=>"M. Elizabeth", "last_name"=>"Ross", "scopus_author_id"=>"7403661996"}, {"first_name"=>"Natalia", "last_name"=>"Maltsev", "scopus_author_id"=>"6603676882"}, {"first_name"=>"T. Conrad", "last_name"=>"Gilliam", "scopus_author_id"=>"7006979743"}], "year"=>2014, "source"=>"PLoS ONE", "identifiers"=>{"pui"=>"600783760", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "issn"=>"19326203", "doi"=>"10.1371/journal.pone.0114903", "scopus"=>"2-s2.0-84918588740", "pmid"=>"25506935", "sgr"=>"84918588740"}, "id"=>"149dba05-49c8-3d97-933d-50e42c17ee45", "abstract"=>"An essential step in the discovery of molecular mechanisms contributing to disease phenotypes and efficient experimental planning is the development of weighted hypotheses that estimate the functional effects of sequence variants discovered by high-throughput genomics. With the increasing specialization of the bioinformatics resources, creating analytical workflows that seamlessly integrate data and bioinformatics tools developed by multiple groups becomes inevitable. Here we present a case study of a use of the distributed analytical environment integrating four complementary specialized resources, namely the Lynx platform, VISTA RViewer, the Developmental Brain Disorders Database (DBDB), and the RaptorX server, for the identification of high-confidence candidate genes contributing to pathogenesis of spina bifida. The analysis resulted in prediction and validation of deleterious mutations in the SLC19A placental transporter in mothers of the affected children that causes narrowing of the outlet channel and therefore leads to the reduced folate permeation rate. The described approach also enabled correct identification of several genes, previously shown to contribute to pathogenesis of spina bifida, and suggestion of additional genes for experimental validations. The study demonstrates that the seamless integration of bioinformatics resources enables fast and efficient prioritization and characterization of genomic factors and molecular networks contributing to the phenotypes of interest.", "link"=>"http://www.mendeley.com/research/integrative-computational-approach-prioritization-genomic-variants", "reader_count"=>26, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>4, "Researcher"=>6, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>5, "Student > Postgraduate"=>1, "Student > Master"=>4, "Student > Bachelor"=>3, "Professor"=>1}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>4, "Researcher"=>6, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>5, "Student > Postgraduate"=>1, "Student > Master"=>4, "Student > Bachelor"=>3, "Professor"=>1}, "reader_count_by_subject_area"=>{"Engineering"=>1, "Unspecified"=>1, "Biochemistry, Genetics and Molecular Biology"=>6, "Nursing and Health Professions"=>1, "Mathematics"=>1, "Agricultural and Biological Sciences"=>9, "Medicine and Dentistry"=>3, "Physics and Astronomy"=>1, "Social Sciences"=>1, "Computer Science"=>2}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>3}, "Social Sciences"=>{"Social Sciences"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>9}, "Computer Science"=>{"Computer Science"=>2}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>6}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>1}}, "reader_count_by_country"=>{"United States"=>1, "Australia"=>1, "France"=>1}, "group_count"=>0}

Scopus | Further Information

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Figshare

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  • {"files"=>["https://s3-eu-west-1.amazonaws.com/pstorage-plos-3567654/1843629/Figure_6.tif"], "description"=>"<p>Access to the analytical tools within the described bioinformatics environment.</p>", "links"=>[], "tags"=>["DBDB", "genomic", "Developmental Brain Disorders Database", "pathogenesis", "identification", "gene", "spina bifida", "folate permeation rate", "vista", "bioinformatics resources", "phenotype", "Integrative Computational Approach", "SLC 19A placental transporter", "validation"], "article_id"=>1271250, "categories"=>["Biological Sciences"], "users"=>["Inna Dubchak", "Sandhya Balasubramanian", "Sheng Wang", "Cem Meyden", "Dinanath Sulakhe", "Alexander Poliakov", "Daniela Börnigen", "Bingqing Xie", "Andrew Taylor", "Jianzhu Ma", "Alex R. Paciorkowski", "Ghayda M. Mirzaa", "Paul Dave", "Gady Agam", "Jinbo Xu", "Lihadh Al-Gazali", "Christopher E. Mason", "M. Elizabeth Ross", "Natalia Maltsev", "T. Conrad Gilliam"], "doi"=>["http://dx.doi.org/10.1371/journal.pone.0114903.g006"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"http://figshare.com/articles/_Access_to_the_analytical_tools_within_the_described_bioinformatics_environment_/1271250", "title"=>"Access to the analytical tools within the described bioinformatics environment.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-12-15 02:59:00"}
  • {"files"=>["https://s3-eu-west-1.amazonaws.com/pstorage-plos-3567654/1843631/Table_1.xls"], "description"=>"<p>*The P-values in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0114903#pone-0114903-t001\" target=\"_blank\">Table 1</a> are generated by 10 000 random permutations of the input data scored according to the strength of association with the phenotype using DBDB recommendations (random reassignment of the scores to network nodes and computation of the corresponding randomized scores for all candidate genes) <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0114903#pone.0114903-Nitsch1\" target=\"_blank\">[38]</a>.</p><p>**Family 1: affected children 2C1, 2C2; mother 2M, father 2F. Family 2: affected children 6C1, 6C2; mother 6M, father 6F.</p><p>Identified genetic variants in folate metabolism genes in spina bifida patients and unaffected parents.</p>", "links"=>[], "tags"=>["DBDB", "genomic", "Developmental Brain Disorders Database", "pathogenesis", "identification", "gene", "spina bifida", "folate permeation rate", "vista", "bioinformatics resources", "phenotype", "Integrative Computational Approach", "SLC 19A placental transporter", "validation"], "article_id"=>1271252, "categories"=>["Biological Sciences"], "users"=>["Inna Dubchak", "Sandhya Balasubramanian", "Sheng Wang", "Cem Meyden", "Dinanath Sulakhe", "Alexander Poliakov", "Daniela Börnigen", "Bingqing Xie", "Andrew Taylor", "Jianzhu Ma", "Alex R. Paciorkowski", "Ghayda M. Mirzaa", "Paul Dave", "Gady Agam", "Jinbo Xu", "Lihadh Al-Gazali", "Christopher E. Mason", "M. Elizabeth Ross", "Natalia Maltsev", "T. Conrad Gilliam"], "doi"=>["http://dx.doi.org/10.1371/journal.pone.0114903.t001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"http://figshare.com/articles/_Identified_genetic_variants_in_folate_metabolism_genes_in_spina_bifida_patients_and_unaffected_parents_/1271252", "title"=>"Identified genetic variants in folate metabolism genes in spina bifida patients and unaffected parents.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-12-15 02:59:00"}
  • {"files"=>["https://s3-eu-west-1.amazonaws.com/pstorage-plos-3567654/1843632/Table_2.xls"], "description"=>"<p>Volume and surface area of the of the cleft in the native and the mutant structures as calculated by 3V.</p>", "links"=>[], "tags"=>["DBDB", "genomic", "Developmental Brain Disorders Database", "pathogenesis", "identification", "gene", "spina bifida", "folate permeation rate", "vista", "bioinformatics resources", "phenotype", "Integrative Computational Approach", "SLC 19A placental transporter", "validation"], "article_id"=>1271253, "categories"=>["Biological Sciences"], "users"=>["Inna Dubchak", "Sandhya Balasubramanian", "Sheng Wang", "Cem Meyden", "Dinanath Sulakhe", "Alexander Poliakov", "Daniela Börnigen", "Bingqing Xie", "Andrew Taylor", "Jianzhu Ma", "Alex R. Paciorkowski", "Ghayda M. Mirzaa", "Paul Dave", "Gady Agam", "Jinbo Xu", "Lihadh Al-Gazali", "Christopher E. Mason", "M. Elizabeth Ross", "Natalia Maltsev", "T. Conrad Gilliam"], "doi"=>["http://dx.doi.org/10.1371/journal.pone.0114903.t002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"http://figshare.com/articles/_Volume_and_surface_area_of_the_of_the_cleft_in_the_native_and_the_mutant_structures_as_calculated_by_3V_/1271253", "title"=>"Volume and surface area of the of the cleft in the native and the mutant structures as calculated by 3V.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-12-15 02:59:00"}
  • {"files"=>["https://s3-eu-west-1.amazonaws.com/pstorage-plos-3567654/1843645/Materials_S1.docx"], "description"=>"<div><p>An essential step in the discovery of molecular mechanisms contributing to disease phenotypes and efficient experimental planning is the development of weighted hypotheses that estimate the functional effects of sequence variants discovered by high-throughput genomics. With the increasing specialization of the bioinformatics resources, creating analytical workflows that seamlessly integrate data and bioinformatics tools developed by multiple groups becomes inevitable. Here we present a case study of a use of the distributed analytical environment integrating four complementary specialized resources, namely the Lynx platform, VISTA RViewer, the Developmental Brain Disorders Database (DBDB), and the RaptorX server, for the identification of high-confidence candidate genes contributing to pathogenesis of spina bifida. The analysis resulted in prediction and validation of deleterious mutations in the SLC19A placental transporter in mothers of the affected children that causes narrowing of the outlet channel and therefore leads to the reduced folate permeation rate. The described approach also enabled correct identification of several genes, previously shown to contribute to pathogenesis of spina bifida, and suggestion of additional genes for experimental validations. The study demonstrates that the seamless integration of bioinformatics resources enables fast and efficient prioritization and characterization of genomic factors and molecular networks contributing to the phenotypes of interest.</p></div>", "links"=>[], "tags"=>["DBDB", "genomic", "Developmental Brain Disorders Database", "pathogenesis", "identification", "gene", "spina bifida", "folate permeation rate", "vista", "bioinformatics resources", "phenotype", "Integrative Computational Approach", "SLC 19A placental transporter", "validation"], "article_id"=>1271262, "categories"=>["Biological Sciences"], "users"=>["Inna Dubchak", "Sandhya Balasubramanian", "Sheng Wang", "Cem Meyden", "Dinanath Sulakhe", "Alexander Poliakov", "Daniela Börnigen", "Bingqing Xie", "Andrew Taylor", "Jianzhu Ma", "Alex R. Paciorkowski", "Ghayda M. Mirzaa", "Paul Dave", "Gady Agam", "Jinbo Xu", "Lihadh Al-Gazali", "Christopher E. Mason", "M. Elizabeth Ross", "Natalia Maltsev", "T. Conrad Gilliam"], "doi"=>["http://dx.doi.org/10.1371/journal.pone.0114903"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"http://figshare.com/articles/_An_Integrative_Computational_Approach_for_Prioritization_of_Genomic_Variants_/1271262", "title"=>"An Integrative Computational Approach for Prioritization of Genomic Variants", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-12-15 02:59:00"}

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Relative Metric

{"start_date"=>"2014-01-01T00:00:00Z", "end_date"=>"2014-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Biology and life sciences/Biochemistry", "average_usage"=>[282]}, {"subject_area"=>"/Biology and life sciences/Evolutionary biology", "average_usage"=>[333]}, {"subject_area"=>"/Biology and life sciences/Genetics", "average_usage"=>[306, 482]}, {"subject_area"=>"/Medicine and health sciences/Congenital disorders", "average_usage"=>[267]}]}
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