A Model-Based Clustering Method for Genomic Structural Variant Prediction and Genotyping Using Paired-End Sequencing Data
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{"title"=>"A Model-Based Clustering Method for Genomic Structural Variant Prediction and Genotyping Using Paired-End Sequencing Data", "type"=>"journal", "authors"=>[{"first_name"=>"Matthew", "last_name"=>"Hayes", "scopus_author_id"=>"57191372756"}, {"first_name"=>"Yoon Soo", "last_name"=>"Pyon", "scopus_author_id"=>"35318252500"}, {"first_name"=>"Jing", "last_name"=>"Li", "scopus_author_id"=>"19034841800"}], "year"=>2012, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "isbn"=>"1932-6203", "sgr"=>"84871668815", "doi"=>"10.1371/journal.pone.0052881", "scopus"=>"2-s2.0-84871668815", "pui"=>"368006025", "pmid"=>"23300804"}, "id"=>"157f3db6-78c8-3efc-8921-ca9923a72a92", "abstract"=>"Structural variation (SV) has been reported to be associated with numerous diseases such as cancer. With the advent of next generation sequencing (NGS) technologies, various types of SV can be potentially identified. We propose a model based clustering approach utilizing a set of features defined for each type of SV events. Our method, termed SVMiner, not only provides a probability score for each candidate, but also predicts the heterozygosity of genomic deletions. Extensive experiments on genome-wide deep sequencing data have demonstrated that SVMiner is robust against the variability of a single cluster feature, and it significantly outperforms several commonly used SV detection programs. SVMiner can be downloaded from http://cbc.case.edu/svminer/.", "link"=>"http://www.mendeley.com/research/modelbased-clustering-method-genomic-structural-variant-prediction-genotyping-using-pairedend-sequen", "reader_count"=>49, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>1, "Researcher"=>20, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>15, "Student > Postgraduate"=>1, "Student > Master"=>3, "Other"=>1, "Student > Bachelor"=>2, "Lecturer > Senior Lecturer"=>1, "Professor"=>3}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>1, "Researcher"=>20, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>15, "Student > Postgraduate"=>1, "Student > Master"=>3, "Other"=>1, "Student > Bachelor"=>2, "Lecturer > Senior Lecturer"=>1, "Professor"=>3}, "reader_count_by_subject_area"=>{"Engineering"=>2, "Unspecified"=>1, "Biochemistry, Genetics and Molecular Biology"=>7, "Mathematics"=>1, "Agricultural and Biological Sciences"=>25, "Medicine and Dentistry"=>2, "Physics and Astronomy"=>1, "Computer Science"=>10}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>2}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>2}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>25}, "Computer Science"=>{"Computer Science"=>10}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>7}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>1}}, "reader_count_by_country"=>{"New Zealand"=>1, "Sweden"=>1, "United States"=>3, "China"=>2, "United Kingdom"=>1, "France"=>1}, "group_count"=>2}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/516652"], "description"=>"<p>(a) The clustering results of the candidate deletions of dataset I (NA18507). (b) Plot of uncertainty for predicted results from the same dataset. Uncertainty is defined as 1−<i>z</i>, where <i>z</i> is the greatest of the class membership probabilities after clustering. The larger, darker circles indicate lower membership probabilities for the given data point. The highest degrees of uncertainties are around cluster boundaries and around the origin where the data features are not prominent enough to allow for a more confident prediction. Data points with prominent features (i.e. high concordant pair depth and/or high number of discordant pairs) are generally classified with high confidence. (c) The clustering results of the candidate deletions of dataset III (chromosome X of NA07340).</p>", "links"=>[], "tags"=>["deletion", "events"], "article_id"=>187152, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Genetics"], "users"=>["Matthew Hayes", "Yoon Soo Pyon", "Jing Li"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0052881.g002", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Predicted_deletion_events_and_their_uncertainty_scores_/187152", "title"=>"Predicted deletion events and their uncertainty scores.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-12-27 01:59:12"}
  • {"files"=>["https://ndownloader.figshare.com/files/517140"], "description"=>"<p>The three constructed sets of reliable events are: R_I) intersection of events from the 1KG project and events predicted by any method; R_II) events predicted by at least two methods; R_III) events from Kidd et al. The three approaches are SVMiner (SVM), BreakDancerMax (BDM) and VariationHunterWeighted (VHW).</p>", "links"=>[], "tags"=>["deletion"], "article_id"=>187643, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Genetics"], "users"=>["Matthew Hayes", "Yoon Soo Pyon", "Jing Li"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0052881.t004", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_deletion_results_from_three_different_approaches_/187643", "title"=>"Comparison of deletion results from three different approaches.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-12-27 02:07:23"}
  • {"files"=>["https://ndownloader.figshare.com/files/279752", "https://ndownloader.figshare.com/files/279809", "https://ndownloader.figshare.com/files/279854", "https://ndownloader.figshare.com/files/279887", "https://ndownloader.figshare.com/files/279932", "https://ndownloader.figshare.com/files/279974", "https://ndownloader.figshare.com/files/280007", "https://ndownloader.figshare.com/files/280062", "https://ndownloader.figshare.com/files/280107", "https://ndownloader.figshare.com/files/280141", "https://ndownloader.figshare.com/files/280179"], "description"=>"<div><p>Structural variation (SV) has been reported to be associated with numerous diseases such as cancer. With the advent of next generation sequencing (NGS) technologies, various types of SV can be potentially identified. We propose a model based clustering approach utilizing a set of features defined for each type of SV events. Our method, termed SVMiner, not only provides a probability score for each candidate, but also predicts the heterozygosity of genomic deletions. Extensive experiments on genome-wide deep sequencing data have demonstrated that SVMiner is robust against the variability of a single cluster feature, and it significantly outperforms several commonly used SV detection programs. SVMiner can be downloaded from <a href=\"http://cbc.case.edu/svminer/\">http://cbc.case.edu/svminer/</a>.</p> </div>", "links"=>[], "tags"=>["model-based", "clustering", "genomic", "variant", "genotyping", "paired-end", "sequencing", "data"], "article_id"=>115162, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Genetics"], "users"=>["Matthew Hayes", "Yoon Soo Pyon", "Jing Li"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0052881.s001", "https://dx.doi.org/10.1371/journal.pone.0052881.s002", "https://dx.doi.org/10.1371/journal.pone.0052881.s003", "https://dx.doi.org/10.1371/journal.pone.0052881.s004", "https://dx.doi.org/10.1371/journal.pone.0052881.s005", "https://dx.doi.org/10.1371/journal.pone.0052881.s006", "https://dx.doi.org/10.1371/journal.pone.0052881.s007", "https://dx.doi.org/10.1371/journal.pone.0052881.s008", "https://dx.doi.org/10.1371/journal.pone.0052881.s009", "https://dx.doi.org/10.1371/journal.pone.0052881.s010", "https://dx.doi.org/10.1371/journal.pone.0052881.s011"], "stats"=>{"downloads"=>2, "page_views"=>18, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/A_Model_Based_Clustering_Method_for_Genomic_Structural_Variant_Prediction_and_Genotyping_Using_Paired_End_Sequencing_Data__/115162", "title"=>"A Model-Based Clustering Method for Genomic Structural Variant Prediction and Genotyping Using Paired-End Sequencing Data", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2012-12-27 01:26:02"}
  • {"files"=>["https://ndownloader.figshare.com/files/517115"], "description"=>"<p>The relationship between paired-end reads and structural variants according to a partition based on their separation distances and orientations. The meanings of symbols: F: forward read; R: reverse read; d: separation distance; μ: insert size (mean); σ: standard deviation; <i>x</i>: a user-defined parameter; N: normal; D: deletion; I: insertion; Iv: inversion; Ta: tandem duplication; Tra: intrachromosomal translocation; Tre: interchromosomal translocation; C: other complex structure event.</p>", "links"=>[], "tags"=>["genetics and genomics", "Computational biology", "computer science", "mathematics"], "article_id"=>187610, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Genetics"], "users"=>["Matthew Hayes", "Yoon Soo Pyon", "Jing Li"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0052881.t001", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Classification_of_read_pairs_/187610", "title"=>"Classification of read pairs.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-12-27 02:06:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/517072"], "description"=>"*<p>For MoDIL, only 137 high confident ones that have overlaps with real events were counted when considering homozygous/heterozygous predictions, its F_score would be extremely low if all predicted events considered.</p>", "links"=>[], "tags"=>["svminer", "modil"], "article_id"=>187567, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Genetics"], "users"=>["Matthew Hayes", "Yoon Soo Pyon", "Jing Li"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0052881.t003", "stats"=>{"downloads"=>4, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_SVMiner_and_MoDIL_on_two_datasets_/187567", "title"=>"Comparison of SVMiner and MoDIL on two datasets.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-12-27 02:06:07"}
  • {"files"=>["https://ndownloader.figshare.com/files/516879"], "description"=>"<p>The distributions of events sizes detected by SVMiner based on short and long read datasets (Red: based on long reads but overlap with events based on short reads; green: based on short reads but overlap with events based on long reads; blue: events detected only from long reads; orange: events detected only from short reads).</p>", "links"=>[], "tags"=>["distributions", "events"], "article_id"=>187376, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Genetics"], "users"=>["Matthew Hayes", "Yoon Soo Pyon", "Jing Li"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0052881.g004", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Size_distributions_of_events_detected_using_short_and_long_read_data_/187376", "title"=>"Size distributions of events detected using short and long read data.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-12-27 02:02:56"}
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  • {"files"=>["https://ndownloader.figshare.com/files/517173"], "description"=>"<p>The overlaps among predicted inversion events by SVMiner, three versions of VariationHunter, and the events detected using a different technology (Kidd et al). The total number of events by each approach is provided in the parentheses.</p>", "links"=>[], "tags"=>["overlaps", "inversion"], "article_id"=>187673, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Genetics"], "users"=>["Matthew Hayes", "Yoon Soo Pyon", "Jing Li"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0052881.t005", "stats"=>{"downloads"=>1, "page_views"=>44, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_overlaps_among_predicted_inversion_events_/187673", "title"=>"The overlaps among predicted inversion events.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-12-27 02:07:53"}
  • {"files"=>["https://ndownloader.figshare.com/files/516968"], "description"=>"<p>The result of the predicted inversions for the NA18507 specimen. The blue points are predicted inversions and the red points are classified as no events.</p>", "links"=>[], "tags"=>["genetics and genomics", "Computational biology", "computer science", "mathematics"], "article_id"=>187461, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Genetics"], "users"=>["Matthew Hayes", "Yoon Soo Pyon", "Jing Li"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0052881.g005", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Inversion_results_/187461", "title"=>"Inversion results.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-12-27 02:04:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/516595"], "description"=>"<p>Pairs from a donor genome “D” have distinct characteristics when mapped to a reference genome “R” for different types of structure variants.</p>", "links"=>[], "tags"=>["variants", "pairs"], "article_id"=>187094, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Genetics"], "users"=>["Matthew Hayes", "Yoon Soo Pyon", "Jing Li"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0052881.g001", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Structural_variants_and_the_read_pairs_that_support_them_/187094", "title"=>"Structural variants and the read pairs that support them.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-12-27 01:58:14"}
  • {"files"=>["https://ndownloader.figshare.com/files/517046"], "description"=>"<p>Mapping results and SV calling results of SVMiner using different alignment tools and different parameter thresholds.</p>", "links"=>[], "tags"=>["sv", "calling", "svminer", "alignment", "tools", "parameter"], "article_id"=>187539, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Genetics"], "users"=>["Matthew Hayes", "Yoon Soo Pyon", "Jing Li"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0052881.t002", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Mapping_results_and_SV_calling_results_of_SVMiner_using_different_alignment_tools_and_different_parameter_thresholds_/187539", "title"=>"Mapping results and SV calling results of SVMiner using different alignment tools and different parameter thresholds.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-12-27 02:05:39"}

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  • {"unique-ip"=>"1", "full-text"=>"1", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"60", "supp-data"=>"62", "cited-by"=>"0", "year"=>"2018", "month"=>"5"}
  • {"unique-ip"=>"2", "full-text"=>"3", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"6"}
  • {"unique-ip"=>"12", "full-text"=>"9", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"4", "cited-by"=>"0", "year"=>"2018", "month"=>"7"}
  • {"unique-ip"=>"7", "full-text"=>"2", "pdf"=>"5", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"8"}
  • {"unique-ip"=>"3", "full-text"=>"3", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"2", "cited-by"=>"0", "year"=>"2018", "month"=>"11"}
  • {"unique-ip"=>"2", "full-text"=>"1", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"10"}
  • {"unique-ip"=>"5", "full-text"=>"4", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"12"}
  • {"unique-ip"=>"4", "full-text"=>"2", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"4", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"9"}
  • {"unique-ip"=>"2", "full-text"=>"1", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"1", "year"=>"2019", "month"=>"2"}
  • {"unique-ip"=>"6", "full-text"=>"4", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"1", "year"=>"2019", "month"=>"3"}
  • {"unique-ip"=>"3", "full-text"=>"3", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"4"}

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/Computational biology", "average_usage"=>[375, 629, 760, 889, 1000, 1110, 1203, 1298, 1399, 1492, 1603, 1699, 1774, 1855, 1918, 1998, 2062, 2152, 2227, 2312, 2378, 2461, 2528, 2600, 2664]}]}
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