Family-Based Benchmarking of Copy Number Variation Detection Software
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{"title"=>"Family-based benchmarking of copy number variation detection software", "type"=>"journal", "authors"=>[{"first_name"=>"Marcel Elie", "last_name"=>"Nutsua", "scopus_author_id"=>"56835694100"}, {"first_name"=>"Annegret", "last_name"=>"Fischer", "scopus_author_id"=>"56895502800"}, {"first_name"=>"Almut", "last_name"=>"Nebel", "scopus_author_id"=>"12776717800"}, {"first_name"=>"Sylvia", "last_name"=>"Hofmann", "scopus_author_id"=>"17734630500"}, {"first_name"=>"Stefan", "last_name"=>"Schreiber", "scopus_author_id"=>"7402870125"}, {"first_name"=>"Michael", "last_name"=>"Krawczak", "scopus_author_id"=>"7006351366"}, {"first_name"=>"Michael", "last_name"=>"Nothnagel", "scopus_author_id"=>"16203778000"}], "year"=>2015, "source"=>"PLoS ONE", "identifiers"=>{"scopus"=>"2-s2.0-84941348687", "pmid"=>"26197066", "issn"=>"19326203", "pui"=>"605939301", "doi"=>"10.1371/journal.pone.0133465", "sgr"=>"84941348687"}, "id"=>"c24d591c-1242-3ecf-91a2-ab77aa061ed6", "abstract"=>"The analysis of structural variants, in particular of copy-number variations (CNVs), has proven valuable in unraveling the genetic basis of human diseases. Hence, a large number of algorithms have been developed for the detection of CNVs in SNP array signal intensity data. Using the European and African HapMap trio data, we undertook a comparative evaluation of six commonly used CNV detection software tools, namely Affymetrix Power Tools (APT), QuantiSNP, PennCNV, GLAD, R-gada and VEGA, and assessed their level of pair-wise prediction concordance. The tool-specific CNV prediction accuracy was assessed in silico by way of intra-familial validation. Software tools differed greatly in terms of the number and length of the CNVs predicted as well as the number of markers included in a CNV. All software tools predicted substantially more deletions than duplications. Intra-familial validation revealed consistently low levels of prediction accuracy as measured by the proportion of validated CNVs (34-60%). Moreover, up to 20% of apparent family-based validations were found to be due to chance alone. Software using Hidden Markov models (HMM) showed a trend to predict fewer CNVs than segmentation-based algorithms albeit with greater validity. PennCNV yielded the highest prediction accuracy (60.9%). Finally, the pairwise concordance of CNV prediction was found to vary widely with the software tools involved. We recommend HMM-based software, in particular PennCNV, rather than segmentation-based algorithms when validity is the primary concern of CNV detection. QuantiSNP may be used as an additional tool to detect sets of CNVs not detectable by the other tools. Our study also reemphasizes the need for laboratory-based validation, such as qPCR, of CNVs predicted in silico.", "link"=>"http://www.mendeley.com/research/familybased-benchmarking-copy-number-variation-detection-software", "reader_count"=>21, "reader_count_by_academic_status"=>{"Researcher"=>7, "Student > Ph. D. Student"=>8, "Student > Bachelor"=>1, "Student > Master"=>2, "Student > Postgraduate"=>3}, "reader_count_by_user_role"=>{"Researcher"=>7, "Student > Ph. D. Student"=>8, "Student > Bachelor"=>1, "Student > Master"=>2, "Student > Postgraduate"=>3}, "reader_count_by_subject_area"=>{"Biochemistry, Genetics and Molecular Biology"=>5, "Mathematics"=>1, "Agricultural and Biological Sciences"=>8, "Medicine and Dentistry"=>4, "Computer Science"=>3}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>4}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>8}, "Computer Science"=>{"Computer Science"=>3}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>5}, "Mathematics"=>{"Mathematics"=>1}}, "reader_count_by_country"=>{"Korea (South)"=>1, "United States"=>1}, "group_count"=>0}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/2185278"], "description"=>"<p>Given are the median and, in parentheses, the inter-quartile range per sample. <b>DDR:</b> Ratio of deletions to duplications.</p><p>Sample-specific CNV validation rate.</p>", "links"=>[], "tags"=>["hmm", "SNP array signal intensity data", "vega", "African HapMap trio data", "apt", "prediction", "Affymetrix Power Tools", "Software tools", "Copy Number Variation Detection Software", "GLAD", "CNV detection software tools"], "article_id"=>1490816, "categories"=>["Biological Sciences"], "users"=>["Marcel Elie Nutsua", "Annegret Fischer", "Almut Nebel", "Sylvia Hofmann", "Stefan Schreiber", "Michael Krawczak", "Michael Nothnagel"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0133465.t002", "stats"=>{"downloads"=>8, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Sample_specific_CNV_validation_rate_/1490816", "title"=>"Sample-specific CNV validation rate.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-07-21 03:47:39"}
  • {"files"=>["https://ndownloader.figshare.com/files/2185279"], "description"=>"<p>Given are the median and, in parentheses, the inter-quartile range per sample. <b>DDR:</b> Ratio of deletions to duplications,</p><p>Sample-specific features of validated CNVs.</p>", "links"=>[], "tags"=>["hmm", "SNP array signal intensity data", "vega", "African HapMap trio data", "apt", "prediction", "Affymetrix Power Tools", "Software tools", "Copy Number Variation Detection Software", "GLAD", "CNV detection software tools"], "article_id"=>1490817, "categories"=>["Biological Sciences"], "users"=>["Marcel Elie Nutsua", "Annegret Fischer", "Almut Nebel", "Sylvia Hofmann", "Stefan Schreiber", "Michael Krawczak", "Michael Nothnagel"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0133465.t003", "stats"=>{"downloads"=>10, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Sample_specific_features_of_validated_CNVs_/1490817", "title"=>"Sample-specific features of validated CNVs.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-07-21 03:47:39"}
  • {"files"=>["https://ndownloader.figshare.com/files/2185280"], "description"=>"<p>Given are the median and, in parentheses, the inter-quartile range.</p><p>Family-based CNV validation by chance alone?</p>", "links"=>[], "tags"=>["hmm", "SNP array signal intensity data", "vega", "African HapMap trio data", "apt", "prediction", "Affymetrix Power Tools", "Software tools", "Copy Number Variation Detection Software", "GLAD", "CNV detection software tools"], "article_id"=>1490818, "categories"=>["Biological Sciences"], "users"=>["Marcel Elie Nutsua", "Annegret Fischer", "Almut Nebel", "Sylvia Hofmann", "Stefan Schreiber", "Michael Krawczak", "Michael Nothnagel"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0133465.t004", "stats"=>{"downloads"=>11, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Family_based_CNV_validation_by_chance_alone_/1490818", "title"=>"Family-based CNV validation by chance alone?", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-07-21 03:47:39"}
  • {"files"=>["https://ndownloader.figshare.com/files/2185300", "https://ndownloader.figshare.com/files/2185301", "https://ndownloader.figshare.com/files/2185302", "https://ndownloader.figshare.com/files/2185303", "https://ndownloader.figshare.com/files/2185304", "https://ndownloader.figshare.com/files/2185305", "https://ndownloader.figshare.com/files/2185306", "https://ndownloader.figshare.com/files/2185307", "https://ndownloader.figshare.com/files/2185308", "https://ndownloader.figshare.com/files/2185309"], "description"=>"<div><p>The analysis of structural variants, in particular of copy-number variations (CNVs), has proven valuable in unraveling the genetic basis of human diseases. Hence, a large number of algorithms have been developed for the detection of CNVs in SNP array signal intensity data. Using the European and African HapMap trio data, we undertook a comparative evaluation of six commonly used CNV detection software tools, namely Affymetrix Power Tools (APT), QuantiSNP, PennCNV, GLAD, R-gada and VEGA, and assessed their level of pair-wise prediction concordance. The tool-specific CNV prediction accuracy was assessed in silico by way of intra-familial validation. Software tools differed greatly in terms of the number and length of the CNVs predicted as well as the number of markers included in a CNV. All software tools predicted substantially more deletions than duplications. Intra-familial validation revealed consistently low levels of prediction accuracy as measured by the proportion of validated CNVs (34-60%). Moreover, up to 20% of apparent family-based validations were found to be due to chance alone. Software using Hidden Markov models (HMM) showed a trend to predict fewer CNVs than segmentation-based algorithms albeit with greater validity. PennCNV yielded the highest prediction accuracy (60.9%). Finally, the pairwise concordance of CNV prediction was found to vary widely with the software tools involved. We recommend HMM-based software, in particular PennCNV, rather than segmentation-based algorithms when validity is the primary concern of CNV detection. QuantiSNP may be used as an additional tool to detect sets of CNVs not detectable by the other tools. Our study also reemphasizes the need for laboratory-based validation, such as qPCR, of CNVs predicted in silico.</p></div>", "links"=>[], "tags"=>["hmm", "SNP array signal intensity data", "vega", "African HapMap trio data", "apt", "prediction", "Affymetrix Power Tools", "Software tools", "Copy Number Variation Detection Software", "GLAD", "CNV detection software tools"], "article_id"=>1490836, "categories"=>["Biological Sciences"], "users"=>["Marcel Elie Nutsua", "Annegret Fischer", "Almut Nebel", "Sylvia Hofmann", "Stefan Schreiber", "Michael Krawczak", "Michael Nothnagel"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0133465.s001", "https://dx.doi.org/10.1371/journal.pone.0133465.s002", "https://dx.doi.org/10.1371/journal.pone.0133465.s003", "https://dx.doi.org/10.1371/journal.pone.0133465.s004", "https://dx.doi.org/10.1371/journal.pone.0133465.s005", "https://dx.doi.org/10.1371/journal.pone.0133465.s006", "https://dx.doi.org/10.1371/journal.pone.0133465.s007", "https://dx.doi.org/10.1371/journal.pone.0133465.s008", "https://dx.doi.org/10.1371/journal.pone.0133465.s009", "https://dx.doi.org/10.1371/journal.pone.0133465.s010"], "stats"=>{"downloads"=>55, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Family_Based_Benchmarking_of_Copy_Number_Variation_Detection_Software_/1490836", "title"=>"Family-Based Benchmarking of Copy Number Variation Detection Software", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-07-21 03:47:39"}
  • {"files"=>["https://ndownloader.figshare.com/files/2185272"], "description"=>"<p><b>A:</b> Number of CNVs predicted per sample. <b>B:</b> Proportion of CNVs per sample validated by parental information. <b>Light grey:</b> HMM-based algorithms; <b>dark grey:</b> segmentation algorithms.</p>", "links"=>[], "tags"=>["hmm", "SNP array signal intensity data", "vega", "African HapMap trio data", "apt", "prediction", "Affymetrix Power Tools", "Software tools", "Copy Number Variation Detection Software", "GLAD", "CNV detection software tools"], "article_id"=>1490810, "categories"=>["Biological Sciences"], "users"=>["Marcel Elie Nutsua", "Annegret Fischer", "Almut Nebel", "Sylvia Hofmann", "Stefan Schreiber", "Michael Krawczak", "Michael Nothnagel"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0133465.g001", "stats"=>{"downloads"=>2, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_CNV_prediction_and_family_based_validation_/1490810", "title"=>"CNV prediction and family-based validation.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-07-21 03:47:39"}
  • {"files"=>["https://ndownloader.figshare.com/files/2185274"], "description"=>"<p>Kernel-smoothed histogram of the median CNV length per sample. Outliers were excluded. <b>Solid line:</b> HMM-based algorithms; <b>dotted lines:</b> segmentation algorithms.</p>", "links"=>[], "tags"=>["hmm", "SNP array signal intensity data", "vega", "African HapMap trio data", "apt", "prediction", "Affymetrix Power Tools", "Software tools", "Copy Number Variation Detection Software", "GLAD", "CNV detection software tools"], "article_id"=>1490812, "categories"=>["Biological Sciences"], "users"=>["Marcel Elie Nutsua", "Annegret Fischer", "Almut Nebel", "Sylvia Hofmann", "Stefan Schreiber", "Michael Krawczak", "Michael Nothnagel"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0133465.g002", "stats"=>{"downloads"=>5, "page_views"=>22, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Median_sample_specific_CNV_length_/1490812", "title"=>"Median sample-specific CNV length.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-07-21 03:47:39"}
  • {"files"=>["https://ndownloader.figshare.com/files/2185276"], "description"=>"<p><b>Light grey:</b> duplications; <b>dark grey:</b> deletions.</p>", "links"=>[], "tags"=>["hmm", "SNP array signal intensity data", "vega", "African HapMap trio data", "apt", "prediction", "Affymetrix Power Tools", "Software tools", "Copy Number Variation Detection Software", "GLAD", "CNV detection software tools"], "article_id"=>1490814, "categories"=>["Biological Sciences"], "users"=>["Marcel Elie Nutsua", "Annegret Fischer", "Almut Nebel", "Sylvia Hofmann", "Stefan Schreiber", "Michael Krawczak", "Michael Nothnagel"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0133465.g003", "stats"=>{"downloads"=>1, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Median_sample_specific_number_of_validated_CNVs_/1490814", "title"=>"Median sample-specific number of validated CNVs.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-07-21 03:47:39"}
  • {"files"=>["https://ndownloader.figshare.com/files/2185277"], "description"=>"<p>Given are the median and, in parentheses, the inter-quartile range per sample. <b>DDR:</b> Ratio of deletions to duplications.</p><p>Sample-specific features of predicted CNVs.</p>", "links"=>[], "tags"=>["hmm", "SNP array signal intensity data", "vega", "African HapMap trio data", "apt", "prediction", "Affymetrix Power Tools", "Software tools", "Copy Number Variation Detection Software", "GLAD", "CNV detection software tools"], "article_id"=>1490815, "categories"=>["Biological Sciences"], "users"=>["Marcel Elie Nutsua", "Annegret Fischer", "Almut Nebel", "Sylvia Hofmann", "Stefan Schreiber", "Michael Krawczak", "Michael Nothnagel"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0133465.t001", "stats"=>{"downloads"=>10, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Sample_specific_features_of_predicted_CNVs_/1490815", "title"=>"Sample-specific features of predicted CNVs.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-07-21 03:47:39"}

PMC Usage Stats | Further Information

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  • {"unique-ip"=>"12", "full-text"=>"9", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"7", "cited-by"=>"0", "year"=>"2018", "month"=>"6"}
  • {"unique-ip"=>"14", "full-text"=>"16", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"5", "cited-by"=>"0", "year"=>"2018", "month"=>"7"}
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  • {"unique-ip"=>"5", "full-text"=>"5", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"10"}
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Relative Metric

{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}
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