Evidence of Influence of Genomic DNA Sequence on Human X Chromosome Inactivation
Publication Date
September 01, 2006
Journal
PLOS Computational Biology
Authors
Zhong Wang, Huntington F Willard, Sayan Mukherjee & Terrence S Furey
Volume
2
Issue
9
Pages
e113
DOI
https://dx.plos.org/10.1371/journal.pcbi.0020113
Publisher URL
http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.0020113
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/16948528
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1557588
Europe PMC
http://europepmc.org/abstract/MED/16948528
Web of Science
000240867500003
Scopus
33749368402
Mendeley
http://www.mendeley.com/research/evidence-influence-genomic-dna-sequence-human-x-chromosome-inactivation-3
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Mendeley | Further Information

{"title"=>"Evidence of influence of genomic DNA sequence on human X chromosome inactivation", "type"=>"journal", "authors"=>[{"first_name"=>"Zhong", "last_name"=>"Wang", "scopus_author_id"=>"57192240878"}, {"first_name"=>"Huntington F.", "last_name"=>"Willard", "scopus_author_id"=>"7103010013"}, {"first_name"=>"Sayan", "last_name"=>"Mukherjee", "scopus_author_id"=>"35426501300"}, {"first_name"=>"Terrence S.", "last_name"=>"Furey", "scopus_author_id"=>"6602302257"}], "year"=>2006, "source"=>"PLoS Computational Biology", "identifiers"=>{"sgr"=>"33749368402", "issn"=>"1553734X", "scopus"=>"2-s2.0-33749368402", "pmid"=>"16948528", "pui"=>"44495714", "isbn"=>"10.1371/journal.pcbi.0020113", "doi"=>"10.1371/journal.pcbi.0020113"}, "id"=>"8f925b62-3c1d-38c4-a691-d432bc123c23", "abstract"=>"A significant number of human X-linked genes escape X chromosome inactivation and are thus expressed from both the active and inactive X chromosomes. The basis for escape from inactivation and the potential role of the X chromosome primary DNA sequence in determining a gene's X inactivation status is unclear. Using a combination of the X chromosome sequence and a comprehensive X inactivation profile of more than 600 genes, two independent yet complementary approaches were used to systematically investigate the relationship between X inactivation and DNA sequence features. First, statistical analyses revealed that a number of repeat features, including long interspersed nuclear element (LINE) and mammalian-wide interspersed repeat repetitive elements, are significantly enriched in regions surrounding transcription start sites of genes that are subject to inactivation, while Alu repetitive elements and short motifs containing ACG/CGT are significantly enriched in those that escape inactivation. Second, linear support vector machine classifiers constructed using primary DNA sequence features were used to correctly predict the X inactivation status for >80% of all X-linked genes. We further identified a small set of features that are important for accurate classification, among which LINE-1 and LINE-2 content show the greatest individual discriminatory power. Finally, as few as 12 features can be used for accurate support vector machine classification. Taken together, these results suggest that features of the underlying primary DNA sequence of the human X chromosome may influence the spreading and/or maintenance of X inactivation.", "link"=>"http://www.mendeley.com/research/evidence-influence-genomic-dna-sequence-human-x-chromosome-inactivation-3", "reader_count"=>27, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>1, "Researcher"=>9, "Student > Ph. D. Student"=>11, "Student > Postgraduate"=>1, "Student > Master"=>4, "Student > Bachelor"=>1}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>1, "Researcher"=>9, "Student > Ph. D. Student"=>11, "Student > Postgraduate"=>1, "Student > Master"=>4, "Student > Bachelor"=>1}, "reader_count_by_subject_area"=>{"Biochemistry, Genetics and Molecular Biology"=>6, "Agricultural and Biological Sciences"=>20, "Medicine and Dentistry"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>20}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>6}}, "reader_count_by_country"=>{"United States"=>1, "Ghana"=>1, "India"=>1}, "group_count"=>1}

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Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/956285"], "description"=>"<div><p>(A) The mean prediction accuracy and standard deviations (<i>y</i>-axis) for 100 recursive feature reduction iterations are shown for the indicated number of genes (<i>x</i>-axis). Green represents the CV rate using randomly selected two-thirds of the XAR nonborder genes for each set of features. The prediction rates for escaping genes (blue) and subject genes (red) in the remaining one-third are also shown. Both escape and subject prediction rates begin declining when the feature set is reduced to fewer than 53 features.</p><p>(B) The content of each feature (<i>y</i>-axis) in specific windows around the transcription start sites for all 82 XAR nonborder genes (<i>x</i>-axis) is represented as a histogram. The first 36 genes on the <i>x</i>-axis escape X inactivation (shaded area), and the remaining 46 are subject to X inactivation. Features found to be consistently chosen during recursive feature reduction for the creation of accurate classifiers are L1 100 kb downstream, MLT1K 100 kb upstream, and MER33 100 kb upstream. For comparison, THE1B 50 kb upstream, a randomly distributed feature, is also shown.</p></div>", "links"=>[], "tags"=>["distributions", "xar", "nonborder"], "article_id"=>626562, "categories"=>["Genetics", "Biological Sciences"], "users"=>["Zhong Wang", "Huntington F Willard", "Sayan Mukherjee", "Terrence S Furey"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.0020113.g003", "stats"=>{"downloads"=>1, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Recursive_Feature_Reduction_and_Distributions_of_Consistent_Features_across_the_XAR_Nonborder_Genes_/626562", "title"=>"Recursive Feature Reduction and Distributions of Consistent Features across the XAR Nonborder Genes", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 13:11:57"}
  • {"files"=>["https://ndownloader.figshare.com/files/956348"], "description"=>"<div><p>(A) The three best principal components (PC1–PC3) among all 5,596 features for 50-kb and 100-kb windows (left) and the selected 12 features (right) for the 82 nonborder genes are shown projected onto a 3-D graph. Escaping genes are represented as blue circles and subject genes as red circles.</p><p>(B) These histograms show the distribution of XAR leave-one-out CV and XCR prediction rates by SVM models constructed using 1,000 random 12-feature sets taken from the 5,596 features for 50-kb and 100-kb windows. Black dots represent mean values, flanked by 95% confidence intervals denoted by error bars representing two standard deviations (2SD). Both the XAR CV rate and XCR prediction rate achieved by the selected 12 features (black arrows) exceed 2SD, and their <i>p</i> values calculated based on these random trials are shown.</p></div>", "links"=>[], "tags"=>["12"], "article_id"=>626629, "categories"=>["Genetics", "Biological Sciences"], "users"=>["Zhong Wang", "Huntington F Willard", "Sayan Mukherjee", "Terrence S Furey"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.0020113.g004", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_Significance_of_the_12_Selected_Features_/626629", "title"=>"The Significance of the 12 Selected Features", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 13:12:29"}
  • {"files"=>["https://ndownloader.figshare.com/files/956402"], "description"=>"<div><p>These histograms summarize the prediction probabilities of genes that are either (A) subject to inactivation (expressed in zero of nine somatic cell hybrids) or (B) escape from inactivation (expressed in nine of nine hybrids) [<a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0020113#pcbi-0020113-b012\" target=\"_blank\">12</a>]. Genes from the XCR, XAR border genes, and nonborder XAR genes coupled with XAR ESTs are represented by different colors.</p><p>XCI, X chromosome inactivation.</p></div>", "links"=>[], "tags"=>["svm", "probabilities", "genes", "inactivation"], "article_id"=>626678, "categories"=>["Genetics", "Biological Sciences"], "users"=>["Zhong Wang", "Huntington F Willard", "Sayan Mukherjee", "Terrence S Furey"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.0020113.g005", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_Distribution_of_SVM_Prediction_Probabilities_for_Genes_with_Known_X_Inactivation_Status_/626678", "title"=>"The Distribution of SVM Prediction Probabilities for Genes with Known X Inactivation Status", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 13:12:57"}
  • {"files"=>["https://ndownloader.figshare.com/files/956194"], "description"=>"<p>For each boxplot, horizontal lines indicate the locations of the lower quartile, median, and upper quartile values. Notches represent a robust estimate of the uncertainty about the medians for box-to-box comparison. Boxes whose notches do not overlap indicate that the medians of the two groups differ at the 0.05 significance level. Clear boxes represent genes that escape inactivation, while shaded boxes represent genes subject to inactivation [<a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0020113#pcbi-0020113-b012\" target=\"_blank\">12</a>]. Analyses were done with all genes for which X inactivation status is known (<i>n</i> = 448) or with genes from the XAR region (<i>n</i> = 110). <i>y</i>-Axes in all plots represent the average content of each sequence feature in 100-kb windows surrounding the transcription starts of genes.</p>", "links"=>[], "tags"=>["3-mers", "100-kb", "windows", "transcription", "starts"], "article_id"=>626471, "categories"=>["Genetics", "Biological Sciences"], "users"=>["Zhong Wang", "Huntington F Willard", "Sayan Mukherjee", "Terrence S Furey"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.0020113.g002", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Boxplots_Representing_the_Content_of_Alu_L1_L2_MIR_and_ACG_CGT_3_mers_in_100_kb_Windows_Surrounding_the_Transcription_Starts_of_Genes_/626471", "title"=>"Boxplots Representing the Content of Alu, L1, L2, MIR, and ACG/CGT 3-mers in 100-kb Windows Surrounding the Transcription Starts of Genes", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 13:11:15"}
  • {"files"=>["https://ndownloader.figshare.com/files/470708", "https://ndownloader.figshare.com/files/470752", "https://ndownloader.figshare.com/files/470813", "https://ndownloader.figshare.com/files/470911", "https://ndownloader.figshare.com/files/470952", "https://ndownloader.figshare.com/files/470972", "https://ndownloader.figshare.com/files/470988", "https://ndownloader.figshare.com/files/471025", "https://ndownloader.figshare.com/files/471041"], "description"=>"<div><p>A significant number of human X-linked genes escape X chromosome inactivation and are thus expressed from both the active and inactive X chromosomes. The basis for escape from inactivation and the potential role of the X chromosome primary DNA sequence in determining a gene's X inactivation status is unclear. Using a combination of the X chromosome sequence and a comprehensive X inactivation profile of more than 600 genes, two independent yet complementary approaches were used to systematically investigate the relationship between X inactivation and DNA sequence features. First, statistical analyses revealed that a number of repeat features, including long interspersed nuclear element (LINE) and mammalian-wide interspersed repeat repetitive elements, are significantly enriched in regions surrounding transcription start sites of genes that are subject to inactivation, while Alu repetitive elements and short motifs containing ACG/CGT are significantly enriched in those that escape inactivation. Second, linear support vector machine classifiers constructed using primary DNA sequence features were used to correctly predict the X inactivation status for >80% of all X-linked genes. We further identified a small set of features that are important for accurate classification, among which LINE-1 and LINE-2 content show the greatest individual discriminatory power. Finally, as few as 12 features can be used for accurate support vector machine classification. Taken together, these results suggest that features of the underlying primary DNA sequence of the human X chromosome may influence the spreading and/or maintenance of X inactivation.</p></div>", "links"=>[], "tags"=>["genomic", "dna", "chromosome", "inactivation"], "article_id"=>152828, "categories"=>["Genetics", "Biological Sciences"], "users"=>["Zhong Wang", "Huntington F Willard", "Sayan Mukherjee", "Terrence S Furey"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.0020113.sg001", "https://dx.doi.org/10.1371/journal.pcbi.0020113.sg002", "https://dx.doi.org/10.1371/journal.pcbi.0020113.sg003", "https://dx.doi.org/10.1371/journal.pcbi.0020113.st001", "https://dx.doi.org/10.1371/journal.pcbi.0020113.st002", "https://dx.doi.org/10.1371/journal.pcbi.0020113.st003", "https://dx.doi.org/10.1371/journal.pcbi.0020113.st004", "https://dx.doi.org/10.1371/journal.pcbi.0020113.st005", "https://dx.doi.org/10.1371/journal.pcbi.0020113.st006"], "stats"=>{"downloads"=>21, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Evidence_of_Influence_of_Genomic_DNA_Sequence_on_Human_X_Chromosome_Inactivation/152828", "title"=>"Evidence of Influence of Genomic DNA Sequence on Human X Chromosome Inactivation", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2006-09-01 00:47:08"}
  • {"files"=>["https://ndownloader.figshare.com/files/956057"], "description"=>"<div><p>(A) A schematic drawing of the X chromosome delineating each evolutionary stratum [<a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0020113#pcbi-0020113-b013\" target=\"_blank\">13</a>,<a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0020113#pcbi-0020113-b015\" target=\"_blank\">15</a>].</p><p>(B) Strategy for statistical analysis and SVM training and classification.</p></div>", "links"=>[], "tags"=>["Computational biology", "genetics and genomics", "genetics and genomics/chromosome biology", "genetics and genomics/epigenetics"], "article_id"=>626334, "categories"=>["Genetics", "Biological Sciences"], "users"=>["Zhong Wang", "Huntington F Willard", "Sayan Mukherjee", "Terrence S Furey"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.0020113.g001", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Data_Analysis_Strategy_/626334", "title"=>"Data Analysis Strategy", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 13:10:12"}

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Relative Metric

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