Modeling Insertional Mutagenesis Using Gene Length and Expression in Murine Embryonic Stem Cells
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{"title"=>"Modeling insertional mutagenesis using gene length and expression in murine embryonic stem cells", "type"=>"journal", "authors"=>[{"first_name"=>"Alex S.", "last_name"=>"Nord", "scopus_author_id"=>"16043399900"}, {"first_name"=>"Karen", "last_name"=>"Vranizan", "scopus_author_id"=>"57197564733"}, {"first_name"=>"Whittemore", "last_name"=>"Tingley", "scopus_author_id"=>"35610390600"}, {"first_name"=>"Alexander C.", "last_name"=>"Zambon", "scopus_author_id"=>"7006480508"}, {"first_name"=>"Kristina", "last_name"=>"Hanspers", "scopus_author_id"=>"17342074100"}, {"first_name"=>"Loren G.", "last_name"=>"Fong", "scopus_author_id"=>"7005621487"}, {"first_name"=>"Yan", "last_name"=>"Hu", "scopus_author_id"=>"7407119441"}, {"first_name"=>"Peter", "last_name"=>"Bacchetti", "scopus_author_id"=>"7004977420"}, {"first_name"=>"Thomas E.", "last_name"=>"Ferrin", "scopus_author_id"=>"6603947110"}, {"first_name"=>"Patricia C.", "last_name"=>"Babbitt", "scopus_author_id"=>"7003654933"}, {"first_name"=>"Scott W.", "last_name"=>"Doniger", "scopus_author_id"=>"8704673000"}, {"first_name"=>"William C.", "last_name"=>"Skarnes", "scopus_author_id"=>"7003292609"}, {"first_name"=>"Stephen G.", "last_name"=>"Young", "scopus_author_id"=>"7404515225"}, {"first_name"=>"Bruce R.", "last_name"=>"Conklin", "scopus_author_id"=>"56381119800"}], "year"=>2007, "source"=>"PLoS ONE", "identifiers"=>{"scopus"=>"2-s2.0-39849088147", "sgr"=>"39849088147", "issn"=>"19326203", "pmid"=>"17637833", "doi"=>"10.1371/journal.pone.0000617", "pui"=>"351314583"}, "id"=>"0d9331ce-5f91-3ce6-aceb-2a3a67d31e0e", "abstract"=>"High-throughput mutagenesis of the mammalian genome is a powerful means to facilitate analysis of gene function. Gene trapping in embryonic stem cells (ESCs) is the most widely used form of insertional mutagenesis in mammals. However, the rules governing its efficiency are not fully understood, and the effects of vector design on the likelihood of gene-trapping events have not been tested on a genome-wide scale.", "link"=>"http://www.mendeley.com/research/modeling-insertional-mutagenesis-using-gene-length-expression-murine-embryonic-stem-cells-2", "reader_count"=>7, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>1, "Researcher"=>2, "Student > Master"=>4}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>1, "Researcher"=>2, "Student > Master"=>4}, "reader_count_by_subject_area"=>{"Biochemistry, Genetics and Molecular Biology"=>1, "Nursing and Health Professions"=>1, "Medicine and Dentistry"=>1, "Agricultural and Biological Sciences"=>1, "Business, Management and Accounting"=>2, "Decision Sciences"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Decision Sciences"=>{"Decision Sciences"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>1}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>1}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>2}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>1}}, "reader_count_by_country"=>{"Portugal"=>1, "Germany"=>1}, "group_count"=>0}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/948495"], "description"=>"<p>Modeled events and genes represent the number of trap events and unique trapped genes considered in the modeling process. <i>P</i> values for expression and length represent likelihood ratio significance tests. Explained deviance is analogous to the percent of the variance that is explained in a linear regression model. Hotspots reported as the number of genes that fell outside the hotspot cut-off, the number of trap events in the hotspot gene set, and as the percent of modeled traps in hotspot genes.</p>", "links"=>[], "tags"=>["Computational biology", "developmental biology", "genetics and genomics", "computational biology/comparative sequence analysis", "computational biology/genomics", "genetics and genomics/animal genetics", "genetics and genomics/bioinformatics", "genetics and genomics/gene expression", "genetics and genomics/gene therapy", "genetics and genomics/genomics", "mathematics/statistics", "molecular biology/bioinformatics", "molecular biology/chromatin structure", "molecular biology/recombination"], "article_id"=>618784, "categories"=>["Mathematics", "Medicine", "Genetics", "Molecular Biology", "Developmental Biology", "Infectious Diseases", "Biological Sciences"], "users"=>["Alex S. Nord", "Karen Vranizan", "Whittemore Tingley", "Alexander C. Zambon", "Kristina Hanspers", "Loren G. Fong", "Yan Hu", "Peter Bacchetti", "Thomas E. Ferrin", "Patricia C. Babbitt", "Scott W. Doniger", "William C. Skarnes", "Stephen G. Young", "Bruce R. Conklin"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0000617.t002", "stats"=>{"downloads"=>6, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Hotspot_Effects_and_Model_Summary_/618784", "title"=>"Hotspot Effects and Model Summary", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2007-07-18 02:26:24"}
  • {"files"=>["https://ndownloader.figshare.com/files/948086"], "description"=>"<p>(A) In the SA-trap, the SA site allows trapping when inserted into any part of the gene <i>via</i> plasmid or viral integration. (B) The poly-A trap relies on the poly-A (pA) of the endogenous gene because the neomycin-resistance gene does not have a poly-A tail. Note that the poly-A trap has its own constitutive promoter (prom). Also indicated are the splice donor (SD), splice acceptor (SA), and neomycin resistance (NeoR). The major components of each trap were excluded from this diagram to emphasize on the essential elements needed to understand the trapping models. Detailed maps of each major vector type are referenced in the <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0000617#s3\" target=\"_blank\">Methods</a> section.</p>", "links"=>[], "tags"=>["mechanisms", "endogenous"], "article_id"=>618401, "categories"=>["Mathematics", "Medicine", "Genetics", "Molecular Biology", "Developmental Biology", "Infectious Diseases", "Biological Sciences"], "users"=>["Alex S. Nord", "Karen Vranizan", "Whittemore Tingley", "Alexander C. Zambon", "Kristina Hanspers", "Loren G. Fong", "Yan Hu", "Peter Bacchetti", "Thomas E. Ferrin", "Patricia C. Babbitt", "Scott W. Doniger", "William C. Skarnes", "Stephen G. Young", "Bruce R. Conklin"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0000617.g001", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Diagram_of_major_mechanisms_of_gene_trapping_of_an_endogenous_gene_with_two_exons_/618401", "title"=>"Diagram of major mechanisms of gene trapping of an endogenous gene with two exons.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2007-07-18 02:20:01"}
  • {"files"=>["https://ndownloader.figshare.com/files/948215"], "description"=>"<p>For each vector type, genes were plotted according to their size and level of expression in ESCs. Genes that have been trapped are shown in red. The circle size is proportional to the number of times a gene has been trapped.</p>", "links"=>[], "tags"=>["genes"], "article_id"=>618530, "categories"=>["Mathematics", "Medicine", "Genetics", "Molecular Biology", "Developmental Biology", "Infectious Diseases", "Biological Sciences"], "users"=>["Alex S. Nord", "Karen Vranizan", "Whittemore Tingley", "Alexander C. Zambon", "Kristina Hanspers", "Loren G. Fong", "Yan Hu", "Peter Bacchetti", "Thomas E. Ferrin", "Patricia C. Babbitt", "Scott W. Doniger", "William C. Skarnes", "Stephen G. Young", "Bruce R. Conklin"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0000617.g002", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Trapped_genes_by_length_and_expression_/618530", "title"=>"Trapped genes by length and expression.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2007-07-18 02:22:10"}
  • {"files"=>["https://ndownloader.figshare.com/files/466554", "https://ndownloader.figshare.com/files/466568"], "description"=>"<div><h3>Background</h3><p>High-throughput mutagenesis of the mammalian genome is a powerful means to facilitate analysis of gene function. Gene trapping in embryonic stem cells (ESCs) is the most widely used form of insertional mutagenesis in mammals. However, the rules governing its efficiency are not fully understood, and the effects of vector design on the likelihood of gene-trapping events have not been tested on a genome-wide scale.</p><h3>Methodology/Principal Findings</h3><p>In this study, we used public gene-trap data to model gene-trap likelihood. Using the association of gene length and gene expression with gene-trap likelihood, we constructed spline-based regression models that characterize which genes are susceptible and which genes are resistant to gene-trapping techniques. We report results for three classes of gene-trap vectors, showing that both length and expression are significant determinants of trap likelihood for all vectors. Using our models, we also quantitatively identified hotspots of gene-trap activity, which represent loci where the high likelihood of vector insertion is controlled by factors other than length and expression. These formalized statistical models describe a high proportion of the variance in the likelihood of a gene being trapped by expression-dependent vectors and a lower, but still significant, proportion of the variance for vectors that are predicted to be independent of endogenous gene expression.</p><h3>Conclusions/Significance</h3><p>The findings of significant expression and length effects reported here further the understanding of the determinants of vector insertion. Results from this analysis can be applied to help identify other important determinants of this important biological phenomenon and could assist planning of large-scale mutagenesis efforts.</p></div>", "links"=>[], "tags"=>["modeling", "insertional", "mutagenesis", "murine", "embryonic", "cells"], "article_id"=>151952, "categories"=>["Mathematics", "Medicine", "Genetics", "Molecular Biology", "Developmental Biology", "Cancer", "Biological Sciences"], "users"=>["Alex S. Nord", "Karen Vranizan", "Whittemore Tingley", "Alexander C. Zambon", "Kristina Hanspers", "Loren G. Fong", "Yan Hu", "Peter Bacchetti", "Thomas E. Ferrin", "Patricia C. Babbitt", "Scott W. Doniger", "William C. Skarnes", "Stephen G. Young", "Bruce R. Conklin"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0000617.s001", "https://dx.doi.org/10.1371/journal.pone.0000617.s002"], "stats"=>{"downloads"=>13, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Modeling_Insertional_Mutagenesis_Using_Gene_Length_and_Expression_in_Murine_Embryonic_Stem_Cells/151952", "title"=>"Modeling Insertional Mutagenesis Using Gene Length and Expression in Murine Embryonic Stem Cells", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2007-07-18 00:32:32"}
  • {"files"=>["https://ndownloader.figshare.com/files/948371"], "description"=>"<p>Models of the likelihood of trapping a gene with particular length (<i>x</i>-axis) and expression (<i>y</i>-axis) values for each gene-trap event were created through an iterative process, in which outliers (<i>P</i><0.001) were removed before the final model was created. Probability (<i>z</i>-axis) is given as events per million traps.</p>", "links"=>[], "tags"=>["gene-trap"], "article_id"=>618642, "categories"=>["Mathematics", "Medicine", "Genetics", "Molecular Biology", "Developmental Biology", "Infectious Diseases", "Biological Sciences"], "users"=>["Alex S. Nord", "Karen Vranizan", "Whittemore Tingley", "Alexander C. Zambon", "Kristina Hanspers", "Loren G. Fong", "Yan Hu", "Peter Bacchetti", "Thomas E. Ferrin", "Patricia C. Babbitt", "Scott W. Doniger", "William C. Skarnes", "Stephen G. Young", "Bruce R. Conklin"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0000617.g003", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Models_of_trap_likelihood_for_gene_trap_vectors_/618642", "title"=>"Models of trap likelihood for gene-trap vectors.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2007-07-18 02:24:02"}
  • {"files"=>["https://ndownloader.figshare.com/files/948589"], "description"=>"<p>Lines, number of cell lines in public gene trap database; Traps, number of gene-trap events mapped to a gene; Genes, total number of unique genes trapped; % in Genes, percent of gene-trap events mapped to exon/intron regions (including UTR) of known genes.</p>", "links"=>[], "tags"=>["Computational biology", "developmental biology", "genetics and genomics", "computational biology/comparative sequence analysis", "computational biology/genomics", "genetics and genomics/animal genetics", "genetics and genomics/bioinformatics", "genetics and genomics/gene expression", "genetics and genomics/gene therapy", "genetics and genomics/genomics", "mathematics/statistics", "molecular biology/bioinformatics", "molecular biology/chromatin structure", "molecular biology/recombination"], "article_id"=>618863, "categories"=>["Mathematics", "Medicine", "Genetics", "Molecular Biology", "Developmental Biology", "Infectious Diseases", "Biological Sciences"], "users"=>["Alex S. Nord", "Karen Vranizan", "Whittemore Tingley", "Alexander C. Zambon", "Kristina Hanspers", "Loren G. Fong", "Yan Hu", "Peter Bacchetti", "Thomas E. Ferrin", "Patricia C. Babbitt", "Scott W. Doniger", "William C. Skarnes", "Stephen G. Young", "Bruce R. Conklin"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0000617.t001", "stats"=>{"downloads"=>3, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Summary_of_gene_trap_data_sets_/618863", "title"=>"Summary of gene trap data sets", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2007-07-18 02:27:43"}

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

Relative Metric

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