Mitotic Recombination: Why? When? How? Where?
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{"title"=>"Mitotic recombination: why? when? how? where?", "type"=>"generic", "authors"=>[{"first_name"=>"Matthew C.", "last_name"=>"Lafave", "scopus_author_id"=>"57192064641"}, {"first_name"=>"Jeff", "last_name"=>"Sekelsky", "scopus_author_id"=>"6603810144"}], "year"=>2009, "source"=>"PLoS Genetics", "identifiers"=>{"isbn"=>"1553-7404 (Electronic)\\n1553-7390 (Linking)", "pui"=>"354404849", "issn"=>"15537390", "pmid"=>"19282976", "doi"=>"10.1371/journal.pgen.1000411", "sgr"=>"63449132961", "scopus"=>"2-s2.0-63449132961"}, "id"=>"9c98387c-16b2-3765-b700-5aca38ec25b9", "abstract"=>"MOTIVATION Copy number alterations (CNAs) associated with cancer are known to contribute to genomic instability and gene deregulation. Integrating CNAs with gene expression helps to elucidate the mechanisms by which CNAs act and to identify the transcriptional downstream targets of CNAs. Such analyses can help to sort functional driver events from the many accompanying passenger alterations. However, the way CNAs affect gene expression can vary in different cellular contexts, for example between different subtypes of the same cancer. Thus, it is important to develop computational approaches capable of inferring differential connectivity of regulatory networks in different cellular contexts. RESULTS We propose a statistical deregulation model that integrates copy number and expression data of different disease subtypes to jointly model common and differential regulatory relationships. Our model not only identifies CNAs driving gene expression changes, but at the same time also predicts differences in regulation that distinguish one cancer subtype from the other. We implement our model in a penalized regression framework and demonstrate in a simulation study the feasibility and accuracy of our approach. Subsequently, we show that this model can identify both known and novel aspects of cross-talk between the ER and NOTCH pathways in ER-negative-specific deregulations, when compared with ER-positive breast cancer. This flexible model can be applied on other modalities such as methylation or microRNA and expression to disentangle cancer signaling pathways. AVAILABILITY The Bioconductor-compliant R package DANCE is available from www.markowetzlab.org/software/ CONTACT yinyin.yuan@cancer.org.uk; florian.markowetz@cancer.org.uk.", "link"=>"http://www.mendeley.com/research/mitotic-recombination-10", "reader_count"=>113, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>4, "Researcher"=>26, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>42, "Student > Postgraduate"=>2, "Student > Master"=>16, "Student > Bachelor"=>10, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>2, "Professor"=>8}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>4, "Researcher"=>26, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>42, "Student > Postgraduate"=>2, "Student > Master"=>16, "Student > Bachelor"=>10, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>2, "Professor"=>8}, "reader_count_by_subject_area"=>{"Unspecified"=>1, "Engineering"=>2, "Environmental Science"=>3, "Biochemistry, Genetics and Molecular Biology"=>12, "Mathematics"=>2, "Agricultural and Biological Sciences"=>83, "Medicine and Dentistry"=>6, "Pharmacology, Toxicology and Pharmaceutical Science"=>1, "Chemistry"=>1, "Computer Science"=>2}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>2}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>6}, "Chemistry"=>{"Chemistry"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>83}, "Computer Science"=>{"Computer Science"=>2}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>12}, "Mathematics"=>{"Mathematics"=>2}, "Unspecified"=>{"Unspecified"=>1}, "Environmental Science"=>{"Environmental Science"=>3}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>1}}, "reader_count_by_country"=>{"Lebanon"=>1, "United States"=>4, "Denmark"=>1, "Brazil"=>1, "Italy"=>1, "United Kingdom"=>1, "Mexico"=>1, "Chile"=>1, "Switzerland"=>1, "Germany"=>1, "India"=>2}, "group_count"=>0}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/905338"], "description"=>"<p>(A) An RCO is depicted between chromatids of two homologous chromosomes. One segregation pattern results in daughter cells that have become homozygous for the sequence distal to the crossover site. (B–D) A close-up view of the region outlined by the dotted box, showing different gene conversion tract configurations detectable using markers <i>a</i> through <i>d</i>. (B) No conversion tract, either because there was no gene conversion or the tract was too small to be detected with the markers available. All markers are still present in a 2∶2 ratio. (C) A typical gene conversion event produces a tract that alters some of the markers (<i>b</i> and <i>c</i>) to a 3∶1 ratio. Note that conversion tracts can only be detected if both reciprocal products (i.e., both daughter cells) are recovered and analyzed, as done by Lee et al. (D) Lee et al. observed some tracts that were wholly or partially 4∶0. In the example shown here, marker <i>b</i> has segregated 4∶0, but marker <i>c</i> has segregated 3∶1; this is therefore a 4∶0/3∶1 hybrid gene conversion tract.</p>", "links"=>[], "tags"=>["crossovers"], "article_id"=>575799, "categories"=>["Genetics"], "users"=>["Matthew C. LaFave", "Jeff Sekelsky"], "doi"=>["https://dx.doi.org/10.1371/journal.pgen.1000411.g001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Reciprocal_crossovers_and_gene_conversion_/575799", "title"=>"Reciprocal crossovers and gene conversion.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 06:35:46"}

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Relative Metric

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