A Dual Model for Prioritizing Cancer Mutations in the Non-coding Genome Based on Germline and Somatic Events
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{"title"=>"A Dual Model for Prioritizing Cancer Mutations in the Non-coding Genome Based on Germline and Somatic Events", "type"=>"journal", "authors"=>[{"first_name"=>"Jia", "last_name"=>"Li", "scopus_author_id"=>"55878669800"}, {"first_name"=>"Marie Anne", "last_name"=>"Poursat", "scopus_author_id"=>"24765226800"}, {"first_name"=>"Damien", "last_name"=>"Drubay", "scopus_author_id"=>"57194754267"}, {"first_name"=>"Arnaud", "last_name"=>"Motz", "scopus_author_id"=>"56993569700"}, {"first_name"=>"Zohra", "last_name"=>"Saci", "scopus_author_id"=>"56993719400"}, {"first_name"=>"Antonin", "last_name"=>"Morillon", "scopus_author_id"=>"6602786116"}, {"first_name"=>"Stefan", "last_name"=>"Michiels", "scopus_author_id"=>"56036766600"}, {"first_name"=>"Daniel", "last_name"=>"Gautheret", "scopus_author_id"=>"7004015105"}], "year"=>2015, "source"=>"PLoS Computational Biology", "identifiers"=>{"scopus"=>"2-s2.0-84949222300", "sgr"=>"84949222300", "doi"=>"10.1371/journal.pcbi.1004583", "pui"=>"607183833", "pmid"=>"26588488", "issn"=>"15537358"}, "id"=>"dda8a843-f6eb-326e-bc3b-6325b4a626f4", "abstract"=>"We address here the issue of prioritizing non-coding mutations in the tumoral genome. To this aim, we created two independent computational models. The first (germline) model estimates purifying selection based on population SNP data. The second (somatic) model estimates tumor mutation density based on whole genome tumor sequencing. We show that each model reflects a different set of constraints acting either on the normal or tumor genome, and we identify the specific genome features that most contribute to these constraints. Importantly, we show that the somatic mutation model carries independent functional information that can be used to narrow down the non-coding regions that may be relevant to cancer progression. On this basis, we identify positions in non-coding RNAs and the non-coding parts of mRNAs that are both under purifying selection in the germline and protected from mutation in tumors, thus introducing a new strategy for future detection of cancer driver elements in the expressed non-coding genome.", "link"=>"http://www.mendeley.com/research/dual-model-prioritizing-cancer-mutations-noncoding-genome-based-germline-somatic-events", "reader_count"=>50, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>2, "Librarian"=>2, "Researcher"=>13, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>13, "Student > Postgraduate"=>1, "Other"=>2, "Student > Master"=>7, "Student > Bachelor"=>5, "Professor"=>3}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>2, "Librarian"=>2, "Researcher"=>13, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>13, "Student > Postgraduate"=>1, "Other"=>2, "Student > Master"=>7, "Student > Bachelor"=>5, "Professor"=>3}, "reader_count_by_subject_area"=>{"Unspecified"=>2, "Engineering"=>3, "Biochemistry, Genetics and Molecular Biology"=>13, "Mathematics"=>3, "Agricultural and Biological Sciences"=>22, "Medicine and Dentistry"=>2, "Chemical Engineering"=>2, "Computer Science"=>3}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>3}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>22}, "Computer Science"=>{"Computer Science"=>3}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>13}, "Mathematics"=>{"Mathematics"=>3}, "Unspecified"=>{"Unspecified"=>2}, "Chemical Engineering"=>{"Chemical Engineering"=>2}}, "reader_count_by_country"=>{"United States"=>1, "Germany"=>1, "Spain"=>1}, "group_count"=>3}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/2448344", "https://ndownloader.figshare.com/files/2448345", "https://ndownloader.figshare.com/files/2448346", "https://ndownloader.figshare.com/files/2448347", "https://ndownloader.figshare.com/files/2448348", "https://ndownloader.figshare.com/files/2448349", "https://ndownloader.figshare.com/files/2448350", "https://ndownloader.figshare.com/files/2448351", "https://ndownloader.figshare.com/files/2448353", "https://ndownloader.figshare.com/files/2448354", "https://ndownloader.figshare.com/files/2448355", "https://ndownloader.figshare.com/files/2448357", "https://ndownloader.figshare.com/files/2448358", "https://ndownloader.figshare.com/files/2448360", "https://ndownloader.figshare.com/files/2448362"], "description"=>"<div><p>We address here the issue of prioritizing non-coding mutations in the tumoral genome. To this aim, we created two independent computational models. The first (germline) model estimates purifying selection based on population SNP data. The second (somatic) model estimates tumor mutation density based on whole genome tumor sequencing. We show that each model reflects a different set of constraints acting either on the normal or tumor genome, and we identify the specific genome features that most contribute to these constraints. Importantly, we show that the somatic mutation model carries independent functional information that can be used to narrow down the non-coding regions that may be relevant to cancer progression. On this basis, we identify positions in non-coding RNAs and the non-coding parts of mRNAs that are both under purifying selection in the germline and protected from mutation in tumors, thus introducing a new strategy for future detection of cancer driver elements in the expressed non-coding genome.</p></div>", "links"=>[], "tags"=>["population SNP data", "mutation model", "future detection", "Dual Model", "genome tumor sequencing", "cancer driver elements", "rna", "tumor genome", "cancer progression", "germline", "genome Features", "Model estimates", "Somatic Events", "Prioritizing Cancer Mutations", "constraint", "model estimates tumor mutation density", "tumoral genome"], "article_id"=>1609234, "categories"=>["Biological Sciences"], "users"=>["Jia Li", "Marie-Anne Poursat", "Damien Drubay", "Arnaud Motz", "Zohra Saci", "Antonin Morillon", "Stefan Michiels", "Daniel Gautheret"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1004583.s001", "https://dx.doi.org/10.1371/journal.pcbi.1004583.s002", "https://dx.doi.org/10.1371/journal.pcbi.1004583.s003", "https://dx.doi.org/10.1371/journal.pcbi.1004583.s004", "https://dx.doi.org/10.1371/journal.pcbi.1004583.s005", "https://dx.doi.org/10.1371/journal.pcbi.1004583.s006", "https://dx.doi.org/10.1371/journal.pcbi.1004583.s007", "https://dx.doi.org/10.1371/journal.pcbi.1004583.s008", "https://dx.doi.org/10.1371/journal.pcbi.1004583.s009", "https://dx.doi.org/10.1371/journal.pcbi.1004583.s010", "https://dx.doi.org/10.1371/journal.pcbi.1004583.s011", "https://dx.doi.org/10.1371/journal.pcbi.1004583.s012", "https://dx.doi.org/10.1371/journal.pcbi.1004583.s013", "https://dx.doi.org/10.1371/journal.pcbi.1004583.s014", "https://dx.doi.org/10.1371/journal.pcbi.1004583.s015"], "stats"=>{"downloads"=>10, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_Dual_Model_for_Prioritizing_Cancer_Mutations_in_the_Non_coding_Genome_Based_on_Germline_and_Somatic_Events_/1609234", "title"=>"A Dual Model for Prioritizing Cancer Mutations in the Non-coding Genome Based on Germline and Somatic Events", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-11-20 15:04:27"}
  • {"files"=>["https://ndownloader.figshare.com/files/2448336"], "description"=>"<p>Positive values indicate enrichment, negative values indicate depletion. Hypomutated (high SNP, low SOM) positions were mapped onto genome features (A) or genes from three different classes (Protein-coding, lncRNA, miRNA) (B). For each feature or gene class, enrichment for hypomutated positions was computed as explained in Methods. As hypomutated positions are cancer-specific, different results are obtained for each cancer class (colored dots). Shaded grey areas show enrichment ranges obtained from 1000 random permutations (see <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004583#sec007\" target=\"_blank\">Methods</a>).</p>", "links"=>[], "tags"=>["population SNP data", "mutation model", "future detection", "Dual Model", "genome tumor sequencing", "cancer driver elements", "rna", "tumor genome", "cancer progression", "germline", "genome Features", "Model estimates", "Somatic Events", "Prioritizing Cancer Mutations", "constraint", "model estimates tumor mutation density", "tumoral genome"], "article_id"=>1609227, "categories"=>["Biological Sciences"], "users"=>["Jia Li", "Marie-Anne Poursat", "Damien Drubay", "Arnaud Motz", "Zohra Saci", "Antonin Morillon", "Stefan Michiels", "Daniel Gautheret"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004583.g004", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Enrichment_for_hypomutated_positions_within_different_genome_features_A_and_gene_classes_B_/1609227", "title"=>"Enrichment for hypomutated positions within different genome features (A) and gene classes (B).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-11-20 15:04:27"}
  • {"files"=>["https://ndownloader.figshare.com/files/2448329"], "description"=>"<p><b>A.</b> Fraction of rare SNPs (allele frequency <0.01) according to different genome features (see <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004583#pcbi.1004583.s008\" target=\"_blank\">S1 Table</a> and <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004583#sec007\" target=\"_blank\">Methods</a> for feature details). Each box shows rare SNP fraction across all human chromosomes, except chr. Y. CDS: coding sequence; cTFBS: conserved transcription factor binding site; CR: evolutionarily conserved region; UTR: untranslated region; Sensitive: region with high rate of rare SNP defined in [<a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004583#pcbi.1004583.ref010\" target=\"_blank\">10</a>], ER/LR: early and late replicated region; DNase: DNase I hypersensitive site; HE/LE: high and low expressed region; Intron L/Intron P: intron of lncRNA/of protein coding gene; ncExon: non coding exon; ECS: evolutionarily conserved structure; RR H/RR L/GC H/GC L: high recombination rate, low recombination rate, high GC content and low GC content regions. The red dotted line represents the average fraction of rare SNPs across the genome. <b>B.</b> Feature importance as measured by IncNodePurity. We only show here features that passed feature selection. <b>C</b>. Distribution of SNP scores for random SNPs and for clinical variants from the Clivariants and HGMD databases. Random SNPs here are a set of 1M random intergenic SNPs from the 1000 Genome project. <b>D</b>. Correlation of SNP scores with densities of disease-causing variants. Genome positions were sorted by SNP score and split into 20 Mb intervals. The plots show the average SNP score and density of disease-causing variants for each interval. The purple dotted line shows cutoff used for defining high SNP score thereafter.</p>", "links"=>[], "tags"=>["population SNP data", "mutation model", "future detection", "Dual Model", "genome tumor sequencing", "cancer driver elements", "rna", "tumor genome", "cancer progression", "germline", "genome Features", "Model estimates", "Somatic Events", "Prioritizing Cancer Mutations", "constraint", "model estimates tumor mutation density", "tumoral genome"], "article_id"=>1609220, "categories"=>["Biological Sciences"], "users"=>["Jia Li", "Marie-Anne Poursat", "Damien Drubay", "Arnaud Motz", "Zohra Saci", "Antonin Morillon", "Stefan Michiels", "Daniel Gautheret"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004583.g001", "stats"=>{"downloads"=>2, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Construction_of_the_rare_SNP_model_/1609220", "title"=>"Construction of the rare SNP model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-11-20 15:04:27"}
  • {"files"=>["https://ndownloader.figshare.com/files/2448331"], "description"=>"<p><b>A</b>. Relative density of somatic mutations from whole genome sequences of 88 liver tumors [<a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004583#pcbi.1004583.ref011\" target=\"_blank\">11</a>], associated to different genome features (see <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004583#sec007\" target=\"_blank\">Methods</a> for feature details). Mutation density is normalized so that the whole genome average has a mutation density of 1. PC gene: protein coding gene; CDS: coding sequence; Exon.P, Intron.P, Exon.L,Intron.L are exon and intron of protein coding gene and lncRNA respectively; CR: conserved region; DNase: DNase I hypersensitive site; ECS: evolutionarily conserved structure; ncExon: non-coding exon; PC gene.HE, LncRNA.HE, PC gene.LE and LncRNA.LE are high expressed and low expressed protein coding gene and lncRNA; PC gene.early, LncRNA.early, PC gene.late and LncRNA.late are early and late replicated protein coding gene and lncRNA; cTFBS: conserved transcription factor binding site;RR H,RR L,GC H,GC L,DNA.met H and DNA.met L are 1-Kb windows with high recombination rate (> 4.0), low recombination rate (< 0.5), high GC content (GC % > 50%), low GC content (GC%<30%), high DNA methylation (average value > 0.7245) and low DNA methylation (average value < 0.4062) respectively; Blue and red dotted lines: base lines showing average values for CDS and intergenic regions, respectively; <b>B:</b> Feature importance as measured by IncNodePurity. We only show here features that passed feature selection. <b>C</b>. Distribution of SOM scores for neutral SNPs and for clinical variants from two disease-causing variants databases Clivariant and HGMD. Neutral SNPs here are SNPs from the 1000 Genome project with allele frequency higher than 0.01, SOM scores predicted by the random forest model were divided by the number of patients. <b>D</b>. Correlation of SOM score with densities of disease-causing variants. Genome positions were sorted by SOM score and split into 100Mb intervals. The plots show the average SOM score and density of disease-causing variants for each interval. The purple dotted line shows cutoff used for defining low SOM score thereafter.</p>", "links"=>[], "tags"=>["population SNP data", "mutation model", "future detection", "Dual Model", "genome tumor sequencing", "cancer driver elements", "rna", "tumor genome", "cancer progression", "germline", "genome Features", "Model estimates", "Somatic Events", "Prioritizing Cancer Mutations", "constraint", "model estimates tumor mutation density", "tumoral genome"], "article_id"=>1609222, "categories"=>["Biological Sciences"], "users"=>["Jia Li", "Marie-Anne Poursat", "Damien Drubay", "Arnaud Motz", "Zohra Saci", "Antonin Morillon", "Stefan Michiels", "Daniel Gautheret"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004583.g002", "stats"=>{"downloads"=>0, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Construction_of_the_Somatic_Mutation_SOM_model_for_liver_cancer_/1609222", "title"=>"Construction of the Somatic Mutation (SOM) model for liver cancer.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-11-20 15:04:27"}
  • {"files"=>["https://ndownloader.figshare.com/files/2448332"], "description"=>"<p>Grey dots: 1 million random genome positions; cyan contour: HGMD disease-causing variant positions; red contour: Clivariant positions. The top and right curves show marginal distributions of SNP scores (top) and SOM scores (right) for random genome positions, HGMD and Clivariant disease-causing variant positions. Dotted lines define cutoff values for hypomutated/hypermutated regions. SNP score cutoff = 0.63 (98.16Mb above cutoff), SOM score cutoffs = 3.10 variants/Mb, defining areas below cutoff of 55.67 Mb, in liver cancer. Hypomutated regions defined by both cutoff correspond to ~56Mb in liver cancer type.</p>", "links"=>[], "tags"=>["population SNP data", "mutation model", "future detection", "Dual Model", "genome tumor sequencing", "cancer driver elements", "rna", "tumor genome", "cancer progression", "germline", "genome Features", "Model estimates", "Somatic Events", "Prioritizing Cancer Mutations", "constraint", "model estimates tumor mutation density", "tumoral genome"], "article_id"=>1609223, "categories"=>["Biological Sciences"], "users"=>["Jia Li", "Marie-Anne Poursat", "Damien Drubay", "Arnaud Motz", "Zohra Saci", "Antonin Morillon", "Stefan Michiels", "Daniel Gautheret"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004583.g003", "stats"=>{"downloads"=>0, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Relationship_between_SNP_and_SOM_scores_in_liver_cancer_/1609223", "title"=>"Relationship between SNP and SOM scores in liver cancer.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-11-20 15:04:27"}

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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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