RFECS: A Random-Forest Based Algorithm for Enhancer Identification from Chromatin State
Publication Date
March 14, 2013
Journal
PLoS Computational Biology
Authors
Nisha Rajagopal, Wei Xie, Yan Li, Uli Wagner, et al
Volume
9
Issue
3
Pages
e1002968
DOI
https://dx.plos.org/10.1371/journal.pcbi.1002968
Publisher URL
http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1002968
Web of Science
000316864200039
Scopus
84876007572
Mendeley
http://www.mendeley.com/research/rfecs-randomforest-based-algorithm-enhancer-identification-chromatin-state
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Mendeley | Further Information

{"title"=>"RFECS: A Random-Forest Based Algorithm for Enhancer Identification from Chromatin State", "type"=>"journal", "authors"=>[{"first_name"=>"Nisha", "last_name"=>"Rajagopal", "scopus_author_id"=>"54397647000"}, {"first_name"=>"Wei", "last_name"=>"Xie", "scopus_author_id"=>"56868049500"}, {"first_name"=>"Yan", "last_name"=>"Li", "scopus_author_id"=>"55882827700"}, {"first_name"=>"Uli", "last_name"=>"Wagner", "scopus_author_id"=>"55968945600"}, {"first_name"=>"Wei", "last_name"=>"Wang", "scopus_author_id"=>"57191738639"}, {"first_name"=>"John", "last_name"=>"Stamatoyannopoulos", "scopus_author_id"=>"6603232343"}, {"first_name"=>"Jason", "last_name"=>"Ernst", "scopus_author_id"=>"10039724300"}, {"first_name"=>"Manolis", "last_name"=>"Kellis", "scopus_author_id"=>"6602385400"}, {"first_name"=>"Bing", "last_name"=>"Ren", "scopus_author_id"=>"7102054727"}], "year"=>2013, "source"=>"PLoS Computational Biology", "identifiers"=>{"pui"=>"368694334", "isbn"=>"1553-7358 (Electronic)\\r1553-734X (Linking)", "issn"=>"1553734X", "doi"=>"10.1371/journal.pcbi.1002968", "scopus"=>"2-s2.0-84876007572", "pmid"=>"23526891", "sgr"=>"84876007572"}, "id"=>"0979c3b2-7286-30fc-a4de-3bae07c63d9a", "abstract"=>"Author Summary Enhancers are regions in the genome that can activate the expression of a gene irrespective of their location with respect to the gene. Identifying these elements is critical in understanding regulatory differences between different cell-types. Since enhancers lack characteristic sequence features and can be far away from the gene they regulate, their identification is not trivial. Experimentally determining the genome-wide binding sites of transcriptional co-activator p300 is one way of finding enhancers but it can only identify a subset of enhancers. A few years ago, it was observed that the binding sites of p300 are marked by distinctive, post-translational histone modifications. Several groups have exploited this discovery to predict genome-wide enhancers based on their similarity to the histone modification profiles of p300 binding sites. We here report a novel algorithm for this purpose and show that it has much greater accuracy than existing methods. Another unique feature of our algorithm is the ability to automatically deduce the most informative set of histone modifications required for enhancer prediction. We expect that this method will become increasingly useful with the expanding number of known histone modifications and rapid accumulation of epigenomic datasets for various cell types and species.", "link"=>"http://www.mendeley.com/research/rfecs-randomforest-based-algorithm-enhancer-identification-chromatin-state", "reader_count"=>234, "reader_count_by_academic_status"=>{"Unspecified"=>3, "Professor > Associate Professor"=>14, "Researcher"=>71, "Student > Doctoral Student"=>11, "Student > Ph. D. Student"=>86, "Student > Postgraduate"=>5, "Other"=>5, "Student > Master"=>19, "Student > Bachelor"=>7, "Lecturer > Senior Lecturer"=>1, "Professor"=>12}, "reader_count_by_user_role"=>{"Unspecified"=>3, "Professor > Associate Professor"=>14, "Researcher"=>71, "Student > Doctoral Student"=>11, "Student > Ph. D. Student"=>86, "Student > Postgraduate"=>5, "Other"=>5, "Student > Master"=>19, "Student > Bachelor"=>7, "Lecturer > Senior Lecturer"=>1, "Professor"=>12}, "reader_count_by_subject_area"=>{"Unspecified"=>8, "Agricultural and Biological Sciences"=>140, "Arts and Humanities"=>1, "Chemistry"=>1, "Computer Science"=>30, "Economics, Econometrics and Finance"=>1, "Engineering"=>3, "Environmental Science"=>2, "Biochemistry, Genetics and Molecular Biology"=>33, "Mathematics"=>4, "Medicine and Dentistry"=>6, "Neuroscience"=>1, "Physics and Astronomy"=>1, "Social Sciences"=>1, "Immunology and Microbiology"=>2}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>6}, "Social Sciences"=>{"Social Sciences"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Mathematics"=>{"Mathematics"=>4}, "Unspecified"=>{"Unspecified"=>8}, "Environmental Science"=>{"Environmental Science"=>2}, "Arts and Humanities"=>{"Arts and Humanities"=>1}, "Engineering"=>{"Engineering"=>3}, "Chemistry"=>{"Chemistry"=>1}, "Neuroscience"=>{"Neuroscience"=>1}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>1}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>140}, "Computer Science"=>{"Computer Science"=>30}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>33}}, "reader_count_by_country"=>{"United States"=>12, "United Kingdom"=>2, "Portugal"=>1, "Spain"=>2, "Canada"=>2, "Netherlands"=>3, "Korea (South)"=>1, "Iran"=>1, "Luxembourg"=>1, "China"=>2, "Finland"=>1, "Italy"=>2, "France"=>1, "Germany"=>4}, "group_count"=>17}

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Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/987408"], "description"=>"<p>The average variable of histone modifications across 5 cross-sections of data in 2 sets of replicates as well as averaged replicates using all 24 modifications in A.)H1 and B.)IMR90 cells. Out-of-bag variable importance was calculated from the random-forest based classification of p300 binding sites against TSS+genomic background. Robust appearance of H3K4me1, H3K4me3 and H3K4me2 among the most important marks across replicates and cell types, indicates these may form a minimal set for prediction of enhancers. Differences observed in correlation clustering of the same 24 modifications in C.)H1 and D.)IMR90 explain some of the differences in ordering of variables in the two cell types. Same non-black colors of modifications indicate clusters that co-occur in both cell-types.</p>", "links"=>[], "tags"=>["histone", "modifications", "enhancer"], "article_id"=>652172, "categories"=>["Information And Computing Sciences"], "users"=>["Nisha Rajagopal", "Wei Xie", "Yan Li", "Uli Wagner", "Wei Wang", "John Stamatoyannopoulos", "Jason Ernst", "Manolis Kellis", "Bing Ren"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002968.g003", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Out_of_bag_variable_importance_of_histone_modifications_in_enhancer_prediction_/652172", "title"=>"Out-of-bag variable importance of histone modifications in enhancer prediction.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-15 10:04:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/987405"], "description"=>"<p>Area under the 5-fold cross-validated ROC curve decreases with increase in number of trees stabilizing gradually in A.)H1 and B.)IMR90 cells. C.)Validation Rate of enhancer predicted in H1 cells, as measured by overlap with DNase-I HS and binding sites of p300, NANOG, OCT4 and SOX2. D.)Misclassification Rate of enhancer predicted using RFECS in H1 as measured as overlap of UCSC TSS, E.)Validation Rate of enhancers predicted by RFECS in IMR90 as measured by overlap with DNase-I HS or p300 binding sites in the same cells. F.)Misclassification Rate of enhancers predicted by RFECS in IMR90 as measured by overlap with UCSC TSS, versus total number of enhancers (upto 40000 enhancers) determined by taking different enrichment cutoffs, are shown for forest trained in the same cell type (⋅red), forest trained in other cell type and predictions made on modifications with averaged RPKM (⋅black), replicate 1 only (⋅blue), and replicate 2 only (⋅green). Training on one replicate and prediction on the other replicate of the same cell-type are indicated by asterisks.</p>", "links"=>[], "tags"=>["rfecs", "enhancer", "predictions", "h1", "imr90"], "article_id"=>652169, "categories"=>["Information And Computing Sciences"], "users"=>["Nisha Rajagopal", "Wei Xie", "Yan Li", "Uli Wagner", "Wei Wang", "John Stamatoyannopoulos", "Jason Ernst", "Manolis Kellis", "Bing Ren"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002968.g002", "stats"=>{"downloads"=>0, "page_views"=>18, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Performance_of_RFECS_for_enhancer_predictions_in_H1_and_IMR90_cells_/652169", "title"=>"Performance of RFECS for enhancer predictions in H1 and IMR90 cells.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-15 10:03:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/987436", "https://ndownloader.figshare.com/files/987438", "https://ndownloader.figshare.com/files/987440", "https://ndownloader.figshare.com/files/987442", "https://ndownloader.figshare.com/files/987443", "https://ndownloader.figshare.com/files/987445", "https://ndownloader.figshare.com/files/987448", "https://ndownloader.figshare.com/files/987451", "https://ndownloader.figshare.com/files/987452", "https://ndownloader.figshare.com/files/987453", "https://ndownloader.figshare.com/files/987454", "https://ndownloader.figshare.com/files/987455", "https://ndownloader.figshare.com/files/987456", "https://ndownloader.figshare.com/files/987457", "https://ndownloader.figshare.com/files/987459", "https://ndownloader.figshare.com/files/987460", "https://ndownloader.figshare.com/files/987461", "https://ndownloader.figshare.com/files/987462", "https://ndownloader.figshare.com/files/987463", "https://ndownloader.figshare.com/files/987464", "https://ndownloader.figshare.com/files/987465", "https://ndownloader.figshare.com/files/987470"], "description"=>"<div><p>Transcriptional enhancers play critical roles in regulation of gene expression, but their identification in the eukaryotic genome has been challenging. Recently, it was shown that enhancers in the mammalian genome are associated with characteristic histone modification patterns, which have been increasingly exploited for enhancer identification. However, only a limited number of cell types or chromatin marks have previously been investigated for this purpose, leaving the question unanswered whether there exists an optimal set of histone modifications for enhancer prediction in different cell types. Here, we address this issue by exploring genome-wide profiles of 24 histone modifications in two distinct human cell types, embryonic stem cells and lung fibroblasts. We developed a Random-Forest based algorithm, RFECS (<u>R</u>andom <u>F</u>orest based <u>E</u>nhancer identification from <u>C</u>hromatin <u>S</u>tates) to integrate histone modification profiles for identification of enhancers, and used it to identify enhancers in a number of cell-types. We show that RFECS not only leads to more accurate and precise prediction of enhancers than previous methods, but also helps identify the most informative and robust set of three chromatin marks for enhancer prediction.</p> </div>", "links"=>[], "tags"=>["random-forest", "algorithm", "enhancer", "chromatin", "state"], "article_id"=>652200, "categories"=>["Information And Computing Sciences"], "users"=>["Nisha Rajagopal", "Wei Xie", "Yan Li", "Uli Wagner", "Wei Wang", "John Stamatoyannopoulos", "Jason Ernst", "Manolis Kellis", "Bing Ren"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1002968.s001", "https://dx.doi.org/10.1371/journal.pcbi.1002968.s002", "https://dx.doi.org/10.1371/journal.pcbi.1002968.s003", "https://dx.doi.org/10.1371/journal.pcbi.1002968.s004", "https://dx.doi.org/10.1371/journal.pcbi.1002968.s005", "https://dx.doi.org/10.1371/journal.pcbi.1002968.s006", "https://dx.doi.org/10.1371/journal.pcbi.1002968.s007", "https://dx.doi.org/10.1371/journal.pcbi.1002968.s008", "https://dx.doi.org/10.1371/journal.pcbi.1002968.s009", "https://dx.doi.org/10.1371/journal.pcbi.1002968.s010", "https://dx.doi.org/10.1371/journal.pcbi.1002968.s011", "https://dx.doi.org/10.1371/journal.pcbi.1002968.s012", "https://dx.doi.org/10.1371/journal.pcbi.1002968.s013", "https://dx.doi.org/10.1371/journal.pcbi.1002968.s014", "https://dx.doi.org/10.1371/journal.pcbi.1002968.s015", "https://dx.doi.org/10.1371/journal.pcbi.1002968.s016", "https://dx.doi.org/10.1371/journal.pcbi.1002968.s017", "https://dx.doi.org/10.1371/journal.pcbi.1002968.s018", "https://dx.doi.org/10.1371/journal.pcbi.1002968.s019", "https://dx.doi.org/10.1371/journal.pcbi.1002968.s020", "https://dx.doi.org/10.1371/journal.pcbi.1002968.s021", "https://dx.doi.org/10.1371/journal.pcbi.1002968.s022"], "stats"=>{"downloads"=>96, "page_views"=>44, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/RFECS_A_Random_Forest_Based_Algorithm_for_Enhancer_Identification_from_Chromatin_State__/652200", "title"=>"RFECS: A Random-Forest Based Algorithm for Enhancer Identification from Chromatin State", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2013-03-15 10:10:35"}
  • {"files"=>["https://ndownloader.figshare.com/files/987403"], "description"=>"<p>A.)Chromatin states for p300 binding sites in H1 cells. B.)Chromatin states for p300 binding sties identified in IMR90 cells, identified by clustering using ChromaSig <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002968#pcbi.1002968-Hon1\" target=\"_blank\">[48]</a>. The heatmap shows RPKM-normalized histone modification levels in 100 bp bins from −5 to +5 kb along p300 binding sites overlapping DHS and distal to known TSS.</p>", "links"=>[], "tags"=>["modification", "patterns", "distal", "p300", "binding", "sites", "h1"], "article_id"=>652166, "categories"=>["Information And Computing Sciences"], "users"=>["Nisha Rajagopal", "Wei Xie", "Yan Li", "Uli Wagner", "Wei Wang", "John Stamatoyannopoulos", "Jason Ernst", "Manolis Kellis", "Bing Ren"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002968.g001", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Histone_modification_patterns_at_distal_p300_binding_sites_in_H1_and_IMR90_/652166", "title"=>"Histone modification patterns at distal p300 binding sites in H1 and IMR90.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-15 10:02:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/987412"], "description"=>"<p>A.) Validation Rate in H1 measured by overlap with DNase-I HS, p300, NANOG, OCT4 or SOX2, B.) Misclassification Rate in H1 measured as overlap of UCSC TSS, C.) Validation Rate in IMR90 measured by overlap with DNase-I HS or p300, D.) Misclassification Rate in IMR90 measured as overlap of UCSC TSS, versus total number of enhancers determined by taking different enrichment cutoffs, are shown for all 24 modifications (red), predicted minimal set of H3K4me1/H3K4me2/H3K4me3 (green) and conventionally used marks H3K4me1/H3K4me3 (black) or H3K4me1/H3K4me3/H3K27ac (blue). E.) Comparison of average validation rates for enhancer predictions using all combinations of 3 histone modifications for 2 replicates of H1.</p>", "links"=>[], "tags"=>["misclassification", "enhancers", "rfecs", "h1"], "article_id"=>652176, "categories"=>["Information And Computing Sciences"], "users"=>["Nisha Rajagopal", "Wei Xie", "Yan Li", "Uli Wagner", "Wei Wang", "John Stamatoyannopoulos", "Jason Ernst", "Manolis Kellis", "Bing Ren"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002968.g004", "stats"=>{"downloads"=>0, "page_views"=>20, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Validation_rate_and_Misclassification_rate_of_enhancers_predicted_using_RFECS_in_H1_and_IMR90_/652176", "title"=>"Validation rate and Misclassification rate of enhancers predicted using RFECS in H1 and IMR90.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-15 10:04:33"}
  • {"files"=>["https://ndownloader.figshare.com/files/987418"], "description"=>"<p>A.)Validation Rate in the 12 cell-types measured by overlap with DNase-I HS, B.)Misclassification Rate in the cell-types measured as overlap of UCSC TSS, C.)Average false discovery rate (FDR) over the 22 autosomal chromosomes for each cell-type plotted as a function of voting percentage of trees, D.)Validation rate and misclassification rate for each cell-type at a FDR of 5% with number of enhancer predictions shown above the bar.</p>", "links"=>[], "tags"=>["predictions", "encode", "cell-lines"], "article_id"=>652182, "categories"=>["Information And Computing Sciences"], "users"=>["Nisha Rajagopal", "Wei Xie", "Yan Li", "Uli Wagner", "Wei Wang", "John Stamatoyannopoulos", "Jason Ernst", "Manolis Kellis", "Bing Ren"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002968.g006", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Enhancer_predictions_in_ENCODE_cell_lines_using_RFECS_/652182", "title"=>"Enhancer predictions in ENCODE cell-lines using RFECS.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-15 10:05:29"}
  • {"files"=>["https://ndownloader.figshare.com/files/987414"], "description"=>"<p>A.) In CD4. True positive rates were measured as overlap with either DNase-I hypersensitive sites (DHS), p300 or CBP binding sites, while false positives were measured as overlap with UCSC TSS. B.) In H1. True positive rates were measured as overlap with either DNase-I hypersensitive sites (DHS), p300 or transcription factor binding sites such as NANOG, OCT4 and SOX2, while false positives were measured as overlap with UCSC TSS.</p>", "links"=>[], "tags"=>["enhancer", "predictions", "csiann"], "article_id"=>652178, "categories"=>["Information And Computing Sciences"], "users"=>["Nisha Rajagopal", "Wei Xie", "Yan Li", "Uli Wagner", "Wei Wang", "John Stamatoyannopoulos", "Jason Ernst", "Manolis Kellis", "Bing Ren"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002968.g005", "stats"=>{"downloads"=>1, "page_views"=>50, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_enhancer_predictions_using_RFECS_ChromaGenSVM_CSIANN_and_Chromia_/652178", "title"=>"Comparison of enhancer predictions using RFECS, ChromaGenSVM, CSIANN and Chromia.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-15 10:04:55"}

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Relative Metric

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