PolyaPeak: Detecting Transcription Factor Binding Sites from ChIP-seq Using Peak Shape Information
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{"title"=>"Polya peak: Detecting transcription factor binding sites from Ch IP-seq using peak shape information", "type"=>"journal", "authors"=>[{"first_name"=>"Hao", "last_name"=>"Wu", "scopus_author_id"=>"56732087700"}, {"first_name"=>"Hongkai", "last_name"=>"Ji", "scopus_author_id"=>"8536743300"}], "year"=>2014, "source"=>"PLoS ONE", "identifiers"=>{"doi"=>"10.1371/journal.pone.0089694", "sgr"=>"84897492725", "scopus"=>"2-s2.0-84897492725", "issn"=>"19326203", "pui"=>"372754349"}, "id"=>"1c42bb55-75b5-3986-81d1-41fda0aa23f7", "abstract"=>"ChIP-seq is a powerful technology for detecting genomic regions where a protein of interest interacts with DNA. ChIP-seq data for mapping transcription factor binding sites (TFBSs) have a characteristic pattern: around each binding site, sequence reads aligned to the forward and reverse strands of the reference genome form two separate peaks shifted away from each other, and the true binding site is located in between these two peaks. While it has been shown previously that the accuracy and resolution of binding site detection can be improved by modeling the pattern, efficient methods are unavailable to fully utilize that information in TFBS detection procedure. We present PolyaPeak, a new method to improve TFBS detection by incorporating the peak shape information. PolyaPeak describes peak shapes using a flexible Pó lya model. The shapes are automatically learnt from the data using Minorization-Maximization (MM) algorithm, then integrated with the read count information via a hierarchical model to distinguish true binding sites from background noises. Extensive real data analyses show that PolyaPeak is capable of robustly improving TFBS detection compared with existing methods. An R package is freely available. © 2014 Wu, Ji.", "link"=>"http://www.mendeley.com/research/polya-peak-detecting-transcription-factor-binding-sites-ch-ipseq-using-peak-shape-information", "reader_count"=>2, "reader_count_by_academic_status"=>{"Researcher"=>1, "Student > Ph. D. Student"=>1}, "reader_count_by_user_role"=>{"Researcher"=>1, "Student > Ph. D. Student"=>1}, "reader_count_by_subject_area"=>{"Agricultural and Biological Sciences"=>2}, "reader_count_by_subdiscipline"=>{"Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>2}}, "group_count"=>0}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1412114"], "description"=>"<p>(a) and (b): Peaks ranked high in MACS but low in PolyaPeak. (c) and (d): Peaks ranked low in MACS but high in PolyaPeak.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "algorithms", "software engineering", "Software tools", "Applied mathematics", "statistics", "Biostatistics", "peaks", "differences", "ranks", "macs"], "article_id"=>955517, "categories"=>["Biological Sciences", "Mathematics", "Engineering"], "users"=>["Hao Wu", "Hongkai Ji"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0089694.g004", "stats"=>{"downloads"=>5, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Examples_of_peaks_showing_large_differences_in_ranks_from_MACS_and_PolyaPeak_/955517", "title"=>"Examples of peaks showing large differences in ranks from MACS and PolyaPeak.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-03-07 03:10:20"}
  • {"files"=>["https://ndownloader.figshare.com/files/1412115"], "description"=>"<div><p>ChIP-seq is a powerful technology for detecting genomic regions where a protein of interest interacts with DNA. ChIP-seq data for mapping transcription factor binding sites (TFBSs) have a characteristic pattern: around each binding site, sequence reads aligned to the forward and reverse strands of the reference genome form two separate peaks shifted away from each other, and the true binding site is located in between these two peaks. While it has been shown previously that the accuracy and resolution of binding site detection can be improved by modeling the pattern, efficient methods are unavailable to fully utilize that information in TFBS detection procedure.</p><p>We present PolyaPeak, a new method to improve TFBS detection by incorporating the peak shape information. PolyaPeak describes peak shapes using a flexible Pólya model. The shapes are automatically learnt from the data using Minorization-Maximization (MM) algorithm, then integrated with the read count information via a hierarchical model to distinguish true binding sites from background noises.</p><p>Extensive real data analyses show that PolyaPeak is capable of robustly improving TFBS detection compared with existing methods. An R package is freely available.</p></div>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "algorithms", "software engineering", "Software tools", "Applied mathematics", "statistics", "Biostatistics", "detecting", "transcription", "binding", "sites", "ChIP-seq"], "article_id"=>955518, "categories"=>["Biological Sciences", "Mathematics", "Engineering"], "users"=>["Hao Wu", "Hongkai Ji"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0089694", "stats"=>{"downloads"=>20, "page_views"=>25, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_PolyaPeak_Detecting_Transcription_Factor_Binding_Sites_from_ChIP_seq_Using_Peak_Shape_Information_/955518", "title"=>"PolyaPeak: Detecting Transcription Factor Binding Sites from ChIP-seq Using Peak Shape Information", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-03-07 03:10:20"}
  • {"files"=>["https://ndownloader.figshare.com/files/1412112"], "description"=>"<p>X-axis represents peak ranks. Y-axis represents the percentage of reported peaks containing at least one DNA motif site.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "algorithms", "software engineering", "Software tools", "Applied mathematics", "statistics", "Biostatistics", "motif", "enrichment", "ranked", "peaks", "methods", "reduced", "polyapeak", "pics"], "article_id"=>955515, "categories"=>["Biological Sciences", "Mathematics", "Engineering"], "users"=>["Hao Wu", "Hongkai Ji"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0089694.g003", "stats"=>{"downloads"=>2, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_motif_enrichment_in_top_ranked_peaks_reported_from_different_methods_PolyaPeak_reduced_PolyaPeak_without_peak_shape_information_MACS_CisGenome_PICS_and_GPS_for_six_public_datasets_/955515", "title"=>"Comparison of motif enrichment in top ranked peaks reported from different methods (PolyaPeak, reduced PolyaPeak without peak shape information, MACS, CisGenome, PICS and GPS) for six public datasets.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-03-07 03:10:20"}
  • {"files"=>["https://ndownloader.figshare.com/files/1412110"], "description"=>"<p>The black solid curve is the theoretical density for the negative binomial distribution with parameters estimated from the method of moment procedure. It can be seen that the total counts in background regions can be approximated well by a negative binomial distribution, and the method of moments for estimating negative binomial parameters works well. (d) Estimated peak shape from polyaPeak for K562 MYC data.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "algorithms", "software engineering", "Software tools", "Applied mathematics", "statistics", "Biostatistics", "histogram", "counts", "regions", "tf", "oct4", "es", "myc", "k562", "gabp", "hepg2"], "article_id"=>955513, "categories"=>["Biological Sciences", "Mathematics", "Engineering"], "users"=>["Hao Wu", "Hongkai Ji"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0089694.g002", "stats"=>{"downloads"=>5, "page_views"=>22, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_a_8211_c_histogram_of_total_read_counts_in_background_regions_from_different_datasets_a_TF_OCT4_in_mouse_ES_cells_b_TF_MYC_in_K562_cell_line_and_c_TF_GABP_in_HepG2_cell_line_/955513", "title"=>"(a)–(c): histogram of total read counts in background regions from different datasets: (a) TF OCT4 in mouse ES cells; (b) TF MYC in K562 cell line; and (c) TF GABP in HepG2 cell line.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-03-07 03:10:20"}
  • {"files"=>["https://ndownloader.figshare.com/files/1412108"], "description"=>"<p>X-axis is genomic location centered at the summit of each peak. Y-axis represents ChIP read counts in 10 bp genomic windows. For each peak, data from the IP sample in a 800 bp window surrounding the MACS peak summit is shown. Bars above the middle represent counts from forward strand, and bars below represent the counts from reverse strand. The black rectangles illustrate the TF binding sites. (a) and (b) are two different binding sites from the same ChIP-seq dataset, and (c) is a binding site from a different dataset. They illustrate that peak shapes at true TFBSs are similar but can vary across binding sites and datasets.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "algorithms", "software engineering", "Software tools", "Applied mathematics", "statistics", "Biostatistics", "shapes", "binding", "sites"], "article_id"=>955511, "categories"=>["Biological Sciences", "Mathematics", "Engineering"], "users"=>["Hao Wu", "Hongkai Ji"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0089694.g001", "stats"=>{"downloads"=>8, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Illustration_of_peak_shapes_from_three_good_binding_sites_a_b_c_and_one_false_positive_d_/955511", "title"=>"Illustration of peak shapes from three good binding sites (a, b, c) and one false positive (d).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-03-07 03:10:20"}

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