Universal Count Correction for High-Throughput Sequencing
Publication Date
March 06, 2014
Journal
PLOS Computational Biology
Authors
Tatsunori B. Hashimoto, Matthew D. Edwards & David K. Gifford
Volume
10
Issue
3
Pages
e1003494
DOI
https://dx.plos.org/10.1371/journal.pcbi.1003494
Publisher URL
http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1003494
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/24603409
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3945112
Europe PMC
http://europepmc.org/abstract/MED/24603409
Web of Science
000336509000017
Scopus
84897406933
Mendeley
http://www.mendeley.com/research/universal-count-correction-highthroughput-sequencing
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Mendeley | Further Information

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Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1411027"], "description"=>"<p>The distribution described as log-concave is the statistical model used in Fixseq. Subfigure (a) shows that de-duplication Poisson can control high per-base errors much like overdispersed models, but Subfigure (b) shows that de-duplication error rises rapidly as sequencing depth increases.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "statistics", "Statistical methods", "errors", "encode", "ChIP-seq"], "article_id"=>954637, "categories"=>["Biological Sciences", "Mathematics"], "users"=>["Tatsunori B. Hashimoto", "Matthew D. Edwards", "David K. Gifford"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003494.g002", "stats"=>{"downloads"=>3, "page_views"=>29, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Log_likelihood_errors_for_ENCODE_ChIP_seq_data_/954637", "title"=>"Log-likelihood errors for ENCODE ChIP-seq data.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-03-06 04:34:24"}
  • {"files"=>["https://ndownloader.figshare.com/files/1411026"], "description"=>"<p>Differences in log-likelihood per base between the fitted model and the empirical distribution, also interpreted as the log-difference between observed and fitted counts. This error metric represents the error when calculating p-values or significance tests using a Poisson assumption. Three assay types are shown in each panel, analyzed by three models: (a) Poisson. (b) Negative binomial. (c) Log-normal Poisson. A model that fits the data would have points along the line. Consistent deviation from zero by all distributions show that none of the distributions fit all assays well.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "statistics", "Statistical methods", "observed"], "article_id"=>954636, "categories"=>["Biological Sciences", "Mathematics"], "users"=>["Tatsunori B. Hashimoto", "Matthew D. Edwards", "David K. Gifford"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003494.g001", "stats"=>{"downloads"=>5, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Log_likelihood_difference_between_maximum_likelihood_and_observed_distributions_/954636", "title"=>"Log-likelihood difference between maximum likelihood and observed distributions.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-03-06 04:34:24"}
  • {"files"=>["https://ndownloader.figshare.com/files/1411034", "https://ndownloader.figshare.com/files/1411035", "https://ndownloader.figshare.com/files/1411036", "https://ndownloader.figshare.com/files/1411037", "https://ndownloader.figshare.com/files/1411038", "https://ndownloader.figshare.com/files/1411039", "https://ndownloader.figshare.com/files/1411040", "https://ndownloader.figshare.com/files/1411041"], "description"=>"<div><p>We show that existing RNA-seq, DNase-seq, and ChIP-seq data exhibit overdispersed per-base read count distributions that are not matched to existing computational method assumptions. To compensate for this overdispersion we introduce a nonparametric and universal method for processing per-base sequencing read count data called Fixseq. We demonstrate that Fixseq substantially improves the performance of existing RNA-seq, DNase-seq, and ChIP-seq analysis tools when compared with existing alternatives.</p></div>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "statistics", "Statistical methods", "correction", "high-throughput"], "article_id"=>954644, "categories"=>["Biological Sciences", "Mathematics"], "users"=>["Tatsunori B. Hashimoto", "Matthew D. Edwards", "David K. Gifford"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1003494.s001", "https://dx.doi.org/10.1371/journal.pcbi.1003494.s002", "https://dx.doi.org/10.1371/journal.pcbi.1003494.s003", "https://dx.doi.org/10.1371/journal.pcbi.1003494.s004", "https://dx.doi.org/10.1371/journal.pcbi.1003494.s005", "https://dx.doi.org/10.1371/journal.pcbi.1003494.s006", "https://dx.doi.org/10.1371/journal.pcbi.1003494.s007", "https://dx.doi.org/10.1371/journal.pcbi.1003494.s008"], "stats"=>{"downloads"=>23, "page_views"=>17, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Universal_Count_Correction_for_High_Throughput_Sequencing_/954644", "title"=>"Universal Count Correction for High-Throughput Sequencing", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2014-03-06 04:34:24"}
  • {"files"=>["https://ndownloader.figshare.com/files/1411031"], "description"=>"<p>Higher rank correlations across exon expression measurements between replicates indicate greater data quality and reproducibility. Fixseq increases the rank correlation coefficient for almost all experiments, though the improvements compared to de-duplication for the CSHL 75bp align and splice experiments are minimal.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "statistics", "Statistical methods", "differences", "coefficient", "encode", "rna-seq"], "article_id"=>954641, "categories"=>["Biological Sciences", "Mathematics"], "users"=>["Tatsunori B. Hashimoto", "Matthew D. Edwards", "David K. Gifford"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003494.g006", "stats"=>{"downloads"=>3, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Bootstrapped_differences_in_Spearman_s_rank_correlation_coefficient_for_ENCODE_RNA_seq_experiments_/954641", "title"=>"Bootstrapped differences in Spearman's rank correlation coefficient for ENCODE RNA-seq experiments.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-03-06 04:34:24"}
  • {"files"=>["https://ndownloader.figshare.com/files/1411029"], "description"=>"<p>Boxplots depicting AUC improvement across multiple factors (boxes above zero represent improvement due to Fixseq). Variance was estimated using one thousand bootstrap replicates each. Transcription factors with no significant difference across methods (p = 0.05) are not shown. Of the 301 factors tested, 90 could be predicted nontrivially () via CENTIPEDE. Of these, 51 show significant differences.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "statistics", "Statistical methods", "comparisons", "baseline", "methods", "compared"], "article_id"=>954639, "categories"=>["Biological Sciences", "Mathematics"], "users"=>["Tatsunori B. Hashimoto", "Matthew D. Edwards", "David K. Gifford"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003494.g004", "stats"=>{"downloads"=>7, "page_views"=>23, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_AUC_comparisons_for_baseline_methods_compared_to_F_ixseq_/954639", "title"=>"AUC comparisons for baseline methods compared to Fixseq.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-03-06 04:34:24"}
  • {"files"=>["https://ndownloader.figshare.com/files/1411030"], "description"=>"<p>Subfigure (a) shows that Fixseq increases the q-value correlation between replicates. Subfigure (b) shows the increase in the log number of bases overlapping between calls on replicates due to Fixseq. Fixseq calls have consistently higher peak overlaps between replicates for both MACS and PeakSeq.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "statistics", "Statistical methods", "reproducibility"], "article_id"=>954640, "categories"=>["Biological Sciences", "Mathematics"], "users"=>["Tatsunori B. Hashimoto", "Matthew D. Edwards", "David K. Gifford"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003494.g005", "stats"=>{"downloads"=>2, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Event_reproducibility_for_ChIP_replicates_/954640", "title"=>"Event reproducibility for ChIP replicates.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-03-06 04:34:24"}
  • {"files"=>["https://ndownloader.figshare.com/files/1411028"], "description"=>"<p>The left panel shows uncorrected counts, and the right shows counts after correction. Poisson distributed counts would follow a straight line; all experiments show significant deviation from linearity that is corrected by Fixseq.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "statistics", "Statistical methods", "counts", "14", "dnase-seq", "23", "rna-seq", "experiments", "87", "ChIP-seq"], "article_id"=>954638, "categories"=>["Biological Sciences", "Mathematics"], "users"=>["Tatsunori B. Hashimoto", "Matthew D. Edwards", "David K. Gifford"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003494.g003", "stats"=>{"downloads"=>1, "page_views"=>26, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Distribution_of_counts_for_14_DNase_seq_experiments_23_RNA_seq_experiments_and_87_ChIP_seq_experiments_/954638", "title"=>"Distribution of counts for 14 DNase-seq experiments, 23 RNA-seq experiments and 87 ChIP-seq experiments.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-03-06 04:34:24"}
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Relative Metric

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