ExprTarget: An Integrative Approach to Predicting Human MicroRNA Targets
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{"title"=>"ExprTarget: An Integrative Approach to Predicting Human MicroRNA Targets", "type"=>"journal", "authors"=>[{"first_name"=>"Eric R.", "last_name"=>"Gamazon"}, {"first_name"=>"Hae-Kyung", "last_name"=>"Im"}, {"first_name"=>"Shiwei", "last_name"=>"Duan"}, {"first_name"=>"Yves A.", "last_name"=>"Lussier"}, {"first_name"=>"Nancy J.", "last_name"=>"Cox"}, {"first_name"=>"M. Eileen", "last_name"=>"Dolan"}, {"first_name"=>"Wei", "last_name"=>"Zhang"}], "year"=>2010, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"1932-6203", "isbn"=>"1932-6203", "doi"=>"10.1371/journal.pone.0013534", "pmid"=>"20975837"}, "id"=>"487cc677-66a3-3de6-b6e6-e8c66efc8703", "abstract"=>"Variation in gene expression has been observed in natural populations and associated with complex traits or phenotypes such as disease susceptibility and drug response. Gene expression itself is controlled by various genetic and non-genetic factors. The binding of a class of small RNA molecules, microRNAs (miRNAs), to mRNA transcript targets has recently been demonstrated to be an important mechanism of gene regulation. Because individual miRNAs may regulate the expression of multiple gene targets, a comprehensive and reliable catalogue of miRNA-regulated targets is critical to understanding gene regulatory networks. Though experimental approaches have been used to identify many miRNA targets, due to cost and efficiency, current miRNA target identification still relies largely on computational algorithms that aim to take advantage of different biochemical/thermodynamic properties of the sequences of miRNAs and their gene targets. A novel approach, ExprTarget, therefore, is proposed here to integrate some of the most frequently invoked methods (miRanda, PicTar, TargetScan) as well as the genome-wide HapMap miRNA and mRNA expression datasets generated in our laboratory. To our knowledge, this dataset constitutes the first miRNA expression profiling in the HapMap lymphoblastoid cell lines. We conducted diagnostic tests of the existing computational solutions using the experimentally supported targets in TarBase as gold standard. To gain insight into the biases that arise from such an analysis, we investigated the effect of the choice of gold standard on the evaluation of the various computational tools. We analyzed the performance of ExprTarget using both ROC curve analysis and cross-validation. We show that ExprTarget greatly improves miRNA target prediction relative to the individual prediction algorithms in terms of sensitivity and specificity. We also developed an online database, ExprTargetDB, of human miRNA targets predicted by our approach that integrates gene expression profiling into a broader framework involving important features of miRNA target site predictions.", "link"=>"http://www.mendeley.com/research/exprtarget-integrative-approach-predicting-human-microrna-targets", "reader_count"=>70, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>8, "Researcher"=>19, "Student > Doctoral Student"=>4, "Student > Ph. D. Student"=>24, "Student > Postgraduate"=>3, "Other"=>4, "Student > Bachelor"=>2, "Professor"=>4}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>8, "Researcher"=>19, "Student > Doctoral Student"=>4, "Student > Ph. D. Student"=>24, "Student > Postgraduate"=>3, "Other"=>4, "Student > Bachelor"=>2, "Professor"=>4}, "reader_count_by_subject_area"=>{"Unspecified"=>3, "Environmental Science"=>1, "Biochemistry, Genetics and Molecular Biology"=>4, "Medicine and Dentistry"=>5, "Agricultural and Biological Sciences"=>48, "Chemistry"=>2, "Computer Science"=>7}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>5}, "Chemistry"=>{"Chemistry"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>48}, "Computer Science"=>{"Computer Science"=>7}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>4}, "Unspecified"=>{"Unspecified"=>3}, "Environmental Science"=>{"Environmental Science"=>1}}, "reader_count_by_country"=>{"Canada"=>1, "Hungary"=>1, "United States"=>6, "Brazil"=>1, "Mexico"=>1, "Spain"=>2, "India"=>1}, "group_count"=>10}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/823032"], "description"=>"<p>Prediction scores (TargetScan, PicTar, and miRanda) for the same miRNA targets are plotted to show pair-wise comparisons (A, B, C). The distributions of scores for targets from the TarBase (experimentally-validated) are shown with the scores for the full set of targets from individual prediction algorithms (D, E, F). (A) miRanda (x-axis) scores are plotted against PicTar scores (y-axis); (B) miRanda (x-axis) scores are plotted against TargetScan scores (y-axis); (C) PicTar scores (x-axis) are plotted against TargetScan scores (y-axis); (D) Histogram of experimentally-validated targets with the distribution of miRanda scores (left y-axis is for the miRanda p values; right y-axis is for the TarBase targets); (E) Histogram of experimentally-validated targets with the distribution of PicTar scores (left y-axis is for the PicTar scores; right y-axis is for the TarBase targets); and (F) Histogram of experimentally-validated targets with the distribution of TargetScan scores (left y-axis is for the TargetScan scores; right y-axis is for the TarBase targets).</p>", "links"=>[], "tags"=>["computational", "approaches"], "article_id"=>493398, "categories"=>["Genetics", "Biological Sciences"], "users"=>["Eric R. Gamazon", "Hae-Kyung Im", "Shiwei Duan", "Yves A. Lussier", "Nancy J. Cox", "M. Eileen Dolan", "Wei Zhang"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0013534.g002", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Prediction_results_from_existing_computational_approaches_are_not_correlated_/493398", "title"=>"Prediction results from existing computational approaches are not correlated.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-10-21 00:56:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/823189"], "description"=>"<p>ExprTarget was assessed by plotting the ROC curve, which shows the true positive rate (sensitivity) and the false positive rate (1-specificity) at various thresholds. The database of manually-curated experimentally verified targets, TarBase, was used as gold standard. The line of no-discrimination was drawn from the left bottom to the top right corners.</p>", "links"=>[], "tags"=>["integrative"], "article_id"=>493556, "categories"=>["Genetics", "Biological Sciences"], "users"=>["Eric R. Gamazon", "Hae-Kyung Im", "Shiwei Duan", "Yves A. Lussier", "Nancy J. Cox", "M. Eileen Dolan", "Wei Zhang"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0013534.g004", "stats"=>{"downloads"=>0, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Performance_of_ExprTarget_an_integrative_prediction_algorithm_/493556", "title"=>"Performance of ExprTarget, an integrative prediction algorithm.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-10-21 00:59:16"}
  • {"files"=>["https://ndownloader.figshare.com/files/822956"], "description"=>"<p>Individual computational methods (miRanda, PicTar, TargetScan) were evaluated using both TarBase and (TarBase + LCL) as gold standards. ExprTarget integrates individual computational methods (miRanda, PicTar, TargetScan) and the LCL expression data. ExprTarget was evaluated using TarBase as gold standard. ExprTargetDB was developed to house the predictions by ExprTarget. LCL refers to the miRNA and mRNA expression data generated on a panel of lymphoblastoid cell lines from the HapMap.</p>", "links"=>[], "tags"=>["integrates", "methods"], "article_id"=>493322, "categories"=>["Genetics", "Biological Sciences"], "users"=>["Eric R. Gamazon", "Hae-Kyung Im", "Shiwei Duan", "Yves A. Lussier", "Nancy J. Cox", "M. Eileen Dolan", "Wei Zhang"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0013534.g001", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_ExprTarget_integrates_various_methods_and_datasets_/493322", "title"=>"ExprTarget integrates various methods and datasets.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-10-21 00:55:22"}
  • {"files"=>["https://ndownloader.figshare.com/files/410208", "https://ndownloader.figshare.com/files/410226", "https://ndownloader.figshare.com/files/410239"], "description"=>"<div><p>Variation in gene expression has been observed in natural populations and associated with complex traits or phenotypes such as disease susceptibility and drug response. Gene expression itself is controlled by various genetic and non-genetic factors. The binding of a class of small RNA molecules, microRNAs (miRNAs), to mRNA transcript targets has recently been demonstrated to be an important mechanism of gene regulation. Because individual miRNAs may regulate the expression of multiple gene targets, a comprehensive and reliable catalogue of miRNA-regulated targets is critical to understanding gene regulatory networks. Though experimental approaches have been used to identify many miRNA targets, due to cost and efficiency, current miRNA target identification still relies largely on computational algorithms that aim to take advantage of different biochemical/thermodynamic properties of the sequences of miRNAs and their gene targets. A novel approach, ExprTarget, therefore, is proposed here to integrate some of the most frequently invoked methods (miRanda, PicTar, TargetScan) as well as the genome-wide HapMap miRNA and mRNA expression datasets generated in our laboratory. To our knowledge, this dataset constitutes the first miRNA expression profiling in the HapMap lymphoblastoid cell lines. We conducted diagnostic tests of the existing computational solutions using the experimentally supported targets in TarBase as gold standard. To gain insight into the biases that arise from such an analysis, we investigated the effect of the choice of gold standard on the evaluation of the various computational tools. We analyzed the performance of ExprTarget using both ROC curve analysis and cross-validation. We show that ExprTarget greatly improves miRNA target prediction relative to the individual prediction algorithms in terms of sensitivity and specificity. We also developed an online database, ExprTargetDB, of human miRNA targets predicted by our approach that integrates gene expression profiling into a broader framework involving important features of miRNA target site predictions.</p></div>", "links"=>[], "tags"=>["integrative", "predicting", "microrna", "targets"], "article_id"=>141026, "categories"=>["Genetics", "Biological Sciences"], "users"=>["Eric R. Gamazon", "Hae-Kyung Im", "Shiwei Duan", "Yves A. Lussier", "Nancy J. Cox", "M. Eileen Dolan", "Wei Zhang"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0013534.s001", "https://dx.doi.org/10.1371/journal.pone.0013534.s002", "https://dx.doi.org/10.1371/journal.pone.0013534.s003"], "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/ExprTarget_An_Integrative_Approach_to_Predicting_Human_MicroRNA_Targets/141026", "title"=>"ExprTarget: An Integrative Approach to Predicting Human MicroRNA Targets", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2010-10-21 00:17:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/823106"], "description"=>"<p>The three prediction algorithms were evaluated using ROC curves, which plot the true positive rate (sensitivity) and the false positive rate (1-specificity) at various score thresholds. TarBase was used as gold standard. The line of no-discrimination was drawn from the left bottom to the top right corners. (A) miRanda vs. TarBase; (B) PicTar vs. TarBase; and (C) TargetScan vs. TarBase.</p>", "links"=>[], "tags"=>["foundational"], "article_id"=>493473, "categories"=>["Genetics", "Biological Sciences"], "users"=>["Eric R. Gamazon", "Hae-Kyung Im", "Shiwei Duan", "Yves A. Lussier", "Nancy J. Cox", "M. Eileen Dolan", "Wei Zhang"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0013534.g003", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Individual_performance_of_foundational_prediction_algorithms_/493473", "title"=>"Individual performance of foundational prediction algorithms.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-10-21 00:57:53"}

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Relative Metric

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