Comparative Analyses between Retained Introns and Constitutively Spliced Introns in Arabidopsis thaliana Using Random Forest and Support Vector Machine
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{"title"=>"Comparative analyses between retained introns and constitutively spliced introns in Arabidopsis thaliana using random forest and support vector machine", "type"=>"journal", "authors"=>[{"first_name"=>"Rui", "last_name"=>"Mao", "scopus_author_id"=>"36167161700"}, {"first_name"=>"Praveen Kumar", "last_name"=>"Raj Kumar", "scopus_author_id"=>"56178202000"}, {"first_name"=>"Cheng", "last_name"=>"Guo", "scopus_author_id"=>"55995244600"}, {"first_name"=>"Yang", "last_name"=>"Zhang", "scopus_author_id"=>"55870054700"}, {"first_name"=>"Chun", "last_name"=>"Liang", "scopus_author_id"=>"55252645000"}], "year"=>2014, "source"=>"PLoS ONE", "identifiers"=>{"isbn"=>"1932-6203", "issn"=>"19326203", "pui"=>"373746918", "pmid"=>"25110928", "doi"=>"10.1371/journal.pone.0104049", "sgr"=>"84905828510", "scopus"=>"2-s2.0-84905828510"}, "id"=>"e8af7fed-ec7c-3064-86b0-793bdc838d97", "abstract"=>"One of the important modes of pre-mRNA post-transcriptional modification is alternative splicing. Alternative splicing allows creation of many distinct mature mRNA transcripts from a single gene by utilizing different splice sites. In plants like Arabidopsis thaliana, the most common type of alternative splicing is intron retention. Many studies in the past focus on positional distribution of retained introns (RIs) among different genic regions and their expression regulations, while little systematic classification of RIs from constitutively spliced introns (CSIs) has been conducted using machine learning approaches. We used random forest and support vector machine (SVM) with radial basis kernel function (RBF) to differentiate these two types of introns in Arabidopsis. By comparing coordinates of introns of all annotated mRNAs from TAIR10, we obtained our high-quality experimental data. To distinguish RIs from CSIs, We investigated the unique characteristics of RIs in comparison with CSIs and finally extracted 37 quantitative features: local and global nucleotide sequence features of introns, frequent motifs, the signal strength of splice sites, and the similarity between sequences of introns and their flanking regions. We demonstrated that our proposed feature extraction approach was more accurate in effectively classifying RIs from CSIs in comparison with other four approaches. The optimal penalty parameter C and the RBF kernel parameter [Formula: see text] in SVM were set based on particle swarm optimization algorithm (PSOSVM). Our classification performance showed F-Measure of 80.8% (random forest) and 77.4% (PSOSVM). Not only the basic sequence features and positional distribution characteristics of RIs were obtained, but also putative regulatory motifs in intron splicing were predicted based on our feature extraction approach. Clearly, our study will facilitate a better understanding of underlying mechanisms involved in intron retention.", "link"=>"http://www.mendeley.com/research/comparative-analyses-between-retained-introns-constitutively-spliced-introns-arabidopsis-thaliana-us", "reader_count"=>12, "reader_count_by_academic_status"=>{"Researcher"=>2, "Student > Ph. D. Student"=>6, "Student > Postgraduate"=>2, "Student > Master"=>1, "Student > Bachelor"=>1}, "reader_count_by_user_role"=>{"Researcher"=>2, "Student > Ph. D. Student"=>6, "Student > Postgraduate"=>2, "Student > Master"=>1, "Student > Bachelor"=>1}, "reader_count_by_subject_area"=>{"Engineering"=>1, "Biochemistry, Genetics and Molecular Biology"=>3, "Agricultural and Biological Sciences"=>8}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>8}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>3}}, "reader_count_by_country"=>{"Canada"=>1, "United States"=>1, "United Kingdom"=>1, "Spain"=>1}, "group_count"=>0}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1630391"], "description"=>"<p>Quantile represents <i>quantile()</i> function in R. For given probabilities [0.02, 0.2, 0.4, 0.6, 0.8, 0.98], <i>quantile()</i> returns estimates of corresponding distribution quantiles based on sort order.</p>", "links"=>[], "tags"=>["Biochemistry", "rna", "RNA processing", "alternative splicing", "Computational biology", "Comparative genomics", "genetics", "gene expression", "neuroscience", "cognitive science", "artificial intelligence", "Machine learning", "Machine learning algorithms", "qualtiles", "ris"], "article_id"=>1134861, "categories"=>["Biological Sciences"], "users"=>["Rui Mao", "Praveen Kumar Raj Kumar", "Cheng Guo", "Yang Zhang", "Chun Liang"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0104049.t003", "stats"=>{"downloads"=>4, "page_views"=>38, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Average_size_range_and_sample_qualtiles_of_RIs_and_CSIs_/1134861", "title"=>"Average size, range and sample qualtiles of RIs and CSIs.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-08-11 02:54:11"}
  • {"files"=>["https://ndownloader.figshare.com/files/1630411", "https://ndownloader.figshare.com/files/1630412", "https://ndownloader.figshare.com/files/1630413"], "description"=>"<div><p>One of the important modes of pre-mRNA post-transcriptional modification is alternative splicing. Alternative splicing allows creation of many distinct mature mRNA transcripts from a single gene by utilizing different splice sites. In plants like <i>Arabidopsis thaliana</i>, the most common type of alternative splicing is intron retention. Many studies in the past focus on positional distribution of retained introns (RIs) among different genic regions and their expression regulations, while little systematic classification of RIs from constitutively spliced introns (CSIs) has been conducted using machine learning approaches. We used random forest and support vector machine (SVM) with radial basis kernel function (RBF) to differentiate these two types of introns in <i>Arabidopsis</i>. By comparing coordinates of introns of all annotated mRNAs from TAIR10, we obtained our high-quality experimental data. To distinguish RIs from CSIs, We investigated the unique characteristics of RIs in comparison with CSIs and finally extracted 37 quantitative features: local and global nucleotide sequence features of introns, frequent motifs, the signal strength of splice sites, and the similarity between sequences of introns and their flanking regions. We demonstrated that our proposed feature extraction approach was more accurate in effectively classifying RIs from CSIs in comparison with other four approaches. The optimal penalty parameter C and the RBF kernel parameter in SVM were set based on particle swarm optimization algorithm (PSOSVM). Our classification performance showed F-Measure of 80.8% (random forest) and 77.4% (PSOSVM). Not only the basic sequence features and positional distribution characteristics of RIs were obtained, but also putative regulatory motifs in intron splicing were predicted based on our feature extraction approach. Clearly, our study will facilitate a better understanding of underlying mechanisms involved in intron retention.</p></div>", "links"=>[], "tags"=>["Biochemistry", "rna", "RNA processing", "alternative splicing", "Computational biology", "Comparative genomics", "genetics", "gene expression", "neuroscience", "cognitive science", "artificial intelligence", "Machine learning", "Machine learning algorithms", "comparative", "retained", "introns", "constitutively", "spliced", "vector"], "article_id"=>1134875, "categories"=>["Biological Sciences"], "users"=>["Rui Mao", "Praveen Kumar Raj Kumar", "Cheng Guo", "Yang Zhang", "Chun Liang"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0104049.s001", "https://dx.doi.org/10.1371/journal.pone.0104049.s002", "https://dx.doi.org/10.1371/journal.pone.0104049.s003"], "stats"=>{"downloads"=>21, "page_views"=>23, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Comparative_Analyses_between_Retained_Introns_and_Constitutively_Spliced_Introns_in_Arabidopsis_thaliana_Using_Random_Forest_and_Support_Vector_Machine/1134875", "title"=>"Comparative Analyses between Retained Introns and Constitutively Spliced Introns in <i>Arabidopsis thaliana</i> Using Random Forest and Support Vector Machine", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2014-08-11 02:54:11"}
  • {"files"=>["https://ndownloader.figshare.com/files/1630390"], "description"=>"<p>All RNAs means the 8 types of RNAs described in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0104049#pone-0104049-g003\" target=\"_blank\">Figure 3</a>. Redundant cases could only happen in RIs, the detailed description sees <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0104049#s2\" target=\"_blank\">Materials and Methods</a>.</p>", "links"=>[], "tags"=>["Biochemistry", "rna", "RNA processing", "alternative splicing", "Computational biology", "Comparative genomics", "genetics", "gene expression", "neuroscience", "cognitive science", "artificial intelligence", "Machine learning", "Machine learning algorithms", "ris", "csis"], "article_id"=>1134860, "categories"=>["Biological Sciences"], "users"=>["Rui Mao", "Praveen Kumar Raj Kumar", "Cheng Guo", "Yang Zhang", "Chun Liang"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0104049.t002", "stats"=>{"downloads"=>10, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Distribution_of_RIs_and_CSIs_in_Arabidopsis_/1134860", "title"=>"Distribution of RIs and CSIs in <i>Arabidopsis</i>.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-08-11 02:54:11"}
  • {"files"=>["https://ndownloader.figshare.com/files/1630387"], "description"=>"<p>P value was calculated by applying F-test in one-way ANOVA based on experiment dataset included RIs and CSIs. The influences of classification among four features are all significant (p<0.0001).</p>", "links"=>[], "tags"=>["Biochemistry", "rna", "RNA processing", "alternative splicing", "Computational biology", "Comparative genomics", "genetics", "gene expression", "neuroscience", "cognitive science", "artificial intelligence", "Machine learning", "Machine learning algorithms", "iddonv"], "article_id"=>1134857, "categories"=>["Biological Sciences"], "users"=>["Rui Mao", "Praveen Kumar Raj Kumar", "Cheng Guo", "Yang Zhang", "Chun Liang"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0104049.t006", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_mean_value_and_P_value_of_SFvalue_SFaccvalue_IDdonv_and_IDacceptv_/1134857", "title"=>"The mean value and P value of SFvalue, SFaccvalue, IDdonv and IDacceptv.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-08-11 02:54:11"}
  • {"files"=>["https://ndownloader.figshare.com/files/1630385"], "description"=>"<p>Classification accuracy is assessed with F-Measure. Each solid round dot represents the accuracy of random forest and each triangle means the accuracy of PSOSVM for a given feature set. Compared with the other feature sets, our combined <b>A+B+C</b> feature set obtains the optimal classification performance by using both classifiers.</p>", "links"=>[], "tags"=>["Biochemistry", "rna", "RNA processing", "alternative splicing", "Computational biology", "Comparative genomics", "genetics", "gene expression", "neuroscience", "cognitive science", "artificial intelligence", "Machine learning", "Machine learning algorithms", "psosvm"], "article_id"=>1134855, "categories"=>["Biological Sciences"], "users"=>["Rui Mao", "Praveen Kumar Raj Kumar", "Cheng Guo", "Yang Zhang", "Chun Liang"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0104049.g005", "stats"=>{"downloads"=>0, "page_views"=>19, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Performance_of_random_forest_and_PSOSVM_F_Measure_in_five_different_feature_sets_/1134855", "title"=>"Performance of random forest and PSOSVM (F-Measure) in five different feature sets.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-08-11 02:54:11"}
  • {"files"=>["https://ndownloader.figshare.com/files/1630386"], "description"=>"<p>In the left side of the histogram there are ten frequent motifs that have higher occurrences in RIs than in CSIs. In the right site of the histogram there are nine frequent motifs that have higher occurrences in CSIs than in RIs.</p>", "links"=>[], "tags"=>["Biochemistry", "rna", "RNA processing", "alternative splicing", "Computational biology", "Comparative genomics", "genetics", "gene expression", "neuroscience", "cognitive science", "artificial intelligence", "Machine learning", "Machine learning algorithms", "occurrences", "motifs", "ris"], "article_id"=>1134856, "categories"=>["Biological Sciences"], "users"=>["Rui Mao", "Praveen Kumar Raj Kumar", "Cheng Guo", "Yang Zhang", "Chun Liang"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0104049.g006", "stats"=>{"downloads"=>0, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_mean_occurrences_of_B_frequent_motifs_between_RIs_and_CSIs_/1134856", "title"=>"The mean occurrences of B frequent motifs between RIs and CSIs.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-08-11 02:54:11"}
  • {"files"=>["https://ndownloader.figshare.com/files/1630384"], "description"=>"<p>The ROC curve of random forest is shown by the solid line and PSOSVM by the dashed line. The classification accuracy of these two methods is measured by AUC (the area under the ROC curve). Random forest gains significant advantages compared to PSOSVM (i.e., 0.900 versus 0.844).</p>", "links"=>[], "tags"=>["Biochemistry", "rna", "RNA processing", "alternative splicing", "Computational biology", "Comparative genomics", "genetics", "gene expression", "neuroscience", "cognitive science", "artificial intelligence", "Machine learning", "Machine learning algorithms", "roc", "curves"], "article_id"=>1134854, "categories"=>["Biological Sciences"], "users"=>["Rui Mao", "Praveen Kumar Raj Kumar", "Cheng Guo", "Yang Zhang", "Chun Liang"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0104049.g004", "stats"=>{"downloads"=>1, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_ROC_curves_of_random_forest_versus_PSOSVM_/1134854", "title"=>"The ROC curves of random forest versus PSOSVM.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-08-11 02:54:11"}
  • {"files"=>["https://ndownloader.figshare.com/files/1630381"], "description"=>"<p>A. The sequence extraction approach for calculating signal strength of splice sites; B. The sequence extraction approach for calculating increment of diversity (ID).</p>", "links"=>[], "tags"=>["Biochemistry", "rna", "RNA processing", "alternative splicing", "Computational biology", "Comparative genomics", "genetics", "gene expression", "neuroscience", "cognitive science", "artificial intelligence", "Machine learning", "Machine learning algorithms", "extraction", "approaches", "splice", "sites", "intron", "flanking"], "article_id"=>1134851, "categories"=>["Biological Sciences"], "users"=>["Rui Mao", "Praveen Kumar Raj Kumar", "Cheng Guo", "Yang Zhang", "Chun Liang"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0104049.g001", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Feature_extraction_approaches_for_calculating_signal_strength_of_splice_sites_and_similarity_of_intron_and_the_flanking_exons_/1134851", "title"=>"Feature extraction approaches for calculating signal strength of splice sites and similarity of intron and the flanking exons.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-08-11 02:54:11"}
  • {"files"=>["https://ndownloader.figshare.com/files/1630392"], "description"=>"<p>The rule-of-thumb settings of , and are cited from <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0104049#pone.0104049-Shi1\" target=\"_blank\">[74]</a>.</p>", "links"=>[], "tags"=>["Biochemistry", "rna", "RNA processing", "alternative splicing", "Computational biology", "Comparative genomics", "genetics", "gene expression", "neuroscience", "cognitive science", "artificial intelligence", "Machine learning", "Machine learning algorithms", "parameter", "ranges"], "article_id"=>1134862, "categories"=>["Biological Sciences"], "users"=>["Rui Mao", "Praveen Kumar Raj Kumar", "Cheng Guo", "Yang Zhang", "Chun Liang"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0104049.t001", "stats"=>{"downloads"=>3, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_parameter_values_or_ranges_of_PSOSVM_/1134862", "title"=>"The parameter values or ranges of PSOSVM.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-08-11 02:54:11"}
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  • {"files"=>["https://ndownloader.figshare.com/files/1630388"], "description"=>"<p>Feature vectors of experimental dataset.</p>", "links"=>[], "tags"=>["Biochemistry", "rna", "RNA processing", "alternative splicing", "Computational biology", "Comparative genomics", "genetics", "gene expression", "neuroscience", "cognitive science", "artificial intelligence", "Machine learning", "Machine learning algorithms", "vectors"], "article_id"=>1134858, "categories"=>["Biological Sciences"], "users"=>["Rui Mao", "Praveen Kumar Raj Kumar", "Cheng Guo", "Yang Zhang", "Chun Liang"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0104049.t004", "stats"=>{"downloads"=>3, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Feature_vectors_of_experimental_dataset_/1134858", "title"=>"Feature vectors of experimental dataset.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-08-11 02:54:11"}
  • {"files"=>["https://ndownloader.figshare.com/files/1630383"], "description"=>"<p>Each horizontal bar (with the number) indicates the number for a given RNA type.</p>", "links"=>[], "tags"=>["Biochemistry", "rna", "RNA processing", "alternative splicing", "Computational biology", "Comparative genomics", "genetics", "gene expression", "neuroscience", "cognitive science", "artificial intelligence", "Machine learning", "Machine learning algorithms", "types", "annotated", "tair10", "annotation"], "article_id"=>1134853, "categories"=>["Biological Sciences"], "users"=>["Rui Mao", "Praveen Kumar Raj Kumar", "Cheng Guo", "Yang Zhang", "Chun Liang"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0104049.g003", "stats"=>{"downloads"=>0, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Numbers_of_various_RNA_types_annotated_in_TAIR10_gene_annotation_for_Arabidopsis_/1134853", "title"=>"Numbers of various RNA types annotated in TAIR10 gene annotation for <i>Arabidopsis</i>.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-08-11 02:54:11"}
  • {"files"=>["https://ndownloader.figshare.com/files/1630382"], "description"=>"<p>The details of <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0104049#pone.0104049.e134\" target=\"_blank\">Eq. 16</a> are illustrated in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0104049#s2\" target=\"_blank\">Materials and Methods</a>.</p>", "links"=>[], "tags"=>["Biochemistry", "rna", "RNA processing", "alternative splicing", "Computational biology", "Comparative genomics", "genetics", "gene expression", "neuroscience", "cognitive science", "artificial intelligence", "Machine learning", "Machine learning algorithms", "pseudo-code"], "article_id"=>1134852, "categories"=>["Biological Sciences"], "users"=>["Rui Mao", "Praveen Kumar Raj Kumar", "Cheng Guo", "Yang Zhang", "Chun Liang"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0104049.g002", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_pseudo_code_of_PSOSVM_/1134852", "title"=>"The pseudo-code of PSOSVM.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-08-11 02:54:11"}

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Relative Metric

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