GENE-Counter: A Computational Pipeline for the Analysis of RNA-Seq Data for Gene Expression Differences
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{"title"=>"GENE-counter: A computational pipeline for the analysis of RNA-seq data for gene expression differences", "type"=>"journal", "authors"=>[{"first_name"=>"Jason S.", "last_name"=>"Cumbie", "scopus_author_id"=>"16041293600"}, {"first_name"=>"Jeffrey A.", "last_name"=>"Kimbrel", "scopus_author_id"=>"35198291500"}, {"first_name"=>"Yanming", "last_name"=>"Di", "scopus_author_id"=>"39361439100"}, {"first_name"=>"Daniel W.", "last_name"=>"Schafer", "scopus_author_id"=>"35874229300"}, {"first_name"=>"Larry J.", "last_name"=>"Wilhelm", "scopus_author_id"=>"20735983500"}, {"first_name"=>"Samuel E.", "last_name"=>"Fox", "scopus_author_id"=>"35299087900"}, {"first_name"=>"Christopher M.", "last_name"=>"Sullivan", "scopus_author_id"=>"16043948700"}, {"first_name"=>"Aron D.", "last_name"=>"Curzon", "scopus_author_id"=>"53881289600"}, {"first_name"=>"James C.", "last_name"=>"Carrington", "scopus_author_id"=>"7005873606"}, {"first_name"=>"Todd C.", "last_name"=>"Mockler", "scopus_author_id"=>"6602442579"}, {"first_name"=>"Jeff H.", "last_name"=>"Chang", "scopus_author_id"=>"7601552801"}], "year"=>2011, "source"=>"PLoS ONE", "identifiers"=>{"sgr"=>"80053610809", "doi"=>"10.1371/journal.pone.0025279", "issn"=>"19326203", "pui"=>"362707320", "isbn"=>"10.1371/journal.pone.0025279", "pmid"=>"21998647", "scopus"=>"2-s2.0-80053610809"}, "id"=>"d6a0b6a9-7db8-3f4e-86b7-b9033bfabaff", "abstract"=>"GENE-counter is a complete Perl-based computational pipeline for analyzing RNA-Sequencing (RNA-Seq) data for differential gene expression. In addition to its use in studying transcriptomes of eukaryotic model organisms, GENE-counter is applicable for prokaryotes and non-model organisms without an available genome reference sequence. For alignments, GENE-counter is configured for CASHX, Bowtie, and BWA, but an end user can use any Sequence Alignment/Map (SAM)-compliant program of preference. To analyze data for differential gene expression, GENE-counter can be run with any one of three statistics packages that are based on variations of the negative binomial distribution. The default method is a new and simple statistical test we developed based on an over-parameterized version of the negative binomial distribution. GENE-counter also includes three different methods for assessing differentially expressed features for enriched gene ontology (GO) terms. Results are transparent and data are systematically stored in a MySQL relational database to facilitate additional analyses as well as quality assessment. We used next generation sequencing to generate a small-scale RNA-Seq dataset derived from the heavily studied defense response of Arabidopsis thaliana and used GENE-counter to process the data. Collectively, the support from analysis of microarrays as well as the observed and substantial overlap in results from each of the three statistics packages demonstrates that GENE-counter is well suited for handling the unique characteristics of small sample sizes and high variability in gene counts.", "link"=>"http://www.mendeley.com/research/genecounter-computational-pipeline-analysis-rnaseq-data-gene-expression-differences", "reader_count"=>222, "reader_count_by_academic_status"=>{"Unspecified"=>3, "Professor > Associate Professor"=>11, "Librarian"=>1, "Researcher"=>80, "Student > Doctoral Student"=>6, "Student > Ph. D. 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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/727443"], "description"=>"<p>(<b>A</b>) Comparison of estimated log<sub>2</sub>-fold changes from analysis of microarrays (x-axis) and RNA-Seq using GENE-counter running NBPSeq (y-axis). Only induced genes measurable by both platforms are presented. Differentially induced genes are colored to highlight genes uniquely identified using microarrays (open red down triangles) or RNA-Seq (open blue up triangles) and found common between the two methods (purple crosses). (<b>B</b>) Three-way Venn comparing differentially expressed genes identified from GENE-counter's assessment of RNA-Seq data and analysis of microarrays. Only genes measurable using both methods were included in the comparison.</p>", "links"=>[], "tags"=>["rna-seq"], "article_id"=>397792, "categories"=>["Biochemistry", "Plant Biology", "Information And Computing Sciences", "Biological Sciences", "Genetics", "Biophysics"], "users"=>["Jason S. Cumbie", "Jeffrey A. Kimbrel", "Yanming Di", "Daniel W. Schafer", "Larry J. Wilhelm", "Samuel E. Fox", "Christopher M. Sullivan", "Aron D. Curzon", "James C. Carrington", "Todd C. Mockler", "Jeff H. Chang"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0025279.g004", "stats"=>{"downloads"=>2, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_analysis_of_RNA_Seq_with_analysis_of_microarrays_/397792", "title"=>"Comparison of analysis of RNA-Seq with analysis of microarrays.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-10-06 02:09:52"}
  • {"files"=>["https://ndownloader.figshare.com/files/727385"], "description"=>"<p>The percent of iterations a gene from the original set was identified as differentially induced (y-axis; n = 1000) was plotted as a function of q-value (x-axis; q-values determined for the original set of differentially induced genes categorized in quantile increments of 0.005 from least significant (q-value = 0.05) on the left to most significant (q-value = 0) on the right). For each iteration, different random number seeds were used to randomly thin gene counts. The percentage of genes found in ≥90% of the samples is indicated.</p>", "links"=>[], "tags"=>["nbpseq", "normalization", "differential"], "article_id"=>397730, "categories"=>["Biochemistry", "Plant Biology", "Information And Computing Sciences", "Biological Sciences", "Genetics", "Biophysics"], "users"=>["Jason S. Cumbie", "Jeffrey A. Kimbrel", "Yanming Di", "Daniel W. Schafer", "Larry J. Wilhelm", "Samuel E. Fox", "Christopher M. Sullivan", "Aron D. Curzon", "James C. Carrington", "Todd C. Mockler", "Jeff H. Chang"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0025279.g003", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Analysis_of_NBPSeq_normalization_on_differential_expression_/397730", "title"=>"Analysis of NBPSeq normalization on differential expression.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-10-06 02:08:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/367613", "https://ndownloader.figshare.com/files/367678", "https://ndownloader.figshare.com/files/367700", "https://ndownloader.figshare.com/files/367714", "https://ndownloader.figshare.com/files/367748"], "description"=>"<div><p>GENE-counter is a complete Perl-based computational pipeline for analyzing RNA-Sequencing (RNA-Seq) data for differential gene expression. In addition to its use in studying transcriptomes of eukaryotic model organisms, GENE-counter is applicable for prokaryotes and non-model organisms without an available genome reference sequence. For alignments, GENE-counter is configured for CASHX, Bowtie, and BWA, but an end user can use any Sequence Alignment/Map (SAM)-compliant program of preference. To analyze data for differential gene expression, GENE-counter can be run with any one of three statistics packages that are based on variations of the negative binomial distribution. The default method is a new and simple statistical test we developed based on an over-parameterized version of the negative binomial distribution. GENE-counter also includes three different methods for assessing differentially expressed features for enriched gene ontology (GO) terms. Results are transparent and data are systematically stored in a MySQL relational database to facilitate additional analyses as well as quality assessment. We used next generation sequencing to generate a small-scale RNA-Seq dataset derived from the heavily studied defense response of <em>Arabidopsis thaliana</em> and used GENE-counter to process the data. Collectively, the support from analysis of microarrays as well as the observed and substantial overlap in results from each of the three statistics packages demonstrates that GENE-counter is well suited for handling the unique characteristics of small sample sizes and high variability in gene counts.</p> </div>", "links"=>[], "tags"=>["computational", "pipeline", "rna-seq", "differences"], "article_id"=>132624, "categories"=>["Cell Biology", "Biochemistry", "Biophysics", "Information And Computing Sciences", "Biological Sciences", "Genetics"], "users"=>["Jason S. Cumbie", "Jeffrey A. Kimbrel", "Yanming Di", "Daniel W. Schafer", "Larry J. Wilhelm", "Samuel E. Fox", "Christopher M. Sullivan", "Aron D. Curzon", "James C. Carrington", "Todd C. Mockler", "Jeff H. Chang"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0025279.s001", "https://dx.doi.org/10.1371/journal.pone.0025279.s002", "https://dx.doi.org/10.1371/journal.pone.0025279.s003", "https://dx.doi.org/10.1371/journal.pone.0025279.s004", "https://dx.doi.org/10.1371/journal.pone.0025279.s005"], "stats"=>{"downloads"=>31, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/GENE_Counter_A_Computational_Pipeline_for_the_Analysis_of_RNA_Seq_Data_for_Gene_Expression_Differences/132624", "title"=>"GENE-Counter: A Computational Pipeline for the Analysis of RNA-Seq Data for Gene Expression Differences", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2011-10-06 00:43:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/727226"], "description"=>"<p>(<b>A</b>) The differentially expressed genes identified between Δ<i>hrcC</i>- and mock-treated Arabidopsis. Results are plotted using an MA-based method. Differentially expressed genes were identified using GENE-counter with the NBPSeq statistics package. Induced and repressed genes are highlighted in red and green, respectively (FDR≤5%). (<b>B</b>) Area-proportional Venn diagram comparing the differentially induced genes identified using GENE-counter running NBPSeq, the trend version of edgeR, or DESeq. Read counts were normalized using the methods provided in each statistical package prior to analysis (FDR≤5%). (<b>C</b>) Distribution of gene expression levels. Percentages of total genes (y-axis) were categorized per expression quantile, increasing from left to right (x-axis; natural log transformation of average number of normalized aligned reads per gene): gray; all genes; blue, red, and green; differentially induced as identified using GENE-counter running edgeR, DESeq, or NBPSeq, respectively. (<b>D</b>) Pair-wise comparisons of p-value rankings for genes identified as significant. Genes were color-coded gray if identified by both statistical packages, blue, red, or green, if uniquely identified by GENE-counter running NBPSeq, edgeR, or DESeq, respectively. Regression lines are plotted based on all genes (black) or only those common to both statistical packages (red). Pearson's r values are shown and colored accordingly.</p>", "links"=>[], "tags"=>["rna-seq", "genes", "differentially", "arabidopsis", "infected", "mock", "inoculation"], "article_id"=>397561, "categories"=>["Biochemistry", "Plant Biology", "Information And Computing Sciences", "Biological Sciences", "Genetics", "Biophysics"], "users"=>["Jason S. Cumbie", "Jeffrey A. Kimbrel", "Yanming Di", "Daniel W. Schafer", "Larry J. Wilhelm", "Samuel E. Fox", "Christopher M. Sullivan", "Aron D. Curzon", "James C. Carrington", "Todd C. Mockler", "Jeff H. Chang"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0025279.g002", "stats"=>{"downloads"=>2, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Analysis_of_RNA_Seq_data_for_genes_differentially_expressed_in_Arabidopsis_infected_with_hrcC_relative_to_mock_inoculation_7_hpi_/397561", "title"=>"Analysis of RNA-Seq data for genes differentially expressed in Arabidopsis infected with Δ<i>hrcC</i> relative to mock inoculation 7 hpi.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-10-06 02:06:01"}
  • {"files"=>["https://ndownloader.figshare.com/files/727537"], "description"=>"<p>*CASHX ver. 1.3 does not allow for mismatches and was not benchmarked for all tests <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0025279#pone.0025279-Langmead2\" target=\"_blank\">[21]</a>, <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0025279#pone.0025279-Fahlgren1\" target=\"_blank\">[28]</a>, <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0025279#pone.0025279-Li3\" target=\"_blank\">[29]</a>.</p>§<p>We derived a simulated RNA-Seq dataset from 8,815,743 regions of the Arabidopsis genome that were unique in sequence and lacked any Ns for use in benchmarking CASHX ver. 2.3.</p>†<p>For no mismatches, values are based on expected unique alignments; for two mismatches, values are based on the number of alignments confirmed by at least two software programs.</p>‡<p>Number of alignments that were not confirmed by at least one of the other tested software programs.</p>", "links"=>[], "tags"=>["cashx"], "article_id"=>397880, "categories"=>["Biochemistry", "Plant Biology", "Information And Computing Sciences", "Biological Sciences", "Genetics", "Biophysics"], "users"=>["Jason S. Cumbie", "Jeffrey A. Kimbrel", "Yanming Di", "Daniel W. Schafer", "Larry J. Wilhelm", "Samuel E. Fox", "Christopher M. Sullivan", "Aron D. Curzon", "James C. Carrington", "Todd C. Mockler", "Jeff H. Chang"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0025279.t001", "stats"=>{"downloads"=>4, "page_views"=>18, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Benchmarking_CASHX_ver_2_3_/397880", "title"=>"Benchmarking CASHX ver. 2.3.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-10-06 02:11:20"}
  • {"files"=>["https://ndownloader.figshare.com/files/727115"], "description"=>"<p>Each tool is numbered indicating the order in which data is typically processed: <b>1a and 1b</b>) the two modules of the processing tool, <b>2</b>) the assessment tool, <b>3</b>) the statistics tool, and <b>4</b>) the GORich tool. The processing tool uses a directory of FASTA files for each replicate as an input (RNA-Seq reads) to tabulate a list of unique read sequences and enumerate the occurrence of each read sequence within each FASTA file. Data are stored in a read database. The processing tool uses a SAM compliant alignment program to align and assign read sequences to features stored in a user-developed reference sequence database. Alignment information and associated count data are stored in the alignment database. Results can be analyzed by the assessment tool to produce an alignment summary, which includes a summary report of replicates and intraclass correlation coefficient (ICC) values. For statistical analysis, the statistics tool can use the NBPSeq, trend version of edgeR, or DESeq statistics package to assess the normalized gene count data. Results are produced as a list of differentially expressed genes, their associated gene counts, normalized gene counts, p- and q-values. The GORich tool can be used to identify enriched Gene Ontology (GO) terms in a list of differentially expressed genes. Three different methods are provided. The amount of time (hours) for steps to analyze over half a billion RNA-Seq reads is shown (GENE-counter running eight instances of CASHX with no throttle control and one instance of Bowtie with maximum throttle control (separated by a comma).</p>", "links"=>[], "tags"=>["diagram", "tools"], "article_id"=>397459, "categories"=>["Biochemistry", "Plant Biology", "Information And Computing Sciences", "Biological Sciences", "Genetics", "Biophysics"], "users"=>["Jason S. Cumbie", "Jeffrey A. Kimbrel", "Yanming Di", "Daniel W. Schafer", "Larry J. Wilhelm", "Samuel E. Fox", "Christopher M. Sullivan", "Aron D. Curzon", "James C. Carrington", "Todd C. Mockler", "Jeff H. Chang"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0025279.g001", "stats"=>{"downloads"=>1, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Entity_relationship_diagram_for_four_tools_of_GENE_counter_/397459", "title"=>"Entity-relationship diagram for four tools of GENE-counter.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-10-06 02:04:19"}

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Relative Metric

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