Identification of Novel Reference Genes Using Multiplatform Expression Data and Their Validation for Quantitative Gene Expression Analysis
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{"title"=>"Identification of novel reference genes using multiplatform expression data and their validation for quantitative gene expression analysis", "type"=>"journal", "authors"=>[{"first_name"=>"Mi Jeong", "last_name"=>"Kwon", "scopus_author_id"=>"56509903400"}, {"first_name"=>"Ensel", "last_name"=>"Oh", "scopus_author_id"=>"35278737100"}, {"first_name"=>"Seungmook", "last_name"=>"Lee", "scopus_author_id"=>"55870216100"}, {"first_name"=>"Mi Ra", "last_name"=>"Roh", "scopus_author_id"=>"35996339800"}, {"first_name"=>"Si Eun", "last_name"=>"Kim", "scopus_author_id"=>"35278225500"}, {"first_name"=>"Yangsoon", "last_name"=>"Lee", "scopus_author_id"=>"57199022450"}, {"first_name"=>"Yoon La", "last_name"=>"Choi", "scopus_author_id"=>"7404777529"}, {"first_name"=>"Yong Ho", "last_name"=>"In", "scopus_author_id"=>"36853401500"}, {"first_name"=>"Taesung", "last_name"=>"Park", "scopus_author_id"=>"34668299500"}, {"first_name"=>"Sang Seok", "last_name"=>"Koh", "scopus_author_id"=>"57197059359"}, {"first_name"=>"Young Kee", "last_name"=>"Shin", "scopus_author_id"=>"26428533000"}], "year"=>2009, "source"=>"PLoS ONE", "identifiers"=>{"pmid"=>"19584937", "doi"=>"10.1371/journal.pone.0006162", "sgr"=>"67650231135", "isbn"=>"1932-6203", "scopus"=>"2-s2.0-67650231135", "issn"=>"19326203", "pui"=>"354885399"}, "id"=>"0a4bc436-449f-3e4f-99e3-c01ab0c84606", "abstract"=>"Normalization of mRNA levels using endogenous reference genes (ERGs) is critical for an accurate comparison of gene expression between different samples. Despite the popularity of traditional ERGs (tERGs) such as GAPDH and ACTB, their expression variability in different tissues or disease status has been reported. Here, we first selected candidate housekeeping genes (HKGs) using human gene expression data from different platforms including EST, SAGE, and microarray, and 13 novel ERGs (nERGs) (ARL8B, CTBP1, CUL1, DIMT1L, FBXW2, GPBP1, LUC7L2, OAZ1, PAPOLA, SPG21, TRIM27, UBQLN1, ZNF207) were further identified from these HKGs. The mean coefficient variation (CV) values of nERGs were significantly lower than those of tERGs and the expression level of most nERGs was relatively lower than high expressing tERGs in all dataset. The higher expression stability and lower expression levels of most nERGs were validated in 108 human samples including formalin-fixed paraffin-embedded (FFPE) tissues, frozen tissues and cell lines, through quantitative real-time RT-PCR (qRT-PCR). Furthermore, the optimal number of nERGs required for accurate normalization was as few as two, while four genes were required when using tERGs in FFPE tissues. Most nERGs identified in this study should be better reference genes than tERGs, based on their higher expression stability and fewer numbers needed for normalization when multiple ERGs are required.", "link"=>"http://www.mendeley.com/research/identification-novel-reference-genes-using-multiplatform-expression-data-validation-quantitative-gen-2", "reader_count"=>54, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>2, "Researcher"=>18, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>20, "Student > Postgraduate"=>2, "Student > Master"=>6, "Other"=>2, "Student > Bachelor"=>1}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>2, "Researcher"=>18, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>20, "Student > Postgraduate"=>2, "Student > Master"=>6, "Other"=>2, "Student > Bachelor"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>1, "Biochemistry, Genetics and Molecular Biology"=>6, "Agricultural and Biological Sciences"=>37, "Medicine and Dentistry"=>7, "Neuroscience"=>1, "Chemistry"=>1, "Computer Science"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>7}, "Neuroscience"=>{"Neuroscience"=>1}, "Chemistry"=>{"Chemistry"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>37}, "Computer Science"=>{"Computer Science"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>6}, "Unspecified"=>{"Unspecified"=>1}}, "reader_count_by_country"=>{"Austria"=>2, "Sweden"=>1, "United States"=>6, "Ukraine"=>1, "Brazil"=>1, "Australia"=>1, "Chile"=>1, "France"=>2, "Germany"=>2}, "group_count"=>1}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/892212"], "description"=>"<p>(A) Comparison of gene expression between 13 nERGs and 13 tERGs. (B) Comparison of CV between 13 nERGs and 13 tERGs. Empty squares represent nERGs identified in this study and circles represent the tERGs.</p>", "links"=>[], "tags"=>["cv", "nergs", "tergs"], "article_id"=>562651, "categories"=>["Genetics", "Medicine"], "users"=>["Mi Jeong Kwon", "Ensel Oh", "Seungmook Lee", "Mi Ra Roh", "Si Eun Kim", "Yangsoon Lee", "Yoon-La Choi", "Yong-Ho In", "Taesung Park", "Sang Seok Koh", "Young Kee Shin"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0006162.g003", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_gene_expression_and_CV_between_nERGs_and_tERGs_in_each_dataset_/562651", "title"=>"Comparison of gene expression and CV between nERGs and tERGs in each dataset.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-07-07 00:44:11"}
  • {"files"=>["https://ndownloader.figshare.com/files/892889"], "description"=>"*<p>Gene expression stability S by NormFinder was calculated as an estimate of the combined intra and intergroup variation between normal and tumor tissues. For ovary tissues (n = 33), 10 normal and 23 tumor tissues were included and 17 stomach tissues, including normal (n = 8) and tumor (n = 9) tissues, were used in the analysis.</p><p>Low average expression stability M and stability value S indicate the high expression stability.</p>", "links"=>[], "tags"=>["tergs", "ranked", "calculated", "genorm", "qrt-pcr", "ffpe"], "article_id"=>563335, "categories"=>["Genetics", "Medicine"], "users"=>["Mi Jeong Kwon", "Ensel Oh", "Seungmook Lee", "Mi Ra Roh", "Si Eun Kim", "Yangsoon Lee", "Yoon-La Choi", "Yong-Ho In", "Taesung Park", "Sang Seok Koh", "Young Kee Shin"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0006162.t007", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_nERGs_and_tERGs_ranked_according_to_their_expression_stability_as_calculated_by_the_two_programs_geNorm_and_NormFinder_based_on_qRT_PCR_data_in_each_tissue_type_of_FFPE_tissues_/563335", "title"=>"nERGs and tERGs ranked according to their expression stability, as calculated by the two programs, geNorm and NormFinder, based on qRT-PCR data in each tissue type of FFPE tissues.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2009-07-07 00:55:35"}
  • {"files"=>["https://ndownloader.figshare.com/files/892747"], "description"=>"*<p>Of 2,087 genes, 2,002 UniGene clusters corresponded to upstream sequences downloaded from the UCSC site (<a href=\"http://hgdownload.cse.ucsc.edu/goldenPath/hg18/bigZips/\" target=\"_blank\">http://hgdownload.cse.ucsc.edu/goldenPath/hg18/bigZips/</a>).</p>**<p>A total of 16,850 UniGene clusters corresponded to upstream sequences downloaded from the UCSC site and the number of non-HKGs was calculated by subtracting the 2,002 HKGs from the total 16,850.</p><p>CpG island criteria: length≥500 bp, % GC≥55, CpG o/e ratio≥0.65.</p>", "links"=>[], "tags"=>["genes", "cpg", "islands", "sequences", "upstream", "transcription", "hkgs"], "article_id"=>563189, "categories"=>["Genetics", "Medicine"], "users"=>["Mi Jeong Kwon", "Ensel Oh", "Seungmook Lee", "Mi Ra Roh", "Si Eun Kim", "Yangsoon Lee", "Yoon-La Choi", "Yong-Ho In", "Taesung Park", "Sang Seok Koh", "Young Kee Shin"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0006162.t002", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_the_proportion_of_genes_with_CpG_islands_in_sequences_upstream_of_the_transcription_start_site_in_HKGs_and_non_HKGs_/563189", "title"=>"Comparison of the proportion of genes with CpG islands in sequences upstream of the transcription start site in HKGs and non-HKGs.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2009-07-07 00:53:09"}
  • {"files"=>["https://ndownloader.figshare.com/files/892701"], "description"=>"<p>Mean: Mean gene expression, CV: Coefficient of Variation (%), 0's P: 0's proportion, GO terms were searched in the Gene Ontology site (<a href=\"http://www.geneontology.org/\" target=\"_blank\">http://www.geneontology.org/</a>).</p>", "links"=>[], "tags"=>["genetics and genomics/bioinformatics", "genetics and genomics/gene expression", "genetics and genomics/genomics", "pathology/clinical chemistry", "pathology/molecular pathology"], "article_id"=>563142, "categories"=>["Genetics", "Medicine"], "users"=>["Mi Jeong Kwon", "Ensel Oh", "Seungmook Lee", "Mi Ra Roh", "Si Eun Kim", "Yangsoon Lee", "Yoon-La Choi", "Yong-Ho In", "Taesung Park", "Sang Seok Koh", "Young Kee Shin"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0006162.t003", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_nERGs_identified_from_four_datasets_/563142", "title"=>"nERGs identified from four datasets.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2009-07-07 00:52:22"}
  • {"files"=>["https://ndownloader.figshare.com/files/892052"], "description"=>"<p>(A) The functional distribution of candidate HKGs was classified by FunCat. Among the 2,087 UniGene clusters, a total of 1,605 UniGene clusters which have GO terms under biological processes were classified according to their major functional categories using FunCat (version 2.0) by mapping the GO terms to FunCat categories. A total of 1,318 UniGene clusters were classified by FunCat. The number of UniGene clusters belonging to each category is presented in A. In some cases, UniGene clusters mapped to two or more FunCat categories. (B) Comparison of gene expression between candidate HKGs and non-HKGs in each dataset. Box and Whisker plots provide a simple description of a distribution of values by depicting the 25<sup>th</sup> and 75<sup>th</sup> percentile values as the bottom and top of a box, respectively. The Y axis represents the natural logarithm transformed mean gene expression levels. The median expression values of HKGs and non-HKGs are marked by horizontal lines in the boxes and the values are provided to the right of each box. *<i>P</i><0.001.</p>", "links"=>[], "tags"=>["hkgs"], "article_id"=>562499, "categories"=>["Genetics", "Medicine"], "users"=>["Mi Jeong Kwon", "Ensel Oh", "Seungmook Lee", "Mi Ra Roh", "Si Eun Kim", "Yangsoon Lee", "Yoon-La Choi", "Yong-Ho In", "Taesung Park", "Sang Seok Koh", "Young Kee Shin"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0006162.g002", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Characterization_of_candidate_HKGs_identified_in_this_study_/562499", "title"=>"Characterization of candidate HKGs identified in this study.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-07-07 00:41:39"}
  • {"files"=>["https://ndownloader.figshare.com/files/892357"], "description"=>"<p>(A) The distribution of mRNA levels of tested ERGs in 48 samples, including frozen tissues and cancer cell lines. (B) The mRNA levels of tERGs (red) and nERGs (blue) in Cp values over all 48 samples (left) and 60 FFPE tissues (right). Values are given as “Crossing point” (Cp) values. All measurements of qRT-PCR were repeated three times for frozen tissues and cell lines and twice for FFPE tissues and mean “crossing point” (Cp) values of repeats were calculated. Box and Whisker plots provide a simple description of the distribution of values by depicting the 25<sup>th</sup> and 75<sup>th</sup> percentile values as the bottom and top of the box, respectively. The median value is marked by a line within the box and the minimum and maximum values are depicted by error bars, or whiskers, protruding from the box.</p>", "links"=>[], "tags"=>["levels", "13", "nergs", "tergs", "qrt-pcr", "taqman", "probes"], "article_id"=>562801, "categories"=>["Genetics", "Medicine"], "users"=>["Mi Jeong Kwon", "Ensel Oh", "Seungmook Lee", "Mi Ra Roh", "Si Eun Kim", "Yangsoon Lee", "Yoon-La Choi", "Yong-Ho In", "Taesung Park", "Sang Seok Koh", "Young Kee Shin"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0006162.g004", "stats"=>{"downloads"=>1, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_distribution_of_expression_levels_of_13_nERGs_and_7_tERGs_determined_by_qRT_PCR_using_Taqman_probes_in_human_samples_/562801", "title"=>"The distribution of expression levels of 13 nERGs and 7 tERGs determined by qRT-PCR using Taqman probes in human samples.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-07-07 00:46:41"}
  • {"files"=>["https://ndownloader.figshare.com/files/891845"], "description"=>"<p>2,087 candidate HKGs were first identified by selecting the genes meeting the following criteria: 0's prop <0.4 in EST, <0.1 in shortSAGE and <0.3 in longSAGE. 0's prop represents 0's proportion (number of tissues in which the gene is not expressed/total number of tissues, 0≤0's prop≤1). Among the candidate 2,087 HKGs, 13 nERGs with the lowest CVs were further identified by selecting the genes common to all four datasets among the genes with the 400 lowest CVs (approximately 20% of candidate HKGs).</p>", "links"=>[], "tags"=>["methodology"], "article_id"=>562288, "categories"=>["Genetics", "Medicine"], "users"=>["Mi Jeong Kwon", "Ensel Oh", "Seungmook Lee", "Mi Ra Roh", "Si Eun Kim", "Yangsoon Lee", "Yoon-La Choi", "Yong-Ho In", "Taesung Park", "Sang Seok Koh", "Young Kee Shin"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0006162.g001", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Flowchart_of_the_methodology_for_identification_of_nERGs_/562288", "title"=>"Flowchart of the methodology for identification of nERGs.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-07-07 00:38:08"}
  • {"files"=>["https://ndownloader.figshare.com/files/892654"], "description"=>"*<p>UPL probes were designed using Probe Finder in the Universal Probe Library (UPL) Assay Design Center (Roche Applied Science, Mannheim, Germany).</p>**<p>PCR efficiency for each gene was determined by using serial dilutions of cDNA from MKN 74 cells for PCR and then calculating the efficiency using the Roche Lightcycler software 4.0.</p>***<p>PCR efficiencies of 48 samples in triplicate were calculated using LinRegPCR (Ramakers et al. Neurosci Lett, 2003. <b>339</b>(1): p. 62–6.).</p>#<p><b>F-</b>ttcttgctggtcttgccat<b>T</b>cctgga-<b>p</b> (T, TAMRA-labeled; F, FAM-labeled; P, Phosphate).</p>", "links"=>[], "tags"=>["pcr", "primers", "taqman", "probes"], "article_id"=>563100, "categories"=>["Genetics", "Medicine"], "users"=>["Mi Jeong Kwon", "Ensel Oh", "Seungmook Lee", "Mi Ra Roh", "Si Eun Kim", "Yangsoon Lee", "Yoon-La Choi", "Yong-Ho In", "Taesung Park", "Sang Seok Koh", "Young Kee Shin"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0006162.t001", "stats"=>{"downloads"=>4, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Real_time_PCR_primers_and_Taqman_probes_used_in_this_study_/563100", "title"=>"Real-time PCR primers and Taqman probes used in this study.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2009-07-07 00:51:40"}
  • {"files"=>["https://ndownloader.figshare.com/files/892510"], "description"=>"<p>Variable V defines the pair-wise variation between two sequential normalization factors containing an increasing number of genes. For example, V2/3 indicates the variation of the normalization factor of two genes in relation to three genes. A large V indicates that the added gene should be included for calculation of the normalization factor. 0.15 was proposed as a cut-off value, below which the inclusion of an additional reference gene is not required. Pair-wise variation analysis to determine the number of ERGs required for accurate normalization was performed in 48 samples including human frozen tissues and cell lines (A), 60 FFPE tissues (B), 33 ovary FFPE tissues (C) and 17 stomach FFPE tissues (D). For each case, the analysis was done for total ERGs, tERGs and nERGs.</p>", "links"=>[], "tags"=>["ergs", "normalization", "calculated", "genorm"], "article_id"=>562954, "categories"=>["Genetics", "Medicine"], "users"=>["Mi Jeong Kwon", "Ensel Oh", "Seungmook Lee", "Mi Ra Roh", "Si Eun Kim", "Yangsoon Lee", "Yoon-La Choi", "Yong-Ho In", "Taesung Park", "Sang Seok Koh", "Young Kee Shin"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0006162.g005", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Optimal_number_of_ERGs_for_normalization_calculated_by_the_geNorm_program_/562954", "title"=>"Optimal number of ERGs for normalization calculated by the geNorm program.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-07-07 00:49:14"}
  • {"files"=>["https://ndownloader.figshare.com/files/892941"], "description"=>"<p>M: average expression stability calculated by the geNorm program, S: stability value calculated by the NormFinder program. For 48 samples, 20 reference genes including 13 novel genes and 7 classical genes, were used in the correlation analysis. For 60 FFPE tissues, 19 reference genes excluding DIMT1L were included in the analysis.</p>", "links"=>[], "tags"=>["nergs", "tergs", "qrt-pcr", "cv"], "article_id"=>563381, "categories"=>["Genetics", "Medicine"], "users"=>["Mi Jeong Kwon", "Ensel Oh", "Seungmook Lee", "Mi Ra Roh", "Si Eun Kim", "Yangsoon Lee", "Yoon-La Choi", "Yong-Ho In", "Taesung Park", "Sang Seok Koh", "Young Kee Shin"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0006162.t006", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Correlation_between_gene_expression_stability_of_nERGs_and_tERGs_from_qRT_PCR_data_and_CV_from_each_dataset_/563381", "title"=>"Correlation between gene expression stability of nERGs and tERGs from qRT-PCR data and CV from each dataset.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2009-07-07 00:56:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/441822", "https://ndownloader.figshare.com/files/441937", "https://ndownloader.figshare.com/files/441979", "https://ndownloader.figshare.com/files/442037", "https://ndownloader.figshare.com/files/442069", "https://ndownloader.figshare.com/files/442109", "https://ndownloader.figshare.com/files/442150", "https://ndownloader.figshare.com/files/442195", "https://ndownloader.figshare.com/files/442239", "https://ndownloader.figshare.com/files/442281", "https://ndownloader.figshare.com/files/442320", "https://ndownloader.figshare.com/files/442365"], "description"=>"<div><p>Normalization of mRNA levels using endogenous reference genes (ERGs) is critical for an accurate comparison of gene expression between different samples. Despite the popularity of traditional ERGs (tERGs) such as <em>GAPDH</em> and <em>ACTB</em>, their expression variability in different tissues or disease status has been reported. Here, we first selected candidate housekeeping genes (HKGs) using human gene expression data from different platforms including EST, SAGE, and microarray, and 13 novel ERGs (nERGs) (<em>ARL8B, CTBP1, CUL1, DIMT1L, FBXW2, GPBP1, LUC7L2, OAZ1, PAPOLA, SPG21, TRIM27, UBQLN1, ZNF207</em>) were further identified from these HKGs. The mean coefficient variation (CV) values of nERGs were significantly lower than those of tERGs and the expression level of most nERGs was relatively lower than high expressing tERGs in all dataset. The higher expression stability and lower expression levels of most nERGs were validated in 108 human samples including formalin-fixed paraffin-embedded (FFPE) tissues, frozen tissues and cell lines, through quantitative real-time RT-PCR (qRT-PCR). Furthermore, the optimal number of nERGs required for accurate normalization was as few as two, while four genes were required when using tERGs in FFPE tissues. Most nERGs identified in this study should be better reference genes than tERGs, based on their higher expression stability and fewer numbers needed for normalization when multiple ERGs are required.</p></div>", "links"=>[], "tags"=>["genes", "multiplatform", "validation", "quantitative"], "article_id"=>147096, "categories"=>["Genetics", "Medicine"], "users"=>["Mi Jeong Kwon", "Ensel Oh", "Seungmook Lee", "Mi Ra Roh", "Si Eun Kim", "Yangsoon Lee", "Yoon-La Choi", "Yong-Ho In", "Taesung Park", "Sang Seok Koh", "Young Kee Shin"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0006162.s001", "https://dx.doi.org/10.1371/journal.pone.0006162.s002", "https://dx.doi.org/10.1371/journal.pone.0006162.s003", "https://dx.doi.org/10.1371/journal.pone.0006162.s004", "https://dx.doi.org/10.1371/journal.pone.0006162.s005", "https://dx.doi.org/10.1371/journal.pone.0006162.s006", "https://dx.doi.org/10.1371/journal.pone.0006162.s007", "https://dx.doi.org/10.1371/journal.pone.0006162.s008", "https://dx.doi.org/10.1371/journal.pone.0006162.s009", "https://dx.doi.org/10.1371/journal.pone.0006162.s010", "https://dx.doi.org/10.1371/journal.pone.0006162.s011", "https://dx.doi.org/10.1371/journal.pone.0006162.s012"], "stats"=>{"downloads"=>55, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Identification_of_Novel_Reference_Genes_Using_Multiplatform_Expression_Data_and_Their_Validation_for_Quantitative_Gene_Expression_Analysis/147096", "title"=>"Identification of Novel Reference Genes Using Multiplatform Expression Data and Their Validation for Quantitative Gene Expression Analysis", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2009-07-07 01:58:16"}
  • {"files"=>["https://ndownloader.figshare.com/files/892846"], "description"=>"*<p>Genomic location was found using Ensembl (<a href=\"http://www.ensembl.org/index.html\" target=\"_blank\">http://www.ensembl.org/index.html</a>).</p>**<p>Genomic variations were found using the Database of Genomic Variants (<a href=\"http://projects.tcag.ca/variation/\" target=\"_blank\">http://projects.tcag.ca/variation/</a>, Human Genome Assembly Build 36 (hg18)).</p>", "links"=>[], "tags"=>["variations", "nergs"], "article_id"=>563297, "categories"=>["Genetics", "Medicine"], "users"=>["Mi Jeong Kwon", "Ensel Oh", "Seungmook Lee", "Mi Ra Roh", "Si Eun Kim", "Yangsoon Lee", "Yoon-La Choi", "Yong-Ho In", "Taesung Park", "Sang Seok Koh", "Young Kee Shin"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0006162.t004", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Gene_copy_number_variations_of_nERGs_and_tERGs_/563297", "title"=>"Gene copy number variations of nERGs and tERGs.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2009-07-07 00:54:57"}
  • {"files"=>["https://ndownloader.figshare.com/files/892809"], "description"=>"<p>Low average expression stability value M and stability value S indicate the high expression stability.</p>", "links"=>[], "tags"=>["tergs", "ranked", "calculated", "genorm", "qrt-pcr", "48", "lines", "60", "ffpe"], "article_id"=>563250, "categories"=>["Genetics", "Medicine"], "users"=>["Mi Jeong Kwon", "Ensel Oh", "Seungmook Lee", "Mi Ra Roh", "Si Eun Kim", "Yangsoon Lee", "Yoon-La Choi", "Yong-Ho In", "Taesung Park", "Sang Seok Koh", "Young Kee Shin"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0006162.t005", "stats"=>{"downloads"=>7, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_nERGs_and_tERGs_ranked_according_to_their_expression_stability_as_calculated_by_the_two_programs_geNorm_and_NormFinder_based_on_qRT_PCR_data_in_48_frozen_tissues_cell_lines_and_60_FFPE_tissues_/563250", "title"=>"nERGs and tERGs ranked according to their expression stability, as calculated by the two programs, geNorm and NormFinder, based on qRT-PCR data in 48 frozen tissues/cell lines and 60 FFPE tissues.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2009-07-07 00:54:10"}

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Relative Metric

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