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dna microarray data  (MolGen LLC)

 
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    MolGen LLC dna microarray data
    Comparison of the properties of different <t> DNA-microarray </t> simulation models described in literature. '+' and '-' indicate availability of the indicated feature in the specified model. Note that the modeling of features in the specific models is usually not the same.
    Dna Microarray Data, supplied by MolGen LLC, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    Images

    1) Product Images from "SIMAGE : si mulation of DNA- m icro a rray g ene e xpression data"

    Article Title: SIMAGE : si mulation of DNA- m icro a rray g ene e xpression data

    Journal: BMC Bioinformatics

    doi: 10.1186/1471-2105-7-205

    Comparison of the properties of different DNA-microarray simulation models described in literature. '+' and '-' indicate availability of the indicated feature in the specified model. Note that the modeling of features in the specific models is usually not the same.
    Figure Legend Snippet: Comparison of the properties of different DNA-microarray simulation models described in literature. '+' and '-' indicate availability of the indicated feature in the specified model. Note that the modeling of features in the specific models is usually not the same.

    Techniques Used: Comparison, Gene Expression, Software

    Distribution of the deviations of several of the model parameters estimated from 100 simulated DNA-microarray slides. The deviation is calculated as (estimate - true value) / (standard deviation of 100 estimates).
    Figure Legend Snippet: Distribution of the deviations of several of the model parameters estimated from 100 simulated DNA-microarray slides. The deviation is calculated as (estimate - true value) / (standard deviation of 100 estimates).

    Techniques Used: Microarray, Standard Deviation

    Distribution of p -values of a DNA-microarray experiment simulated by SIMAGE . Data for 2200 genes, in 6 slides with technical duplicates hybridized in dye-swaps, was simulated using the MolGen experiment profile (supplementary Table T1) with some changes: π - = 1% and π + = 2%, μ - = -2 and μ + = 2), σ bg = 700, and s = 30 % × μ . The main graph shows the resulting ratios after normalization plotted versus the p -value. The graph was simplified by removing genes with ratios between 2/3 and 3/2. The 66 genes for which differential expressions were modeled are depicted by blue diamonds. The remaining genes are depicted in purple squares. The small graph on the right demonstrates the reversed p -value dependency on the average signal for the 66 differentially expressed genes modeled. The average signal was calculated for each of the 66 genes over the maximum of 12 normalized measurements. Normalization was performed using Lowess normalization and differential expression tests were performed with the non-Bayesian Cyber-T implementation of a variant of the t -test [3]. The Cyber-T test provides the p -values, which indicate the probability that a given ratio is not differential caused by chance. Genes with less than 8 measurements were excluded from these tests and assigned a p -value of 1, in order to be able to present these genes in the graph.
    Figure Legend Snippet: Distribution of p -values of a DNA-microarray experiment simulated by SIMAGE . Data for 2200 genes, in 6 slides with technical duplicates hybridized in dye-swaps, was simulated using the MolGen experiment profile (supplementary Table T1) with some changes: π - = 1% and π + = 2%, μ - = -2 and μ + = 2), σ bg = 700, and s = 30 % × μ . The main graph shows the resulting ratios after normalization plotted versus the p -value. The graph was simplified by removing genes with ratios between 2/3 and 3/2. The 66 genes for which differential expressions were modeled are depicted by blue diamonds. The remaining genes are depicted in purple squares. The small graph on the right demonstrates the reversed p -value dependency on the average signal for the 66 differentially expressed genes modeled. The average signal was calculated for each of the 66 genes over the maximum of 12 normalized measurements. Normalization was performed using Lowess normalization and differential expression tests were performed with the non-Bayesian Cyber-T implementation of a variant of the t -test [3]. The Cyber-T test provides the p -values, which indicate the probability that a given ratio is not differential caused by chance. Genes with less than 8 measurements were excluded from these tests and assigned a p -value of 1, in order to be able to present these genes in the graph.

    Techniques Used: Microarray, Quantitative Proteomics, Variant Assay

    Estimation of parameters from the simulation of 100 DNA-microarray slides. The mentioned deviations are the number of estimated standard-deviations that the estimated mean, respectively median, lie away from the true value of the parameter.
    Figure Legend Snippet: Estimation of parameters from the simulation of 100 DNA-microarray slides. The mentioned deviations are the number of estimated standard-deviations that the estimated mean, respectively median, lie away from the true value of the parameter.

    Techniques Used:

    Related Articles

    Microarray:

    Article Title: Changing a Single Amino Acid in Clostridium perfringens β-Toxin Affects the Efficiency of Heterologous Secretion by Bacillus subtilis
    Article Snippet: .. All DNA microarray data, including the slide images and raw data, obtained in this study are available online (http://molgen.biol.rug.nl/publication/btox_data/). ..

    Article Title: SIMAGE : si mulation of DNA- m icro a rray g ene e xpression data
    Article Snippet: .. To illustrate the use of SIMAGE in drawing meaningful conclusions about the design and analysis of DNA microarray experiments we show a number of examples based on DNA microarray data generated within the MolGen department. ..

    Article Title: Identification and Functional Characterization of the Lactococcus lactis CodY-Regulated Branched-Chain Amino Acid Permease BcaP (CtrA)
    Article Snippet: .. All DNA microarray data, including the slide images and raw data, obtained in this study are available online (http: //molgen.biol.rug.nl/publication/bcaP_data/). ..

    Generated:

    Article Title: SIMAGE : si mulation of DNA- m icro a rray g ene e xpression data
    Article Snippet: .. To illustrate the use of SIMAGE in drawing meaningful conclusions about the design and analysis of DNA microarray experiments we show a number of examples based on DNA microarray data generated within the MolGen department. ..



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    H1‐0 is consistently upregulated in preleukemia and BCP‐ALL expressing ETV6::RUNX1 . (A) Principal component analysis (PCA) plot of ETV6::RUNX1 + (E::R) and wild‐type (WT) hiPSC transcriptome profiles based on all detected genes ( n = 16,328). (B) Hierarchical clustering analysis of differentially expressed genes (absolute fold change > 2 and p < 0.05) between ETV6::RUNX1 + and WT hiPSCs detected by RNA‐seq. (C) H1‐0 expression levels determined by RT‐qPCR in ETV6::RUNX1 + and WT hiPSCs subjected to RNA‐seq. Values were normalized to HW8 WT expression levels as well as to GAPDH expression. (D) Representative Western blot analysis of ETV6::RUNX1, H1‐0, ETV6, and β‐actin levels in ETV6::RUNX1 + and WT hiPSCs. (E) H1‐0 levels in HSCs (CD19‐CD34+CD45RA‐), IL7R+ (CD19‐CD34+CD45RA+IL7R+), and pro‐B (CD19+CD34+) cells differentiated from ETV6::RUNX1 + or reverted MIFF3 hiPSCs, and fetal liver cells. Data are derived from an RNA‐seq dataset by Böiers et al. (accession number E‐MTAB‐6382 <xref ref-type= 10 ). Data were analyzed for statistical significance using an ordinary one‐way ANOVA (* p < 0.05, ** p < 0.01). H1‐0 levels across two leukemia patient cohorts derived from the (F) PeCan St. Jude database 30 , 31 and (G) an expression microarray dataset (accession number GSE87070 32 ). The number of patients per leukemia entity and mean expression is indicated. Data were analyzed for statistical significance using an ordinary one‐way ANOVA (*** p < 0.001). (H) H1‐0 expression was quantified by RT‐qPCR in PDX samples ( n = 9). Mean expression ± standard deviation is shown. (I) RNA‐seq expression levels of H1‐0 in control and ETV6 shRNA‐transduced REH cells. Data are derived from E‐MTAB‐10308 11 and are normalized to control shRNA. Mean expression ± standard deviation is indicated. Statistical significance was determined by performing a one‐way ANOWA (*** p < 0.001). (J) Pearson correlation of H1‐0 and RUNX1 expression in healthy bone marrow cells ( n = 71) derived from the MILE study (R2 platform, accession number GSE13159 33 ). " width="100%" height="100%">

    Journal: HemaSphere

    Article Title: H1‐0 is a specific mediator of the repressive ETV6::RUNX1 transcriptional landscape in preleukemia and B cell acute lymphoblastic leukemia

    doi: 10.1002/hem3.70116

    Figure Lengend Snippet: H1‐0 is consistently upregulated in preleukemia and BCP‐ALL expressing ETV6::RUNX1 . (A) Principal component analysis (PCA) plot of ETV6::RUNX1 + (E::R) and wild‐type (WT) hiPSC transcriptome profiles based on all detected genes ( n = 16,328). (B) Hierarchical clustering analysis of differentially expressed genes (absolute fold change > 2 and p < 0.05) between ETV6::RUNX1 + and WT hiPSCs detected by RNA‐seq. (C) H1‐0 expression levels determined by RT‐qPCR in ETV6::RUNX1 + and WT hiPSCs subjected to RNA‐seq. Values were normalized to HW8 WT expression levels as well as to GAPDH expression. (D) Representative Western blot analysis of ETV6::RUNX1, H1‐0, ETV6, and β‐actin levels in ETV6::RUNX1 + and WT hiPSCs. (E) H1‐0 levels in HSCs (CD19‐CD34+CD45RA‐), IL7R+ (CD19‐CD34+CD45RA+IL7R+), and pro‐B (CD19+CD34+) cells differentiated from ETV6::RUNX1 + or reverted MIFF3 hiPSCs, and fetal liver cells. Data are derived from an RNA‐seq dataset by Böiers et al. (accession number E‐MTAB‐6382 10 ). Data were analyzed for statistical significance using an ordinary one‐way ANOVA (* p < 0.05, ** p < 0.01). H1‐0 levels across two leukemia patient cohorts derived from the (F) PeCan St. Jude database 30 , 31 and (G) an expression microarray dataset (accession number GSE87070 32 ). The number of patients per leukemia entity and mean expression is indicated. Data were analyzed for statistical significance using an ordinary one‐way ANOVA (*** p < 0.001). (H) H1‐0 expression was quantified by RT‐qPCR in PDX samples ( n = 9). Mean expression ± standard deviation is shown. (I) RNA‐seq expression levels of H1‐0 in control and ETV6 shRNA‐transduced REH cells. Data are derived from E‐MTAB‐10308 11 and are normalized to control shRNA. Mean expression ± standard deviation is indicated. Statistical significance was determined by performing a one‐way ANOWA (*** p < 0.001). (J) Pearson correlation of H1‐0 and RUNX1 expression in healthy bone marrow cells ( n = 71) derived from the MILE study (R2 platform, accession number GSE13159 33 ).

    Article Snippet: Hence, we analyzed previously published 450K Infinium microarray DNA methylation data comprising patient samples of T‐ALL and six B‐ALL subtypes ( n = 546).

    Techniques: Expressing, RNA Sequencing, Quantitative RT-PCR, Western Blot, Derivative Assay, Microarray, Standard Deviation, Control, shRNA

    ETV6::RUNX1 induces H1‐0 promoter activation . (A) Schematic representation of the H1‐0 locus, including the 512‐bp region (nucleotides −351 to +161 from TSS) encompassing promoter‐like signature EH38E2163184 (ENCODE). The H1‐0 CpG island (CGI) shore and 450K Infinium array probes are indicated. (B) 293T cells were transfected with a vector encoding the H1‐0 promoter‐like signature indicated in (A) , together with the empty pcDNA3.1 vector or pcDNA3.1 expressing either ETV6::RUNX1 or RUNX1, and a vector expressing Renilla luciferase. Luciferase activities were normalized to Renilla luciferase activity and the empty vector control. Data represent mean values of three independent replicates ± standard deviation. Significance was calculated using an ordinary one‐way ANOVA (*** p < 0.001). Representative protein levels of ETV6::RUNX1, RUNX1, and β‐actin determined by Western blot are shown. (C) Pearson correlation of H1‐0 expression and mean DNA methylation of the H1‐0 CGI shore probes cg07141002 and cg01883777 in leukemia patients (accession number GSE49032 <xref ref-type= 41 ). Expression is shown for microarray probe 208886_at. Each dot represents a single patient. (D) H1‐0 DNA methylation in different leukemia entities is visualized as a heatmap with each column corresponding to a single patient (accession number GSE49032 41 ). Within each entity, patients are sorted according to mean DNA methylation of CGI shore probes cg07141002 and cg01883777. The total number of patients per entity is indicated. " width="100%" height="100%">

    Journal: HemaSphere

    Article Title: H1‐0 is a specific mediator of the repressive ETV6::RUNX1 transcriptional landscape in preleukemia and B cell acute lymphoblastic leukemia

    doi: 10.1002/hem3.70116

    Figure Lengend Snippet: ETV6::RUNX1 induces H1‐0 promoter activation . (A) Schematic representation of the H1‐0 locus, including the 512‐bp region (nucleotides −351 to +161 from TSS) encompassing promoter‐like signature EH38E2163184 (ENCODE). The H1‐0 CpG island (CGI) shore and 450K Infinium array probes are indicated. (B) 293T cells were transfected with a vector encoding the H1‐0 promoter‐like signature indicated in (A) , together with the empty pcDNA3.1 vector or pcDNA3.1 expressing either ETV6::RUNX1 or RUNX1, and a vector expressing Renilla luciferase. Luciferase activities were normalized to Renilla luciferase activity and the empty vector control. Data represent mean values of three independent replicates ± standard deviation. Significance was calculated using an ordinary one‐way ANOVA (*** p < 0.001). Representative protein levels of ETV6::RUNX1, RUNX1, and β‐actin determined by Western blot are shown. (C) Pearson correlation of H1‐0 expression and mean DNA methylation of the H1‐0 CGI shore probes cg07141002 and cg01883777 in leukemia patients (accession number GSE49032 41 ). Expression is shown for microarray probe 208886_at. Each dot represents a single patient. (D) H1‐0 DNA methylation in different leukemia entities is visualized as a heatmap with each column corresponding to a single patient (accession number GSE49032 41 ). Within each entity, patients are sorted according to mean DNA methylation of CGI shore probes cg07141002 and cg01883777. The total number of patients per entity is indicated.

    Article Snippet: Hence, we analyzed previously published 450K Infinium microarray DNA methylation data comprising patient samples of T‐ALL and six B‐ALL subtypes ( n = 546).

    Techniques: Activation Assay, Transfection, Plasmid Preparation, Expressing, Luciferase, Activity Assay, Control, Standard Deviation, Western Blot, DNA Methylation Assay, Microarray

    H1‐0 expression decreases during hematopoiesis . (A) H1‐0 expression in ETV6::RUNX1 + BCP‐ALL ( n = 6) and healthy B cell precursor stages derived from a published RNA‐seq dataset (accession number GSE115656 <xref ref-type= 45 ). B cell precursor fractions are HSCs (CD34+CD19‐IgM‐), pro‐B cells (CD34+CD19+IgM‐), pre‐B cells (CD34‐CD19+IgM‐) and immature B cells (CD34‐CD19+IgM+). (B) H1‐0 expression in healthy B cell precursor stages derived from a published expression microarray dataset (accession number GSE24759 46 ). B cell precursor fractions are HSCs (CD34+CD38‐), pro‐B cells (CD34+CD10+CD19+), pre‐B cells (CD34‐CD10+CD19+), naïve B cells (CD19+IgD+CD27‐), and mature B cells (CD19+IgD+CD27+). (B, C) Mean expression ± standard deviation is indicated and data was analyzed for statistical significance using an ordinary one‐way ANOVA (* p < 0.05, *** p < 0.001). (C) Min–max‐normalized mean expression per cell type derived from a fetal liver scRNA‐seq dataset (accession number E‐MTAB‐7407 47 ). (D) H1‐0 expression levels across normal B‐lymphoid differentiation distinguishing cell cycle status is depicted in a scRNA‐seq UMAP visualization of B cell precursor cells from bone marrow of eight healthy donors. 48 " width="100%" height="100%">

    Journal: HemaSphere

    Article Title: H1‐0 is a specific mediator of the repressive ETV6::RUNX1 transcriptional landscape in preleukemia and B cell acute lymphoblastic leukemia

    doi: 10.1002/hem3.70116

    Figure Lengend Snippet: H1‐0 expression decreases during hematopoiesis . (A) H1‐0 expression in ETV6::RUNX1 + BCP‐ALL ( n = 6) and healthy B cell precursor stages derived from a published RNA‐seq dataset (accession number GSE115656 45 ). B cell precursor fractions are HSCs (CD34+CD19‐IgM‐), pro‐B cells (CD34+CD19+IgM‐), pre‐B cells (CD34‐CD19+IgM‐) and immature B cells (CD34‐CD19+IgM+). (B) H1‐0 expression in healthy B cell precursor stages derived from a published expression microarray dataset (accession number GSE24759 46 ). B cell precursor fractions are HSCs (CD34+CD38‐), pro‐B cells (CD34+CD10+CD19+), pre‐B cells (CD34‐CD10+CD19+), naïve B cells (CD19+IgD+CD27‐), and mature B cells (CD19+IgD+CD27+). (B, C) Mean expression ± standard deviation is indicated and data was analyzed for statistical significance using an ordinary one‐way ANOVA (* p < 0.05, *** p < 0.001). (C) Min–max‐normalized mean expression per cell type derived from a fetal liver scRNA‐seq dataset (accession number E‐MTAB‐7407 47 ). (D) H1‐0 expression levels across normal B‐lymphoid differentiation distinguishing cell cycle status is depicted in a scRNA‐seq UMAP visualization of B cell precursor cells from bone marrow of eight healthy donors. 48

    Article Snippet: Hence, we analyzed previously published 450K Infinium microarray DNA methylation data comprising patient samples of T‐ALL and six B‐ALL subtypes ( n = 546).

    Techniques: Expressing, Derivative Assay, RNA Sequencing, Microarray, Standard Deviation

    In this study, genome-wide DNA methylation of 100 samples from 11 patients was assessed. Each patient had multiple primary small intestinal neuroendocrine tumours (ranging from 2-16 per patient), and a subset of 9 patients had a matched normal small intestinal epithelial sample assessed. A subset of 8 patients also had metastases originating from their SI-NETs (ranging from 1-2 per patient). DNA methylation data was used to assess differential methylation relating to the multifocal tumours, the epigenetic clock was used to assess the ‘timing’ of tumour development and metabolic traits predicted by DNA methylation were compared between samples.

    Journal: bioRxiv

    Article Title: Epigenetic investigation of multifocal small intestinal neuroendocrine tumours reveals accelerated ageing of tumours and epigenetic alteration of metabolic genes

    doi: 10.1101/2024.12.02.626017

    Figure Lengend Snippet: In this study, genome-wide DNA methylation of 100 samples from 11 patients was assessed. Each patient had multiple primary small intestinal neuroendocrine tumours (ranging from 2-16 per patient), and a subset of 9 patients had a matched normal small intestinal epithelial sample assessed. A subset of 8 patients also had metastases originating from their SI-NETs (ranging from 1-2 per patient). DNA methylation data was used to assess differential methylation relating to the multifocal tumours, the epigenetic clock was used to assess the ‘timing’ of tumour development and metabolic traits predicted by DNA methylation were compared between samples.

    Article Snippet: Genome-wide DNA methylation microarray data was generated on the Illumina iScan System using the Illumina Infinium MethylationEPIC BeadChip according to the manufacturer’s protocol.

    Techniques: Genome Wide, DNA Methylation Assay, Methylation

    (A) Plot of age predictions for normal epithelia samples (green), primary tumours (purple) and metastatic tumours (blue). Chronological age is indicated with the orange points and patients are indicated in order of chronological age. (B) Boxplot of age acceleration difference for the skin and blood clock predictions. (C) Somatic mutation count from tumours correlates with DNA methylation age in the skin and blood clock (p=0.0007).

    Journal: bioRxiv

    Article Title: Epigenetic investigation of multifocal small intestinal neuroendocrine tumours reveals accelerated ageing of tumours and epigenetic alteration of metabolic genes

    doi: 10.1101/2024.12.02.626017

    Figure Lengend Snippet: (A) Plot of age predictions for normal epithelia samples (green), primary tumours (purple) and metastatic tumours (blue). Chronological age is indicated with the orange points and patients are indicated in order of chronological age. (B) Boxplot of age acceleration difference for the skin and blood clock predictions. (C) Somatic mutation count from tumours correlates with DNA methylation age in the skin and blood clock (p=0.0007).

    Article Snippet: Genome-wide DNA methylation microarray data was generated on the Illumina iScan System using the Illumina Infinium MethylationEPIC BeadChip according to the manufacturer’s protocol.

    Techniques: Mutagenesis, DNA Methylation Assay