Spike-in at 0.25pmol with 18 replicates
Ontology highlight
ABSTRACT:
ORGANISM(S): Mus musculus
PROVIDER: GSE8655 | GEO | 2007/08/03
SECONDARY ACCESSION(S): PRJNA101851
REPOSITORIES: GEO
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Project description:Spectral Counts approaches (SpCs) are largely employed for the comparison of protein expression profiles in label free differential proteomics applications. Similarly, to other comparative methods, also SpCs based approaches require a normalization procedure before Fold Changes (FC) calculation. Here, we propose new Complexity Based Normalization (CBN) methods that introduced a variable adjustment factor (f), related to the complexity of the sample, both in terms of total number of identified proteins (CBN(P)) and as total number of spectral counts (CBN(S)). Both these new methods were compared with the Normalized Spectral Abundance Factor (NSAF) and the Spectral Counts log Ratio (Rsc), by using standard protein mixtures. Finally, to test the robustness and the effectiveness of the CBNs methods, they were employed for the comparative analysis ofcortical protein extract fromzQ175 mouse brains, model of Huntington Disease (HD), and control animals. On standard mixtures, both CBN methods showed an excellent behavior in terms of reproducibility and coefficients of variation (CVs) in comparison to the other SpCs approaches. Moreover, the CBN(S) was demonstrated to be more sensitive in detecting small difference in proteins amount when applied on biological samples in comparison to CBN(P).
2020-08-18 | PXD017471 | Pride
Project description:Allele-specific copy number analysis of tumors (ASCAT) assesses copy number variations (CNV) while accounting for aberrant cell fraction (ACF) and tumor ploidy. Here, we evaluate if ASCAT-assessed CNV are associated with survival outcomes in patients with WHO grade IV gliomas. Methods: We identified 56 patients with WHO grade IV gliomas. Tumor data analyzed by Affymetrix OncoScan FFPE Assay yielded the log ratio (R) and B-allele frequency (BAF). Input into ASCAT quantified CNV using the segmentation function to measure copy number inflection points throughout the genome. Actuarial overall survival (OS) and progression-free survival (PFS) assessed with Kaplan-Meier method and subgroups compared by the log-rank test. Results were validated on The Cancer Genome Atlas (TCGA) glioblastoma dataset. Results: Our cohort’s median age was 60.4 years (range: 26.1-86.3). Tumors were hyper-methylated at MGMT in 25 (44.6%) with IDH1 mutations in 6 (10.7%). Median follow-up time was 36.4 months, and median PFS and OS were 8 and 14.7 months, respectively. PFS was longer for higher log R and BAF segment counts with hazard ratios (HR) of 0.32 and 0.49 respectively (p<0.001 and p=0.022). Patients with higher log R segment counts had significantly longer 12-month OS (81% vs 46.3%, p=0.0088). In the TCGA validation cohort, higher BAF segment counts had longer 12-month OS with a trend for log R (62.3% vs. 51.9%, p=0.0129 and 63.6% vs. 55.2%, p=0.0696 respectively). Conclusions: Our analysis demonstrated a longer PFS and OS for patients with increased genomic CNV. This may provide a novel method for assessing glioblastoma outcomes.
2019-01-18 | GSE125255 | GEO
Project description:Purpose: Next-generation sequencing (NGS) has revolutionized systems-based analysis of cellular pathways. The goals of this study are using RNA sequencing analysis to test the effects of OTUD6B-knockdown on the mRNA of KYSE30 and KYSE450 cells. Methods: KYSE30 and KYSE450 cells were transfected with negative control or siOTUD6B for 48 hours in 1640 medium plus 10% serum. Three independent replicates were plated, transfected in parallel for each negative control and siOTUD6B. Results: Log-fold changes of mRNAs between negative control and siOTUD6B group were selected with a significance threshod of p<0.05. Conclusions: Our study determined the mRNA changes of OTUD6B-knockdown KYSE30 and KYSE450 cells.
2023-04-01 | GSE209521 | GEO
Project description:Array Design; Mouse Compugen 22K Spotted Oligo Author; Sean Grimmond Common Reference (Yes/No, ID); Yes Mouse universal Reference (Stratagene) Concentration; 100 Data processing/normalization; Lowess Normalisation GeneSpring) Developmental Stage; embryonic Diseased/Normal; normal Dye Swap; red/green Experiment Type; time course Genetic Characteristic; Wild type Image Analysis Software; Imagene5.5 (Biodiscovery) Labeling Protocol; Amino Allyl Message Amp- Ambion Organ/Organism part; kidney Organism; Mus Musculus RNA type; aRNA Message Amp-ambion Tissue Type; Pool of whole organs Organization; IMB, Uni of QLD, Brisbane Australia Sample Source; primary tissue Sampling Method; Dissection of whole organ Sex; Pool of both sexes Strain/Cell-line; CD1 Series_overall_design: To account for dye swap, the signal channel and control channel measurements for each sample were reversed. A Lowess curve was fit to the log-intensity versus log-ratio plot. 20.0% of the data was used to calculate the Lowess fit at each point. This curve was used to adjust the control value for each measurement. If the control channel was lower than 10 then 10 was used instead. Specific samples were normalized to one another: sample(s) 1-38 were normalized against the median of the control sample(s) 1-38. Each measurement for each gene in those specific samples was divided by the median of that gene's measurements in the corresponding control samples. Keywords: time-course
2005-07-20 | GSE2282 | GEO
Project description:Structural and functional impacts of copy number variations (CNVs) on livestock genomes are not yet well understood. In this study, we have identified 1853 CNV regions (CNVRs) using population-scale sequencing data generated from 75 cattle of 8 breeds (Holstein, Angus, Jersey, Limousin, Romagnola, Brahman, Gir and Nelore). Individual genome sequence coverage ranged from 4 to 30 fold, with a mean of 11.8 fold. A total of 3.1% (87.5 Mb) of the cattle genome is predicted to be copy number variable, representing a substantial increase over the previous estimates (~2%). This dataset was highly correlated with array CGH data (r2 = 0.761) and was validated to be accurate with an estimated 12% false positive rate and a 19% false negative rate based on qPCR and array CGH, respectively. Hundreds of CNVs were found to be either breed specific or differentially variable across breeds, including the RICTOR gene in dairy breeds and the PNPLA3 gene in the beef breeds. In contrast, clusters of the PRP and PAG genes are duplicated in all sequenced animals, implicating that subfunctionalization, neofunctionalization or overdominance play a role in diversifying these fertility related genes. Further population-genetic analyses based on CNVs revealed the population structures of these taurine and indicine breeds and uncovered hundreds of positively selected CNV candidates near important functional genes. These CNV results provide a new glimpse of diverse selections during cattle speciation, domestication, breed formation, and recent genetic improvement. 25 animals were analyzed using a custom Nimblegen aCGH chip with 2.1 million probes. The reference animal chosen was L1 Dominette, a Hereford cow of European ancestry. The array was subjected to a dye-swap with the reference sample to test probe intensity fidelity. Single channel intensity data from the array was used in a digital aCGH analysis to compare aCGH copy number estimates to copy number estimates derived from sequence data. Briefly, the reference signal from all analyzed arrays was collected and a median signal intensity was calculated from probe intensities within the BTF3 gene. The copy number of the reference animal was then inferred by division of single channel probe intensities with the median intensity of the BTF3 gene. Next, test sample intensities were normalized by taking the log2 ratio of the test intensity divided by the normalized reference copy number for the probe. CN values derived from sequence data were also normalized in this fashion by taking the log2 of the ratio of NGS CN divided by aCGH reference copy number.
2015-11-04 | E-GEOD-62990 | biostudies-arrayexpress
Project description:Experiment Design 1) Experiment Type normal vs diseased comparison, time course 2) Experimental Factor response to viral infection - SHIV89.6P 3) Number of hybridizations 60 4) Common Reference No 5) Quality Control Steps 4 technical replicate hybridizations per animal (3 replicate hybridizations for C89115M, C95067F) dual spotting of gene per array multiple spotting of select genes per array 2 biological replicates (2 different animals) per time point dye swaps 6) Experiment Description 4 cynomolgus monkeys (Macaca fascicularis) were infected with TCID50 of SHIV89.6P. Whole blood RNA was collected at 0, 1, 2, 4 and 5 weeks post-infection. Post-infection samples were competitively hybridized with pre-infected samples for each individual animal. Unpublished Data 7) Qualifier, value, source 2.2. Samples: samples used, extract preparation and labeling 2.2.1. Biosource properties 1) Organism (NCBI taxonomy) Macaca fascicularis 2) Contact details for sample Dr Erling W Rud Animal Resources Division, Health Canada. Sir Frederick Banting Research Centre Building 22, Room C308, postal locator 2203E Tunneys' Pasture Ottawa,Ontario, Canada K1A 0L2 Tel:(613) 957-8049 FAX:(613) 941-6625 erling_rud@hc-sc.gc.ca 3) Cell Type not applicable 4) Sex C88015F - female C89115M - male C94071M - male C95067F - female 5) Age C88015F - 13 years C89115M - 12 years C94071M - 7 years C95067F - 6 years 6) Developmental Stage adult 7) Organism Part peripheral whole blood 8) Strain or line not applicable 9) Genetic variation not applicable 10) Individual number C88015F, C89115M, C94071M & C95067F 11) Individual genetic characteristics not applicable 12) Disease state normal, AIDS 13) Targeted cell type blood 14) Cell line not applicable 2.2.2. Biomaterial manipulation 1) Growth conditions not applicable 2) In vivo treatments description - 4 cynomolgus monkeys (Macaca fascicularis) were infected intravenously with TCID50 of SHIV89.6P. 10 ml of peripheral blood was drawn at -2, 0, 1, 2, 4 and 5 weeks post-infection. 3) In vitro treatment not applicable 4) Treatment type infection 5) Compound not applicable 6) Separation technique not applicable 2.2.3. Hybridization extract preparation 1) Extraction method PAXgene peripheral blood RNA preparation 10 ml of peripheral blood was drawn from each animal directly into 4 PAXgene Blood RNA Tubes (QIAGEN #762125) according to manufacturer's protocol (http://www.qiagen.com/literature/Handbooks/PDF/DNA_RNA_isolation_clinical/INT/PAXgene_Blood_RNA/PaxgeneBloodRNAtube.pdf) Total RNA was purified using PAXgene Blood RNA kit (QIAGEN # 762134) according to manufacturer's protocol http://www.qiagen.com/literature/Handbooks/PDF/DNA_RNA_isolation_clinical/INT/PAXgene_Blood_RNA/1017455PAXgeneBloodRNA_prot.pdf) 2) Nucleic acid type total RNA 3) Amplification Method Total RNA was amplified using the Ambion MessageAmp aRNA kit (#1750), an Eberwine based method, with slight modifications to the manufacturer's protocol (http://www.transnet.ca/Amplification%20Protocol.htm). 2.2.3. Sample labeling 1) Amount of nucleic acid labeled 6 micrograms of aRNA was labeled with Cy3/Cy5 for hybridization 2) Label used Cy5 & Cy3 3) Label incorporation method aRNA was labelled by reverse transcription using the standard protocol of the Ontario Cancer Institute with slight modifications (http://www.transnet.ca/Hybridization%20Protocol_2.htm) 2.2.5. Spiking controls 1) Spiking control feature OCI Human 19k3-part1 OCI Human 19k3-part2 2) Spike type and qualifier not applicable 3) Qualifier, value, source not applicable 2.3. Hybridization procedure and parameters 2) Hybridization Protocol Protocol - (http://www.transnet.ca/Hybridization%20Protocol_2.htm) Solution Hybridization buffer: DIG solution (Roche cat.#1603558) supplemented with 0.45 ug/ul yeast tRNA, 0.45 ug/ul salmon sperm DNA Blocking agent not applicable Slide blocking not applicable Probe blocking: 0.45 ug/ul yeast tRNA, 0.45 ug/ul salmon sperm DNA during hybridization Wash procedure 3x15 min with 1xSSC, 0.1% SDS at 50 degrees Celsius, 3 fast washes in 1xSSC at 50 degrees Celsius Quantity of labelled target used all material generated from 6 ug amplified RNA per sample Time 18 hr (overnight) hybridization Concentration 0.2 ug/ul of aRNA per hybridization Volume 60 ul Temperature 30 degrees Celsius Description of the hybridization chamber hybridizations were incubated in a microscope slide box stored within a tissue culture incubator Qualifier, value, source not applicable 2.4. Measurements 2.4.1. Raw data Scanning protocol Scanning hardware: Scanarray Express Microarray Scanner, Packard Biosciences Scanning software: Scanarray Express Microarray Analysis System v1.1 Build #9 Scan Parameters Laser power: see individual image files in section 2.4.1 Spatial resolution:10 microns PMT gain: 60% 2.4.2. Image analysis and quantitation Image analysis software: Quantarray v.3.0, Packard BioSciences Availability: No longer commercially available Quantitation Parameters: Array Pattern: Spot rows: 25 Spot columns: 24 Spot spacing (vertical): 170 microns Spot spacing (horizontal): 170 microns Interstitial spacing: Off Array rows: 8 Array columns: 4 Array spacing (vertical): 4500 microns Array spacing (horizontal): 4500 microns Auto-adjustable Grid: Grid elasticity: 50/100 Spot elasticity: 50/100 Quantitation: Method: Fixed Circle Crosstalk correction: No Normalized to brightest: No Background subtraction: No Gene database file: for OCI Human 19k3-part1- H19k3a.qa (attached) for OCI Human 19k3-part2- H19k3b.qa (attached) Fixed circle quantitation parameters: Spot diameter: 60 microns Inner background diameter: 180 microns Outer background diameter: 200 microns Signal low percentile: 45 Signal high percentile: 95 Background low percentile: 5 Background high percentile: 55 Quantification output: Mean Intensity Quality Criteria Confidence calculation: Minimum Tolerance and weight: None selected Replicates: Combine replicates: no 2.4.3. Normalized and summarized data Data processing protocol Background subtraction and normalization Background subtraction and normalization was performed using the Quantarray Normalization Excel macro on individual quantitation output files. Each channel was background subtracted using the equation: ch Bkg. Subtr. Intensity = ch intensity - ch Bkg. Normalization strategy: total array Normalization algorithm: log ratio median centering Median log ratio = median(log2(ch1 Bkg. Subtr. Intensity) - log2(ch2 Bkg. Subtr. Intensity)) Normalization factor = 2^ median log ratio Normalized ch2 intensity = ch2 intensity*Normalization factor Statistical Analysis The background subtracted normalized intensities were used for statistical analysis. All spot intensities for within-chip, repeat hybridization, fluor-flip hybridization and biological replicates for each time point post-infection were compiled and tested for significance testing using the software package ArrayStat v.1.0, Imaging Research. ArrayStat parameters: Model selection: Manual Model selected: Proportional model with offsets Outlier detection: yes Threshold detection: Manual Threshold detected: p<0.01 Minimum No. of replicates: 12 (for 1 & 5 wks PI), 11 (for 2 & 4 wks PI) Conditions: Dependent Random Error Estimation Method: Small Sample Test: t-test Normalization: By median Nominal alpha: 0.05 Multiple Test correction: False Discovery Rate Keywords = SHIV89.6P Keywords = HIV Keywords = nonhuman primate Keywords = cDNA microarray Keywords = time course Keywords: time-course
2004-11-03 | GSE1854 | GEO
Project description:Purpose: Examining the transcriptome of Bacteroides thetaiotaomicron VPI-5482 challenged with Bacteroides phage to assess surface molecule expression changes Methods: Bacteroides thetaiotaomicron was grown in BPRM in vitro or Germ-Free mice were monocolonized with Bacteroides thetaiotaomicron and gavaged with ARB25 phage. Fold change was calculated as live phage versus heat-killed phage treated samples with n=3 biological replicates. Once cells reached an optical density corresponding to mid-log phase growth (absorbance between 0.4-0.5), RNA was isolated and rRNA depleted. Samples were multiplexed for sequencing on the Illumina HiSeq platform at the University of Michigan Sequencing Core. Data was analyzed using Arraystar software (DNASTAR, Inc.) using DEseq2 normalization with default parameters. Genes with significant up- or down-regulation were determined by the following criteria: genes with an average fold-change >5-fold and with at least 2/3 biological replicates with a normalized expression level >1% of the overall average, and a p-value < 0.05 (t test with Benjamini-Hochberg correction) Results: Specific capsule expression was increased in wild-type B. thetaiotaomicron during phage infection in vitro and in vivo. Many corresponding in vivo genes were upregulated as well as other surface layer proteins.
2020-05-01 | GSE147071 | GEO
Project description:Purpose: Examining the transcriptome of Bacteroides thetaiotaomicron VPI-5482 grown on ribose to look at global changes in regulation in vitro on the defined monosaccharide, ribose as a sole carbon source. Methods: Bacteroides thetaiotaomicron was grown on 5 mg/ml ribose or glucose as a sole carbon source in vitro. Fold change was calculated as ribose over glucose with n=3 biological replicates. Once cells reached an optical density corresponding to mid-log phase growth (absorbance between 0.6-0.8), RNA was isolated and rRNA depleted. Samples were multiplexed for sequencing on the Illumina HiSeq platform at the University of Michigan Sequencing Core. Data was analyzed using Arraystar software (DNASTAR, Inc.) using RPKM normalization with default parameters. Genes with significant up- or down-regulation were determined by the following criteria: genes with an average fold-change >5-fold and with at least 2/3 biological replicates with a normalized expression level >1% of the overall average RPKM expression level in either glucose or ribose, and a p-value < 0.05 (t test with Benjamini-Hochberg correction) Results: We identified 81 genes differentially expressed at a 5-fold cutoff when grown in ribose over the reference glucose condition, many of which are involved in other metabolic processes important for seemingly unrealted nutrients.
2020-01-01 | GSE137571 | GEO
Project description:Pancreatic cancer is the fourth leading cancer-related death in United States. The clinical relevance of genomic imbalances in pancreatic cancer is uncertain. We performed array-comparative genomic hybridization (aCGH) in 44 resected pancreatic cancer specimens from a Korean cohort. We observed recurrent copy number gains were observed in chromosome 1q, 11q, 18q11.1-11.2, and 20q13.13; and losses in chromosome 2p, 9p, 10q, 14q, 15q, 18q12.2-23, 19q, 20q11.1, 21p, and 22q. High copy number gains, with log2 ratio >2.0, were observed in 4 cytobands, encoding genes such as G protein-coupled receptor 48, c-myc and gamma-catenin. By comparing the aCGH results of long survivors and short survivors, determined according to the median survival, we observed that loss of cytoband 18q22.3, encoding 5 genes, was the only alteration with significantly different frequencies. The copy number of the CPGL (Carboxypeptidase of glutamate-like) gene in cytoband 18q22.3 was found to be significantly associated with overall survival in univariate analyses (p=0.019 by log-rank test). This was subsequently assessed in the Italian cohort: 41 out of the 61 specimens carried deletion of the CPGL gene. Patients with deletion of the CPGL gene also had shorter overall survival (p=0.003 by log-rank test). Multivariate analysis of the two cohorts combined showed that loss of 18q22.3 / deletion of the CPGL gene was an independent poor prognostic factor for overall survival after adjusting for other factors which were found to impact the outcome (hazard ratio 2.72, p=0.0007). overexpression of the CPGL or its alternating splicing isoform CPGL-B in a pancreatic cancer cell lines resulted in slower proliferation of cells, G1 cell cycle arrest, and reduced migration activity. In conclusion, through aCGH analysis, we identified loss of 18q22.3 / deletion of the CPGL gene as potentially being an independent poor prognostic factor in resected pancreatic cancer. We further identified the CPGL gene as a potential tumor suppressor for pancreatic cancer cell lines.
2011-04-21 | GSE28732 | GEO
Project description:Yeast strain containing either a histone H4 wild type or K91A allele was grown in YPD to mid-log phase and harvested. Three replicates of each allele were performed Keywords: repeat sample
2005-05-24 | GSE2349 | GEO