Global ChIP-seq Normalization Reveals Epigenome Modulations
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ABSTRACT:
ORGANISM(S): Homo sapiens Drosophila melanogaster
PROVIDER: GSE60104 | GEO | 2014/10/30
SECONDARY ACCESSION(S): PRJNA257491
REPOSITORIES: GEO
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Project description:Epigenomic profiling by ChIP-seq is a prevailing methodology used to investigate chromatin-based regulation in biological systems, such as human disease, yet the lack of an empirical methodology to normalize amongst experiments has limited the usefulness of this technique. Here we describe a “spike-in” normalization method that allows the quantitative comparison of histone modification status across cell populations using defined quantities of a reference epigenome. We demonstrate the utility of this method in measuring epigenomic changes following chemical perturbations and show how control normalization of ChIP-seq experiments enables discovery of disease-relevant changes in histone modification occupancy. ChIP-Seq of histone modifications H3K79me2 and H3K4me3 in human samples treated with EPZ-5676 with/without reference epigenome spike-in.
2014-10-30 | E-GEOD-60104 | biostudies-arrayexpress
Project description:Global ChIP-seq Normalization Reveals Epigenome Modulations
| PRJNA257491 | ENA
Project description:The technological advances in mass spectrometry allow us to collect more comprehensive data with higher quality and increasing speed. With the rapidly increasing amount of data generated, the need for streamlining analyses becomes more apparent. Proteomic data is known to be often affected by systemic bias from unknown sources, and failing to adequately normalize the data can lead to erroneous conclusions. To allow researchers to easily evaluate and compare different normalization methods via a user-friendly interface, we have developed “proteiNorm”. The current implementation of proteiNorm accommodates preliminary filter on peptide and sample level, followed by an evaluation of several popular normalization methods and visualization of missing value. The user then selects an adequate normalization method and one of several imputation methods used for the subsequent comparison of different differential abundance/expression methods and estimation of statistical power. The application of proteiNorm and interpretation of its results is demonstrated on a Tandem Mass Tag mass spectrometry example data set, where the proteome of three different breast cancer cell lines was profiled with and without hydroxyurea treatment. With proteiNorm, we provide a user-friendly tool to identify an adequate normalization method and to select an appropriate method for a differential abundance/expression analysis.
2021-09-09 | PXD018152 | Pride
Project description:<p><strong>INTRODUCTION:</strong> Because of its ease of collection, urine is one of the most commonly used matrices for metabolomics studies. However, unlike other biofluids, urine exhibits tremendous variability that can introduce confounding inconsistency during result interpretation. Despite many existing techniques to normalize urine samples, there is still no consensus on either which method is most appropriate or how to evaluate these methods. </p><p><strong>OBJECTIVES:</strong> To investigate the impact of several methods and combinations of methods conventionally used in urine metabolomics on the statistical discrimination of two groups in a simple metabolomics study. </p><p><strong>METHODS:</strong> We applied 14 different strategies of normalization to 40 urine samples analysed by liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS). To evaluate the impact of these different strategies, we relied on the ability of each method to reduce confounding variability while retaining variability of interest, as well as the predictability of statistical models. </p><p><strong>RESULTS:</strong> Among all tested normalization methods, osmolality-based normalization gave the best results. Moreover, we demonstrated that normalization using a specific dilution prior to the analysis outperformed post-acquisition normalization. We also demonstrated that the combination of various normalization methods does not necessarily improve statistical discrimination.</p><p><strong>CONCLUSIONS:</strong> This study re-emphasized the importance of normalizing urine samples for metabolomics studies. In addition, it appeared that the choice of method had a significant impact on result quality. Consequently, we suggest osmolality-based normalization as the best method for normalizing urine samples.</p>
2023-09-04 | MTBLS1765 | MetaboLights
Project description:Chromatin immunoprecipitation (ChIP) is an antibody-based approach that is most frequently utilized in research areas of chromatin biology and epigenetics. The ENCODE Consortium provided guidelines about antibody but the fundamental aspect regarding antibody titer was not addressed. Here, we introduced a simple and quick DNA-based quantification of chromatin input, allowing to normalize the antibody amount in individual ChIP reaction to the optimal titer. We further demonstrated the titration-based normalization of antibody amount generates improved assay outcome with high consistency among samples in an experiment or recurrent experiments for a broad range of chromatin input amount. Chromatin immunoprecipitation-sequencing (ChIP-seq) was performed for histone H3K27ac mark in the conditions of various titer of antibody in ChIP reactions..
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Project description:Profiling miRNA levels in cells with miRNA-microarrays is becoming a widely used technique. Although normalization methods for mRNA gene expression arrays are well established, miRNA array normalization has so far not been investigated in detail. In this study we investigate the impact of normalization on data generated with the Agilent miRNA array platform. Here, we developed a method to select non-changing miRNAs (“invariants”) and used them to compute linear regression normalization coefficients or Variance Stabilizing Normalization (VSN) parameters. We compared the invariant normalizations to normalization by, scaling, quantile and VSN with default parameters as well as to no normalization using samples with strong differential expression of miRNAs (heart-brain comparison) and samples where only few miRNAs are affected (p53 overexpression in SCC13 cells versus GFP vector control transfected cells). All normalization methods performed better than no normalization. Normalizations procedures based on the set of invariants and quantile were the most robust over all experimental conditions tested. Our method of invariant selection and normalization is not limited to Agilent miRNA arrays and can be applied to other datasets from one color miRNA microarray platforms, focused gene expression arrays and gene expression analysis using quantitative PCR. Keywords: miRNA profiling
2009-02-17 | GSE12085 | GEO
Project description:Tumor vasculature are structurally chaotic and functionally inefficient. Restoring aberrant tumor blood vessels, or “normalize” the tumor vasculature can alleviate hypoxia and enhance intra-tumoral drug delivery. However, identifying tumor vascular normalizing drugs are currently hampered by an absence of efficient screening platform. We aimed to develop a robust method to visualize, digitalize and evaluate the structural and functional changes of tumor vasculature in batch: zebrafish functional xenograft vasculature platform (zFXVP). As proof of principle, we applied zFXVP to a small compound library which has been implicated in affecting the morphology of tumor vasculature. zFXVP identified PF-502 as a durable tumor vascular normalization agent. Further molecular analysis using RNA-Seq, pharmaceutical inhibition and gene knockout indicate PF-502 can induce endothelial cell cycle arrest and simplify redundant tumor vasculature by blocking PI3K/mTOR signaling and stabilize the vessel lumen and stimulate the blood function by activating Notch1 signaling with mediating S3 cleavage. Last, in mouse tumor xenograft model, PF-502 reproduced a potent vascular normalizing effect at a sub MTD (maximum tolerated dose) and showed its ability to improve intra-tumoral drug delivery and synergize chemotherapy, which confirms the reliability of zFXVP and implicates the potential application of PF-502 in clinical practice as an adjuvant vascular normalization drug.
2023-09-13 | GSE224106 | GEO
Project description:Supernatant analysis from pY enrichment. Supernatent used to normalize TMT-10plex data.
2018-11-29 | PXD008940 | Pride
Project description:Label-free quantification is a powerful method for studying cellular protein phosphorylation dynamics. However, whether current data normalization methods achieve sufficient accuracy has not been examined systematically. Here, we demonstrate that a large uni-directional shift in the phosphopeptide abundance distribution is problematic for global median centering and quantile-based normalizations and may mislead the biological conclusion from unlabeled phosphoproteome data. Instead, we present a novel normalization strategy, named pairwise normalization, which is based on adjusting phosphopeptide abundances measured before and after the enrichment. The superior performance of pairwise normalization was validated by statistical methods, western blotting analysis, and by bioinformatics analysis. In addition, we demonstrate that the choice of normalization method influences the downstream analyses of the data and perceived pathway activities. Furthermore, we demonstrate that the developed normalization method, combined with pathway analysis algorithms, revealed a novel biological synergism between Ras signalling and PP2A inhibition by CIP2A.
2015-08-24 | PXD001374 | Pride
Project description:Background Array-based comparative genome hybridization (aCGH) is commonly used to determine the genomic content of bacterial strains. In aCGH data, systematic errors are comparable to those occurring in transcriptome data. However, especially for microbes, an additional source of variation exists: differences in hybridization due to gene sequence divergence between the strains hybridized. Current normalization methods do not take this source of variation into consideration. Results We present Supervised Lowess, or S-Lowess, an application of the subset Lowess normalization method that does take difference in genomic content into account. The performance of S-Lowess was assessed on aCGH experiments in which differentially labeled genomic DNA fragments of Lactococcus lactis IL1403 and L. lactis MG1363 strains were hybridized to IL1403 microarrays. Since both genome sequences are known (they have only on average 85 % sequence identity), the success rate in detecting deletions in the MG1363 genome of different aCGH normalization methods can be compared. S-Lowess detects 97% of the deletions, whereas other aCGH normalization methods detect up to only 60% of the deletions. Conclusions S-Lowess removes systematic errors from dual-dye aCGH data in two steps: (i) determination of likely homologous genes (LHG); and (ii) estimation of correction factors for systematic errors from spots of LHG and subset Lowess normalization of the remaining spots using these correction factors. It is implemented in a user-friendly web-tool accessible from http://bioinformatics.biol.rug.nl/websoftware/s-lowess. We demonstrate that it outperforms existing normalization methods and maximizes the number of detectable genomic deletions or duplications from microbial aCGH data. Keywords: comparative genome hybridisation
2008-07-20 | GSE9039 | GEO