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Platform Comparison for miRNA

ABSTRACT: The goal of this study was to investigate the generation and normalization of microRNA expression data based on microarray technologies to comparatively assess their quality. Two profiling platforms were compared: the single-channel Affymetrix GeneChip(r) and the Exiqon dual-channel miRCURY LNA(tm), which was processed as a single-channel array. Due to fundamental differences in the platform constitution, the normalization methods developed for gene expression need to be applied very cautiously to microRNA raw data. This motivated the development of a novel normalization method based on controllable assumptions, which uses the intensities of spike-in control probes. The results showed that the novel normalization method reduced the data variability in the most consistent way and confirmed the reliability of the differentially expressed microRNAs obtained, based on an RT-qPCR experiment performed for a subset of microRNAs. The conclusion was that the Exiqon platform combined with the novel spike-in controls based normalization method provides high-quality microRNA expression data suitable for reliable downstream analysis. This preprocessing pipeline was implemented into an R package called ExiMiR and deposited in the Bioconductor repository. The Exiqon data set has also been deposited in ArrayExpress under accession number E-MTAB-876.

INSTRUMENT(S): Affymetrix GeneChip Scanner 3000 7G

ORGANISM(S): Mus musculus

SUBMITTER: Sam Ansari

PROVIDER: E-MTAB-875 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Platform Comparison for miRNA

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Global ChIP-seq Normalization Reveals Epigenome Modulations

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.

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