DNA methylation profiling for 850k reannotation and normalization evaluation [450k]
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PROVIDER: PRJNA816180 | ENA |
REPOSITORIES: ENA
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Project description:DNA methylation profiling for 850k reannotation and normalization evaluation [850k]
| PRJNA816179 | ENA
Project description:Illumina Infinium DNA Methylation (5mC) profiling arrays are a popular technology to measure genome-scale distribution of 5mC at low cost and high throughput, especially in cancer and other complex diseases. Following the success of the HumanMethylation450 array (450k), Illumina released the MethylationEPIC array (850k) featuring increased coverage of enhancers in addition to regulatory regions primarily covered by the 450k (i.e. promoters, gene bodies). Despite its widespread use, the analysis of 850k data remains suboptimal as it mostly still relies on Illumina’s default annotation, which underestimates enhancers and long noncoding RNAs (lncRNAs). We thus developed an approach, based on ENCODE and LNCipedia databases, that greatly improves Illumina’s default annotation of enhancers and long noncoding transcripts. Comparisons between the re-annotated 850k and its precursor, the 450k, or RRBS, another high-throughput 5mC profiling technology, revealed that the 850k covers at least three times more enhancers and lncRNAs than the other two technologies. We further investigated the reproducibility of the three technologies and applied various normalisation methods to 850k data, showing that most of them reduce variability to a level below that of RRBS. When analyzed with our new annotation and normalization pipeline, profiling for 5mC changes in breast cancer biopsies with the 850k highlighted aberrant enhancers methylation as the predominant feature, confirming previous reports. In conclusion, our study provides an updated analysis pipeline for 850k data based on a refined probe annotation and normalization that allows for the improved analysis of methylation at enhancers and long noncoding transcripts.
2022-11-11 | GSE198626 | GEO
Project description:DNA methylation profiling for 850k reannotation and normalization evaluation
| PRJNA816178 | ENA
Project description:Illumina Infinium DNA Methylation (5mC) profiling arrays are a popular technology to measure genome-scale distribution of 5mC at low cost and high throughput, especially in cancer and other complex diseases. Following the success of the HumanMethylation450 array (450k), Illumina released the MethylationEPIC array (850k) featuring increased coverage of enhancers in addition to regulatory regions primarily covered by the 450k (i.e. promoters, gene bodies). Despite its widespread use, the analysis of 850k data remains suboptimal as it mostly still relies on Illumina’s default annotation, which underestimates enhancers and long noncoding RNAs (lncRNAs). We thus developed an approach, based on ENCODE and LNCipedia databases, that greatly improves Illumina’s default annotation of enhancers and long noncoding transcripts. Comparisons between the re-annotated 850k and its precursor, the 450k, or RRBS, another high-throughput 5mC profiling technology, revealed that the 850k covers at least three times more enhancers and lncRNAs than the other two technologies. We further investigated the reproducibility of the three technologies and applied various normalisation methods to 850k data, showing that most of them reduce variability to a level below that of RRBS. When analyzed with our new annotation and normalization pipeline, profiling for 5mC changes in breast cancer biopsies with the 850k highlighted aberrant enhancers methylation as the predominant feature, confirming previous reports. In conclusion, our study provides an updated analysis pipeline for 850k data based on a refined probe annotation and normalization that allows for the improved analysis of methylation at enhancers and long noncoding transcripts.
2022-11-11 | GSE198627 | GEO
Project description:This SuperSeries is composed of the SubSeries listed below.
2022-11-11 | GSE198628 | GEO
Project description:Abstract The proper identification of differentially methylated CpGs is central in most epigenetic studies. The Illumina Human Methylation 450k BeadChip is widely used to quantify DNA methylation, nevertheless the design of an appropriate analysis pipeline faces severe challenges due to the convolution of biological and technical variability and the presence of a signal bias between Infinium I and II probe design types. Despite recent attempts to investigate how to analyze DNA methylation data with such an array design, it has not been possible to perform a comprehensive comparison between different bioinformatics pipelines due to the lack of appropriate datasets having both large sample size and sufficient number of technical replicates. Here we perform such a comparative analysis, targeting the problems of reducing the technical variability, eliminating the probe design bias and reducing the batch effect by exploiting two unpublished datasets, which included technical replicates and were profiled for DNA methylation either on peripheral blood, monocytes or muscle biopsies. The blood samples included individuals with Multiple Sclerosis (MS). We evaluated the performance of different analysis pipelines and demonstrated that a) it is critical to correct for the probe design type, since the amplitude of the measured methylation change depends on the underlying chemistry; b) the effect of different normalization schemes is mixed, and the most effective method in our hands were quantile normalization and Beta Mixture Quantile dilation (BMIQ); c) it is beneficial to correct for batch effects. In conclusion, our comparative analysis using a comprehensive dataset suggests an efficient pipeline for proper identification of differentially methylated CpGs using the Illumina 450k arrays.
2013-03-14 | GSE43976 | GEO
Project description:The Illumina Infinium HumanMethylation450 BeadChip is frequently used in epigenetic research. Besides quantile normalization there is currently no standard method to normalize the data between arrays. We describe some properties of the data generated by this platform and present a normalization method based on local regression. We compare the performance of this method with other commonly used approaches in three benchmarks (correlation between 21 pairs of technical replicates, detection of differential methylation and correlation of methylation levels for smoking-associated CpG sites with smoking behavior of 655 participants of an epidemiological study). Results indicate that the proposed method improves reproducibility, whereas some commonly used methods can have adverse effects.
| S-EPMC4354407 | biostudies-literature
Project description:Abstract The proper identification of differentially methylated CpGs is central in most epigenetic studies. The Illumina Human Methylation 450k BeadChip is widely used to quantify DNA methylation, nevertheless the design of an appropriate analysis pipeline faces severe challenges due to the convolution of biological and technical variability and the presence of a signal bias between Infinium I and II probe design types. Despite recent attempts to investigate how to analyze DNA methylation data with such an array design, it has not been possible to perform a comprehensive comparison between different bioinformatics pipelines due to the lack of appropriate datasets having both large sample size and sufficient number of technical replicates. Here we perform such a comparative analysis, targeting the problems of reducing the technical variability, eliminating the probe design bias and reducing the batch effect by exploiting two unpublished datasets, which included technical replicates and were profiled for DNA methylation either on peripheral blood, monocytes or muscle biopsies. We evaluated the performance of different analysis pipelines and demonstrated that a) it is critical to correct for the probe design type, since the amplitude of the measured methylation change depends on the underlying chemistry; b) the effect of different normalization schemes is mixed, and the most effective method in our hands were quantile normalization and Beta Mixture Quantile dilation (BMIQ); c) it is beneficial to correct for batch effects. In conclusion, our comparative analysis using a comprehensive dataset suggests an efficient pipeline for proper identification of differentially methylated CpGs using the Illumina 450k arrays. DNA samples from peripheral blood or CD14+ monocytes were included in the study. DNA methylation levels were profiled using Illumina 450K arrays. Specifically, 50 biological sample replicates from PB and 36 biological sample replicates from monocytes were randomly assigned to 8 BeadChips with technical replicates and processed in one run (a total of 96 DNA samples). Eight samples were technically replicated in pairs, while one sample was represented in a trio of replicates. Different analysis pipelines were compared, however, the file uploaded refers to the best scored. In our publication we used this one to make all analyses and conclusions.
2013-03-14 | E-GEOD-43976 | biostudies-arrayexpress
Project description:Compare a single sample run with two different technologies: Illumina 450k methylation array and MIRA on NimbleGen
2013-06-08 | GSE42707 | GEO
Project description:Background: As the most stable and experimentally accessible epigenetic mark, DNA methylation is of great interest to the research community. The landscape of DNA methylation across tissues, through development and in disease pathogenesis is not yet well characterised. Thus there is a need for rapid and cost effective methods for assessing genome-wide levels of DNA methylation. The Illumina Infinium HumanMethylation450 (450K) BeadChip is a very useful addition to the available methods but its complex design, incorporating two different assay methods, requires careful consideration. Accordingly, several normalization schemes have been published. We have taken advantage of known DNA methylation patterns associated with genomic imprinting and X-chromosome inactivation (XCI), in addition to the performance of SNP genotyping assays present on the array, to derive three independent metrics which we use to test alternative schemes of correction and normalization. These metrics also have potential utility as quality scores for datasets. Results: The standard index of DNA methylation at any specific CpG site is β = M/(M + U + 100) where M and U are methylated and unmethylated signal intensities. Betas calculated from raw signal intensities (the default GenomeStudio behaviour) perform well, but using 11 methylomic datasets we demonstrate that quantile normalization methods produce marked improvement, even in highly consistent data, by all three metrics. The commonly used procedure of normalizing betas is inferior to the separate normalization of M and U, and it is also advantageous to normalize Type I and Type II assays separately. More elaborate manipulation of quantiles proves to be counterproductive. Conclusions: Careful selection of preprocessing steps can minimise variance and thus improve statistical power, especially for the detection of the small absolute DNA methylation changes likely associated with complex disease phenotypes. For the convenience of the research community we have created an R software package called wateRmelon, compatible with the existing methylumi, minfi and IMA packages, that allows others to utilise the same normalization methods and data quality tests on 450K data.
2013-01-24 | GSE43414 | GEO