450k methylation array

Manufactured by Illumina

Sourced in Germany

The 450K methylation array is a lab equipment product designed for genome-wide DNA methylation analysis. It provides a comprehensive platform for the interrogation of over 450,000 CpG sites across the human genome.

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Lab products found in correlation

Qiaamp dna mini kit, by Qiagen (2 mentions) Infinium humanmethylation450 450k beadchip, by Illumina (2 mentions) Infinium methylationepic 850k platform, by Illumina (2 mentions) Rneasy kit, by Qiagen (1 mentions) 450k array, by Illumina (1 mentions) Genejet pcr purification kit, by Thermo Fisher Scientific (1 mentions) G tube, by Covaris (1 mentions) Qubit 3.0 fluorometer, by Thermo Fisher Scientific (1 mentions) Qiamp dneasy tissue kit, by Qiagen (1 mentions) Ht 12 microarray, by Illumina (1 mentions) Picogreen dsdna quantitation assay, by Thermo Fisher Scientific (1 mentions) 5 aza 2 deoxycytidine, by Merck Group (1 mentions) Ez dna methylation gold kit, by Zymo Research (1 mentions) Dubeccos modified eagle medium, by Thermo Fisher Scientific (1 mentions) Nanodrop spectrophotometer, by Thermo Fisher Scientific (1 mentions) 7900ht fast real time pcr system, by Thermo Fisher Scientific (1 mentions) Allprep dna rna mini kit, by Qiagen (1 mentions) Transcriptor first strand cdna synthesis kit, by Roche (1 mentions) Itaq universal sybr green supermix, by Bio-Rad (1 mentions) Spin column, by Qiagen (1 mentions)

Close spelling variants of this product

450K array (94 citations) Human Methylation 450K array (33 citations) Infinium Human Methylation 450K array (22 citations) Human Methylation 450K Bead Chip Array (12 citations) Infinium 450K Methylation array (10 citations) 450K DNA methylation array (10 citations) 450K methylation array data (6 citations) Illumina methylation arrays (2 citations) In nium Human Methylation 450k BeadChip array (2 citations) Human Methylation 450K array platform (2 citations)

44 protocols using 450k methylation array

Illumina 450K Methylation Data Processing

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For Illumina 450K methylation arrays IDAT files were processed using the R package minfi [39 ]. Quantile normalisation was performed on the intensity values of the red and green channels of the type I and type II probes separately. Probes with a detection P value <0.01 or those mapping to more than one location (with 90 % similarity) or to the X or Y chromosome were removed from further analysis, leaving 401,915 probes (experiment 1) and 431,909 probes (experiment 2). The X chromosome was removed to allow for consistent analysis across a large number of datasets. Intensities were combined into a single beta value. We also performed the analysis and called senDMPs only removing the Y chromosome and those DMPs found to be significantly changed with >0.3 beta value difference and located on chrX are included in Additional file 9: Table S4. For IlluminaHT12 RNA arrays, the samples were processed using the lumi package. Arrays were first transformed using the Variance Stabilizing Transform and then normalised using robust spline normalisation. All further analysis was performed on the normalised transformed data.

Lowe R., Overhoff M.G., Ramagopalan S.V., Garbe J.C., Koh J., Stampfer M.R., Beach D.H., Rakyan V.K, & Bishop C.L. (2015). The senescent methylome and its relationship with cancer, ageing and germline genetic variation in humans. Genome Biology, 16(1), 194.

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FKBP5 DNA Methylation Profiling

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DNA methylation levels in both studies were analyzed using the HAM-TBS approach, comprising an optimized PCR panel of 28 amplicons in the FKBP5 locus [25 (link)]. These data were complemented by Illumina 450K methylation arrays. An overview of the methylation data obtained can be found in Additional file 6: Table S1.

Wiechmann T., Röh S., Sauer S., Czamara D., Arloth J., Ködel M., Beintner M., Knop L., Menke A., Binder E.B, & Provençal N. (2019). Identification of dynamic glucocorticoid-induced methylation changes at the FKBP5 locus. Clinical Epigenetics, 11, 83.

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Methylation-based Cancer Subtyping Protocol

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Sequenom primers were designed to three highly methylated genes in group A (CIMP⁺), PKP1, CRIP1, CYP26C1, as selected by CpG coverage and PCR efficiency. PCR amplification was performed in a training data set consisting of the samples, which were analysed by Illumina 450K methylation arrays. These three methylated genes were used to train a classification model using the Prediction Analysis for Microarrays algorithm. Class prediction was performed on a non-overlapping cohort of 82 samples collected from The Hospital for Sick Children, Children’s Hospital Boston, University of Michigan, and the MD Anderson Cancer Center. Posterior probabilities corresponding to Group A (CIMP⁺) or Group B (CIMP⁻) were calculated for each sample, and an odds ratio >2-fold (probability group A/probability group B) for either subgroup was used to classify tumours. Survival was graphed throughout the manuscript using Kaplan–Meier curves and assessed statistically using a log-rank test.

Mack S.C., Witt H., Piro R.M., Gu L., Zuyderduyn S., Stütz A.M., Wang X., Gallo M., Garzia L., Zayne K., Zhang X., Ramaswamy V., Jäger N., Jones D.T., Sill M., Pugh T.J., Ryzhova M., Wani K.M., Shih D.J., Head R., Remke M., Bailey S.D., Zichner T., Faria C.C., Barszczyk M., Stark S., Seker-Cin H., Hutter S., Johann P., Bender S., Hovestadt V., Tzaridis T., Dubuc A.M., Northcott P.A., Peacock J., Bertrand K.C., Agnihotri S., Cavalli F.M., Clarke I., Nethery-Brokx K., Creasy C.L., Verma S.K., Koster J., Wu X., Yao Y., Milde T., Sin-Chan P., Zuccaro J., Lau L., Pereira S., Castelo-Branco P., Hirst M., Marra M.A., Roberts S.S., Fults D., Massimi L., Cho Y.J., Van Meter T., Grajkowska W., Lach B., Kulozik A.E., von Deimling A., Witt O., Scherer S.W., Fan X., Muraszko K.M., Kool M., Pomeroy S.L., Gupta N., Phillips J., Huang A., Tabori U., Hawkins C., Malkin D., Kongkham P.N., Weiss W.A., Jabado N., Rutka J.T., Bouffet E., Korbel J.O., Lupien M., Aldape K.D., Bader G.D., Eils R., Lichter P., Dirks P.B., Pfister S.M., Korshunov A, & Taylor M.D. (2014). Epigenomic alterations define lethal CIMP-positive ependymomas of infancy. Nature, 506(7489), 445-450.

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Standardized FASN DNA Methylation Analysis

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Methylation pipelines were conducted according to our previously published study (21 (link)). Briefly, for JHU and NCI (GSE183019) cohorts, raw Infinium EPIC array methylation data were processed and normalized via SWAN (Subset quantile Within-Array Normalization) method using minfi in R. Individual beta values for CpGs were then obtained for all 850,000 methylation sites. DNA methylation beta values are continuous variables between 0 and 1, representing the proportion of methylation for a given CpG site, calculated as the ratio of the intensity of the methylated bead type to the combined methylated and unmethylated bead intensity for the specific probe. Overall annotation for CpGs was used to select all 56 probes located up to 1,500 bp upstream or within the FASN gene body. Mean beta value for FASN was obtained by calculating the average beta for all 56 probes per sample. For TCGA and NCI cohorts, mean FASN beta values were obtained by calculating the average beta across 55 and 56 probes, respectively, as TCGA methylation data was obtained from Illumina 450k methylation arrays containing only 55 probes.

Dairo O., DePaula Oliveira L., Schaffer E., Vidotto T., Mendes A.A., Lu J., Huynh S.V., Hicks J., Sowalsky A.G., De Marzo A.M., Joshu C.E., Hanratty B., Sfanos K.S., Isaacs W.B., Haffner M.C, & Lotan T.L. (2024). FASN Gene Methylation is Associated with Fatty Acid Synthase Expression and Clinical-genomic Features of Prostate Cancer. Cancer Research Communications, 4(1), 152-163.

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Whole Blood DNA Methylation Profiling

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Cellular material and clot was removed from whole blood using centrifugation. DNA was then extracted using the Qiagen RNeasy kit from serum samples collected from 20 controls and 20 patients admitted for acute mania and similar in the demographic variable of age, sex and race. Five hundred nanogram of DNA was then bisulfite converted and hybridized to Illumina 450K Methylation Arrays using the manufacturers recommended protocol at the Johns Hopkins CIDR core laboratory.

Sabunciyan S., Maher B., Bahn S., Dickerson F, & Yolken R.H. (2015). Association of DNA Methylation with Acute Mania and Inflammatory Markers. PLoS ONE, 10(7), e0132001.

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Methylation QTL Analysis of Myeloma

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Methylation quantitative trait locus analyses of adjacent genes to SNPs of interest using probe-level DNA methylation data generated using Illumina 450 K methylation arrays on plasma cells from 365 MRC myeloma XI trial patients (UK-My11). Association between SNP genotype and normalized methylation levels was tested by linear regression.

Johnson D.C., Weinhold N., Mitchell J.S., Chen B., Kaiser M., Begum D.B., Hillengass J., Bertsch U., Gregory W.A., Cairns D., Jackson G.H., Försti A., Nickel J., Hoffmann P., Nöethen M.M., Stephens O.W., Barlogie B., Davis F.E., Hemminki K., Goldschmidt H., Houlston R.S, & Morgan G.J. (2016). Genome-wide association study identifies variation at 6q25.1 associated with survival in multiple myeloma. Nature Communications, 7, 10290.

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Prostate Cancer Molecular Profiling

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The present study includes PC methylation profiling data (Illumina 450k methylation arrays) and RNA-seq data from TCGA project (TCGA-PRAD) [24 ]. The cohort included PC patients belonging to the Caucasian population. The patients were not receiving neoadjuvant therapy. The cohort (n = 358) was divided into two PC groups, high (n = 251) and intermediate (n = 107) risk, according to the classification of D’Amico (Table 1) [2 (link)]. High-risk group (n = 251) was divided into favorable (n = 83) and unfavorable (n = 21) prognoses groups based on biochemical recurrence (postoperative PSA ≥ 0.2 ng/ml), and TMPRSS2-ERG-positive (n = 75) and TMPRSS2-ERG-negative (n = 79) groups.

Kobelyatskaya A., Pudova E., Fedorova M., Nyushko K., Alekseev B., Kaprin A., Trofimov D., Sukhikh G., Snezhkina A., Krasnov G., Razin S, & Kudryavtseva A. (2020). Differentially methylated CpG sites associated with the high-risk group of prostate cancer. Journal of Integrative Bioinformatics, 17(4), 20200031.

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Quantifying Methylation in MS Hippocampus

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Genomic DNA was isolated from frozen tissue sections corresponding to regions of myelinated (n = 8) or demyelinated MS hippocampus (n = 7). Genomic DNA was isolated using a QIAamp DNA mini kit (Qiagen Inc, USA) following the manufacturer’s instructions. Purified genomic DNA was processed for bisulfide conversion and subsequent methylation assays. DNA samples were delivered to the Case Western Reserve University Genomics Core Facility, where 1.5 µg of DNA was bisulphite-treated (Zymo EZ DNA methylation gold) per manufacturer’s instructions. Genome-wide methylation profiles were generated using Illumina 450 K methylation arrays. Isolated DNA (100 ng) was used to measure DNA hydroxymethylation with MethylFlash™ Global DNA Hydroxymethylation (5hmC) ELISAs (Epigentek Inc, USA) using a 5hmC mAb-based detection complex.

Chomyk A.M., Volsko C., Tripathi A., Deckard S.A., Trapp B.D., Fox R.J, & Dutta R. (2017). DNA methylation in demyelinated multiple sclerosis hippocampus. Scientific Reports, 7, 8696.

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Genome-wide DNA Methylation Profiling of Psoriasis

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Genomic DNA was isolated from the peripheral blood mononuclear cells (PBMC) of psoriasis patients and PBMC of healthy volunteers. Genomic DNA was isolated using a QIAamp^® DNA mini kit (Qiagen, Hilden, Germany) following the manufacturer’s instructions. Purified genomic DNA was processed for bisulfide conversion and subsequent methylation assays. DNA samples were delivered to Macrogen Inc., Korea. Genome-wide methylation profiles were generated using Illumina 450 K methylation arrays. Isolated DNA (100 ng) was employed to measure DNA methylation. All bioinformatics analyses were performed in Macrogen Inc., Korea.

Um J.Y., Chung B.Y., Kim H.B., Kim J.C., Park C.W, & Kim H.O. (2021). Aryl Hydrocarbon Receptor Repressor Is Hypomethylated in Psoriasis and Promotes Psoriasis-like Inflammation in HaCaT Cells. International Journal of Molecular Sciences, 22(23), 12715.

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Copy Number Variation Analysis of DNA Methylation Data

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CNV analysis was performed using the conumee R package on raw methylation data (IDAT files) from the Illumina 450k methylation arrays to confirm that differentially methylated regions or positions were not copy number driven. [http://bioconductor.org/packages/conumee/] Conumee requires control data for analysis. Therefore, we downloaded a publicly available dataset that measured DNA methylation using Illumina’s 450K array in non-demented control brain tissue of the cerebellum (n=179, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE134379). Conumee normalizes the combined intensity values of the methylated and unmethylated probes of each CpG site using these controls (representing genomes with no copy number alterations). Surrounding probes are then combined to create bins of a minimum size and probe number (default values and conumee exclude_regions data were used) prior to segmentation into clusters of the same state of variation in the number of copies via the circular binary segmentation algorithm. Segment tables were created for all samples and segment means for male versus female samples within subgroups at each DMP/gene/chromosome region of interest were calculated using pairwise Wilcoxon Rank Sum test.

Moss R.M., Sorajja N., Mills L.J., Moertel C.L., Hoang T.T., Spector L.G., Largaespada D.A, & Williams L.A. (2023). Sex differences in methylation profiles are apparent in medulloblastoma, particularly among SHH tumors. Frontiers in Oncology, 13, 1113121.

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