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Infinium 450k array

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The Infinium 450k array is a genome-wide DNA methylation analysis platform developed by Illumina. It measures the methylation status of over 450,000 CpG sites across the human genome. The array provides a comprehensive and efficient way to interrogate the DNA methylome.

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Lab products found in correlation

Ez dna methylation kit, by Zymo Research (2 mentions) Genomestudio data analysis software, by Illumina (2 mentions) Qiaamp dna mini kit, by Qiagen (2 mentions) Infinium humanmethylation450 beadchip, by Illumina (2 mentions) Ez dna methylation gold kit, by Zymo Research (2 mentions) Snp 6.0 platform, by Thermo Fisher Scientific (1 mentions) Snp 6.0 array platform, by Thermo Fisher Scientific (1 mentions) Rnase a, by Qiagen (1 mentions) Phenol chloroform isoamyl alcohol, by Merck Group (1 mentions) Proteinase k, by Merck Group (1 mentions) Iscan beadchip scanner, by Illumina (1 mentions) Nanophotometer p300, by Implen (1 mentions) Qiaamp dna micro kit, by Qiagen (1 mentions) Chemagic dna blood kit, by PerkinElmer (1 mentions)

Spelling variants of this product

450K array (94 citations) Infinium HumanMethylation450 BeadChip (450K array) (33 citations) Infinium Human Methylation 450K array (22 citations) Infinium 450K (20 citations) Infinium arrays (12 citations) Infinium 450K Methylation array (10 citations) Infinium HumanMethylation450 BeadChip array (450k array) (4 citations) Infinium Illumina HumanMethylation 450k BeadChip arrays (3 citations) Infinium HumanMethylation450 (450k) array (3 citations) 450K Infinium array platform (2 citations)

16 protocols using infinium 450k array

1

Comprehensive DNA Methylation Profiling

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DNA methylation arrays were performed on both test and validation cohort tumour samples and additionally on 10 randomly selected normal samples from the test cohort. We used the Infinium 450k arrays (Illumina Inc., Cambridge, UK) following the manufacturer’s instructions and our previously published protocol (Alvi et al, 2015 (link)). Total 200 ng of DNA as quantified using Qubit Fluorometric Quantitation assay (Thermo Fisher Scientific Inc.) was used and arrays were scanned using iScan (Illumina Inc.).
Alvi M.A., Loughrey M.B., Dunne P., McQuaid S., Turkington R., Fuchs M.A., McGready C., Bingham V., Pang B., Moore W., Maxwell P., Lawler M., James J.A., Murray G.I., Wilson R.H, & Salto-Tellez M. (2017). Molecular profiling of signet ring cell colorectal cancer provides a strong rationale for genomic targeted and immune checkpoint inhibitor therapies. British Journal of Cancer, 117(2), 203-209.
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2

Integrative Molecular Profiling of Cancers

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Five molecular platforms, DNA copy number, DNA methylation, mRNA expression, miRNA expression and RPPA were provided as input to iCluster. Data were pre-processed using the following procedures. Copy number alteration data was derived from CBS segmented data from the Affymetrix SNP6.0 platform, and further reduced to a set of non-redundant regions as described (Mo et al., 2013 (link)). For the methylation data (Illumina Infinium 450k arrays), the median absolute deviation was employed to select the top 1000 most variable CpG sites after beta-mixture quantile normalization. Methylation probes with >20% or more missing data and those corresponding to SNP and autosomal chromosomes were removed. For mRNA and miRNA sequence data, lowly expressed genes were excluded based on median-normalized counts, and variance filtering led to 1266 mRNAs and 258 miRNAs for clustering. mRNA and miRNA expression features were log2 transformed, normalized and scaled before using as an input to iCluster.
Wheeler D.A, & Roberts L.R. (2017). Comprehensive and Integrative Genomic Characterization of Hepatocellular Carcinoma. Cell, 169(7), 1327-1341.e23.
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3

Multi-Omics Data Integration for Clustering

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As input to iCluster, we used four molecular platforms: DNA copy number, DNA methylation, mRNA expression, and miRNA expression. Data were pre-processed using the following procedures. Copy number alteration data was derived from CBS segmented data from the Affymetrix SNP6.0 array platform, and further reduced to a set of ~4000 non-redundant regions as described (Mo et al., 2013 (link)). For the methylation data (Illumina Infinium 450k arrays), the median absolute deviation was employed to select the top 4000 most variable CpG sites after beta-mixture quantile normalization (Pidsley et al., 2013 (link)). We removed methylation probes with >20% or more missing data and those corresponding to SNP and autosomal chromosomes. For mRNA and miRNA sequence data, we excluded genes with low expression (based on median-normalized counts). Variance filtering led to 4000 mRNAs and a variable number of miRNAs for clustering. mRNA and miRNA expression features were log2 transformed, normalized, and scaled before inputting to iCluster.
Abeshouse A., Adebamowo C., Adebamowo S.N., Akbani R., Akeredolu T., Ally A., Anderson M.L., Anur P., Appelbaum E.L., Armenia J., Auman J.T., Bailey M.H., Baker L., Balasundaram M., Balu S., Barthel F.P., Bartlett J., Baylin S.B., Behera M., Belyaev D., Bennett J., Benz C., Beroukhim R., Birrer M., Bocklage T., Bodenheimer T., Boice L., Bootwalla M.S., Bowen J., Bowlby R., Boyd J., Brohl A.S., Brooks D., Byers L., Carlsen R., Castro P., Chen H.W., Cherniack A.D., Chibon F., Chin L., Cho J., Chuah E., Chudamani S., Cibulskis C., Cooper L.A., Cope L., Cordes M.G., Crain D., Curley E., Danilova L., Dao F., Davis I.J., Davis L.E., Defreitas T., Delman K., Demchok J.A., Demetri G.D., Demicco E.G., Dhalla N., Diao L., Ding L., DiSaia P., Dottino P., Doyle L.A., Drill E., Dubina M., Eschbacher J., Fedosenko K., Felau I., Ferguson M.L., Frazer S., Fronick C.C., Fulidou V., Fulton L.A., Fulton R.S., Gabriel S.B., Gao J., Gao Q., Gardner J., Gastier-Foster J.M., Gay C.M., Gehlenborg N., Gerken M., Getz G., Godwin A.K., Godwin E.M., Gordienko E., Grilley-Olson J.E., Gutman D.A., Gutmann D.H., Hayes D.N., Hegde A.M., Heiman D.I., Heins Z., Helsel C., Hepperla A.J., Higgins K., Hoadley K.A., Hobensack S., Holt R.A., Hoon D.B., Hornick J.L., Hoyle A.P., Hu X., Huang M., Hutter C.M., Iacocca M., Ingram D.R., Ittmann M., Iype L., Jefferys S.R., Jones K.B., Jones C.D., Jones S.J., Kalir T., Karlan B.Y., Karseladze A., Kasaian K., Kim J., Kundra R., Kuo H., Ladanyi M., Lai P.H., Laird P.W., Larsson E., Lawrence M.S., Lazar A.J., Lee S., Lee D., Lehmann K.V., Leraas K.M., Lester J., Levine D.A., Li I., Lichtenberg T.M., Lin P., Liu J., Liu W., Liu E.M., Lolla L., Lu Y., Ma Y., Madan R., Maglinte D.T., Magliocco A., Maki R.G., Mallery D., Manikhas G., Mardis E.R., Mariamidze A., Marra M.A., Martignetti J.A., Martinez C., Mayo M., McLellan M.D., Meier S., Meng S., Meyerson M., Mieczkowski P.A., Miller C.A., Mills G.B., Moore R.A., Morris S., Mose L.E., Mozgovoy E., Mungall A.J., Mungall K., Nalisnik M., Naresh R., Newton Y., Noble M.S., Novak J.E., Ochoa A., Olvera N., Owonikoko T.K., Paklina O., Parfitt J., Parker J.S., Pastore A., Paulauskis J., Penny R., Pereira E., Perou C.M., Perou A.H., Pihl T., Pollock R.E., Potapova O., Radenbaugh A.J., Ramalingam S.S., Ramirez N.C., Rathmell W.K., Raut C.P., Riedel R.F., Reilly C., Reynolds S.M., Roach J., Robertson A.G., Roszik J., Rubin B.P., Sadeghi S., Saksena G., Salner A., Sanchez-Vega F., Sander C., Schein J.E., Schmidt H.K., Schultz N., Schumacher S.E., Sekhon H., Senbabaoglu Y., Setdikova G., Shelton C., Shelton T., Shen R., Shi Y., Shih J., Shmulevich I., Sica G.L., Simons J.V., Singer S., Sipahimalani P., Skelly T., Socci N., Sofia H.J., Soloway M.G., Spellman P., Sun Q., Swanson P., Tam A., Tan D., Tarnuzzer R., Thiessen N., Thompson E., Thorne L.B., Tong P., Torres K.E., van de Rijn M., Van Den Berg D.J., Van Tine B.A., Veluvolu U., Verhaak R., Voet D., Voronina O., Wan Y., Wang Z., Wang J., Weinstein J.N., Weisenberger D.J., Wilkerson M.D., Wilson R.K., Wise L., Wong T., Wong W., Wrangle J., Wu Y., Wyczalkowski M., Yang L., Yau C., Yellapantula V., Zenklusen J.C., Zhang J.(., Zhang H., Zhang H, & Zmuda E. (2017). COMPREHENSIVE AND INTEGRATED GENOMIC CHARACTERIZATION OF ADULT SOFT TISSUE SARCOMAS. Cell, 171(4), 950-965.e28.
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4

Multi-omics Data Pre-processing Protocol

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Each individual data type was pre-processed using the following procedure. Copy number alteration data were derived from the segmented data using the Circular Binary Segmentation algorithm41 (link), and further reduced to a set of non-redundant regions as described in Mo et al.42 (link). For the methylation data (Illumina Infinium 450k arrays), a beta-mixture quantile normalization43 (link) was applied to normalize the beta-value. Methylation probes with >20% or more missing data and those corresponding to SNP and autosomal chromosomes were removed. RNAseq version 2 was used. MapSplice44 (link) was used for sequence alignment and RSEM13 (link) for the quantitation of gene expression. For mRNA and miRNA sequence data, lowly-expressed genes were excluded based on median-normalized counts.
Zhu B., Song N., Shen R., Arora A., Machiela M.J., Song L., Landi M.T., Ghosh D., Chatterjee N., Baladandayuthapani V, & Zhao H. (2017). Integrating Clinical and Multiple Omics Data for Prognostic Assessment across Human Cancers. Scientific Reports, 7, 16954.
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5

Genome-wide DNA Methylation Analysis

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Approximately ~76 mg of MTG from each of 404 cases was treated with lysis buffer to decompose the tissue (100 mM Tris.HCl pH 8.5, 5mM EDTA, 0.2% SDS, 200 mM NaCl, 100 ug/ml Proteinase K (Sigma)) and incubated overnight at 55°C. A handheld pestle mixer (Kontes) was used periodically during incubation to further disassemble the tissue. The samples were treated with 4 uL of RNase A (Qiagen, 19101) at room temperature for 30 minutes. An equal volume of phenol/chloroform/isoamyl alcohol (Sigma, P3803) was added to each sample and samples were vortexed and subsequently rocked for five minutes. The samples were then centrifuged at room temperature for 10 minutes (10,000 RPM). The aqueous phase was collected and treated with ethanol overnight at −20°C. The precipitate was isolated and resuspended in 50 uL of TE buffer (pH 8.0) and stored at −20 °C. One microgram (1 ug) of extracted genomic DNA was treated with bisulfite. Bisulfite-treated DNA was then amplified using whole-genome amplification using a hexamer primer. Samples were randomly assorted among nine Illumina Infinium 450K arrays and annealed. The chips were read using the iScan System.
Brokaw D.L., Piras I.S., Mastroeni D., Weisenberger D.J., Nolz J., Delvaux E., Serrano G.E., Beach T.G., Huentelman M.J, & Coleman P.D. (2020). Cell Death and Survival Pathways in Alzheimer’s Disease: An Integrative Hypothesis Testing Approach Utilizing -Omic Datasets. Neurobiology of aging, 95, 15-25.
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6

Illumina 450k Array Bisulfite Protocol

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Genomic DNA (500 ng) was bisulfite converted using the EZ DNA Methylation kit (Zymo Research, cat# D5020). Converted DNA was hybridized onto the Illumina Infinium 450k array according to the manufacturer’s protocols. Signal intensities were measured using an Illumina iScan BeadChip scanner. Data was normalized using functional normalization in the Bioconductor package minfi using five principal components [30 (link), 31 (link)]. Probes that ambiguously mapped or had a high detection P value (>0.01), low bead count (<3 beads), and a low success rate (missing in >95% of the samples) were set to missing. All CpG probes were analyzed independently.
Mason A.G., Slieker R.C., Balog J., Lemmers R.J., Wong C.J., Yao Z., Lim J.W., Filippova G.N., Ne E., Tawil R., Heijmans B.T., Tapscott S.J, & van der Maarel S.M. (2017). SMCHD1 regulates a limited set of gene clusters on autosomal chromosomes. Skeletal Muscle, 7, 12.
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7

Illumina 450k DNA Methylation Profiling

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The study of the DNA methylation pattern of 485577 CpG sites was performed using Illumina® Infinium 450 k array and the IDAT files from the microarray were processed further using the R/Bioconductor package minfi [59 (link)]. In order to adjust for the different probe design types present in the 450 k architecture, red and green signals from the IDAT files were corrected using the SWAN algorithm [60 (link)]. No background correction or control probe normalization was applied. Probes where at least two samples had detection p values over 0.01 were filtered out. In accordance with Du et al. [61 (link)], both Beta values and M values were computed and employed across the analysis pipeline. M values were used for all the statistical analyses, assuming homoscedasticity, while Beta values were mostly used for the intuitive interpretation and visualization of the results.
Urdinguio R.G., Torró M.I., Bayón G.F., Álvarez-Pitti J., Fernández A.F., Redon P., Fraga M.F, & Lurbe E. (2016). Longitudinal study of DNA methylation during the first 5 years of life. Journal of Translational Medicine, 14, 160.
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8

Genome-wide DNA Methylation Analysis

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Genomic DNA was extracted from xenografts by using QIAamp DNA mini kit (QIAGEN, Valencia, CA). Sodium bisulfite conversion was performed by using the EZ DNA Methylation-Gold kit (Zymo Research, Orange, CA), according to the manufacturer's instructions. After bisulfite conversion, methylation of CpG sites was determined by Infinium HumanMethylation450 BeadChips (Illumina, San Diego, CA) following a procedure provided by Illumina, at the University of Chicago Genomics Core, Knapp Center for Biomedical Discovery (Chicago, IL). Data quality verification and levels of methylation of the 485,000 CpG sites included in the array were generated by the Illumina GenomeStudio Data Analysis Software. The Illumina Infinium 450k array was used to analyze DNA methylation in promoter site regions. The method measures the methylation levels over 482,000 CpG probes. The average percentage of methylation levels were expressed as β -values and ranged from 0 (completely unmethylated) to 1 (completely methylated). Data are deposited in GEO (NCBI# pending).
de Leon M., Cardenas H., Vieth E., Emerson R., Segar M., Liu Y., Nephew K, & Matei D. (2016). Transmembrane Protein 88 (TMEM88) Promoter Hypomethylation is Associated with Platinum Resistance in Ovarian Cancer. Gynecologic oncology, 142(3), 539-547.
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9

DNA Methylation Analysis of Nanomaterial Exposures

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Raw methylation data files from Illumina Infinium 450 k array were imported with minfi package (Aryee et al., 2014 (link)). Data was then normalized using Subset-quantile Within Array Normalization (SWAN) (Makismovic et al., 2012 (link)) and represented as M-values. CpG probes were then filtered by a) removing all CpG probes with a detection p-value higher than 0,01 in at least on sample, b) removing all probes containing single nucleotide polymorphisms at the interrogation or the elongation site and c) removing all probes belonging to the set of known cross-hybridizing probes of the employed platform (Chen et al., 2013 (link)).
Batch effects evaluation was performed by detecting the presence of surrogate variables with sva function from SVA package. After estimating these variables, their value was discretized into n_samples^(1/3) bins using the discretize function from infotheo package (Meyer et al., 2008 ) and corrected using ComBat method from the package SVA (Leek et al., 2012 (link)). Finally a limma model was employed in order to statistically estimate differences in average methylation levels between each nanomaterial exposure and the corresponding controls (Smyth, 2005 ).
Scala G., Kinaret P., Marwah V., Sund J., Fortino V, & Greco D. (2018). Multi-omics analysis of ten carbon nanomaterials effects highlights cell type specific patterns of molecular regulation and adaptation. Nanoimpact, 11, 99-108.
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10

Promoter Methylation and mRNA Expression

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DNA methylation levels determined using Illumina's Infinium 450 K array were downloaded as β-values. We chose a cut-off of 10% to distinguish between methylated and unmethylated loci, as previously published.(17 (link)) All promoter methylation probes located 1 kb upstream of the transcriptional start site were considered for each gene. For genes with multiple promoter methylation probes, analyses were carried out for probes least correlated with mRNA expression to minimize false positive results. mRNA expression was downloaded as RNA-Seq by Expectation Maximization values(18 (link)) from the cBioPortal freeware (http://www.cbioportal.org/public-portal.(19 (link),20 (link)) Only samples with matched DNA methylation and mRNA expression data were included in this analysis (n = 173).
Wong J.J., Lau K.A., Pinello N, & Rasko J.E. (2014). Epigenetic modifications of splicing factor genes in myelodysplastic syndromes and acute myeloid leukemia. Cancer Science, 105(11), 1457-1463.
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