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dna methylation microarray  (Illumina Inc)


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    Illumina Inc dna methylation microarray
    Dna Methylation Microarray, supplied by Illumina Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/dna+methylation+microarray/dna+methylation+arrays/pm40644431-2-15-14
    Average 90 stars, based on 1 article reviews
    dna methylation microarray - by Bioz Stars, 2026-09
    90/100 stars

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    Related Articles

    DNA Methylation Assay:

    Article Title: Robust molecular subgrouping and reference-free aneuploidy detection in medulloblastoma using low-depth whole genome bisulfite sequencing.
    Article Snippet: .. This classifier relies on data arising from Illumina DNA methylation microarray technology, a cost-effective assay whose current release interrogates ~ 935,000 CpGs across the human genome and is compatible with both fresh-frozen and FFPE DNA derivatives [15]. ..

    Article Title: Ultra-low-input cell-free DNA sequencing for tumor detection and characterization in a real-world pediatric brain tumor cohort.
    Article Snippet: .. Hovestadt VZM (2017) conumee: Enhanced copy-number variation analysis using Illumina DNA methylation arrays. h t t p s : / / b i o c o n d u c t o r . o r g / p a c k a g e s / r e l e a s e / b i o c / h t m l / c o n u m e e . h t m l Accessed. ..

    Article Title: Measuring technical variability in illumina DNA methylation microarrays.
    Article Snippet: .. In this study, we measure the impact of common sources of technical variability in Illumina DNA methylation microarray data, with a specific focus on positional biases inherent within the microarray technology. ..

    Article Title: Robust molecular subgrouping and reference-free aneuploidy detection in medulloblastoma using low-depth whole genome bisulfite sequencing
    Article Snippet: .. This classifier relies on data arising from Illumina DNA methylation microarray technology, a cost-effective assay whose current release interrogates ~ 935,000 CpGs across the human genome and is compatible with both fresh-frozen and FFPE DNA derivatives [ ]. ..

    Article Title: Measuring technical variability in illumina DNA methylation microarrays.
    Article Snippet: .. Experimental design to measure technical variability in Illumina’s MethylationEPIC microarrays We first sought to design an experiment that would allow a comparison of positional effects in Illumina’s DNA methylation microarrays. .. We concentrated on isolating the Illumina MethylationEPICv1 arrays.

    Article Title: Immune impacts of fire smoke exposure.
    Article Snippet: Exposure to fire smoke has become a global health concern and is associated with increased morbidity and mortality.. There is a lack of understanding of the specific immune mechanisms involved in smoke exposure, with preventive and targeted interventions needed.. After exposure to fire smoke, which includes PM2.5, toxic metals and perfluoroalkyl and polyfluoroalkyl substances, epidemiology-based studies have demonstrated increases in respiratory (for example, asthma exacerbation), cardiac (for example, myocardial infarction, arrhythmias), neurological (for example, stroke) and pregnancy-related (for example, low birthweight, premature birth) outcomes.

    Article Title: Robust molecular subgrouping and reference-free aneuploidy detection in medulloblastoma using low-depth whole genome bisulfite sequencing.
    Article Snippet: .. As the WGBS platform surveys single CpGs and each probe assessed by the DNA methylation microarray spans ~ 50bp (Illumina), CpGs were defined as overlapping if they were within 25bp in both directions of a CpG site. ..

    Article Title: DNA methylation as a contributor to dysregulation of STX6 and other frontotemporal Lobar degeneration genetic risk-associated loci.
    Article Snippet: .. The available DNA methylation profiles were derived using Illumina 450K/EPIC arrays, which are not comprehensive despite their coverage throughout the genome. ..

    Microarray:

    Article Title: Robust molecular subgrouping and reference-free aneuploidy detection in medulloblastoma using low-depth whole genome bisulfite sequencing.
    Article Snippet: .. This classifier relies on data arising from Illumina DNA methylation microarray technology, a cost-effective assay whose current release interrogates ~ 935,000 CpGs across the human genome and is compatible with both fresh-frozen and FFPE DNA derivatives [15]. ..

    Article Title: Measuring technical variability in illumina DNA methylation microarrays.
    Article Snippet: .. In this study, we measure the impact of common sources of technical variability in Illumina DNA methylation microarray data, with a specific focus on positional biases inherent within the microarray technology. ..

    Article Title: Robust molecular subgrouping and reference-free aneuploidy detection in medulloblastoma using low-depth whole genome bisulfite sequencing
    Article Snippet: .. This classifier relies on data arising from Illumina DNA methylation microarray technology, a cost-effective assay whose current release interrogates ~ 935,000 CpGs across the human genome and is compatible with both fresh-frozen and FFPE DNA derivatives [ ]. ..

    Article Title: Robust molecular subgrouping and reference-free aneuploidy detection in medulloblastoma using low-depth whole genome bisulfite sequencing.
    Article Snippet: .. As the WGBS platform surveys single CpGs and each probe assessed by the DNA methylation microarray spans ~ 50bp (Illumina), CpGs were defined as overlapping if they were within 25bp in both directions of a CpG site. ..

    Formalin-fixed Paraffin-Embedded:

    Article Title: Robust molecular subgrouping and reference-free aneuploidy detection in medulloblastoma using low-depth whole genome bisulfite sequencing.
    Article Snippet: .. This classifier relies on data arising from Illumina DNA methylation microarray technology, a cost-effective assay whose current release interrogates ~ 935,000 CpGs across the human genome and is compatible with both fresh-frozen and FFPE DNA derivatives [15]. ..

    Article Title: Robust molecular subgrouping and reference-free aneuploidy detection in medulloblastoma using low-depth whole genome bisulfite sequencing
    Article Snippet: .. This classifier relies on data arising from Illumina DNA methylation microarray technology, a cost-effective assay whose current release interrogates ~ 935,000 CpGs across the human genome and is compatible with both fresh-frozen and FFPE DNA derivatives [ ]. ..

    Comparison:

    Article Title: Measuring technical variability in illumina DNA methylation microarrays.
    Article Snippet: .. Experimental design to measure technical variability in Illumina’s MethylationEPIC microarrays We first sought to design an experiment that would allow a comparison of positional effects in Illumina’s DNA methylation microarrays. .. We concentrated on isolating the Illumina MethylationEPICv1 arrays.

    Derivative Assay:

    Article Title: DNA methylation as a contributor to dysregulation of STX6 and other frontotemporal Lobar degeneration genetic risk-associated loci.
    Article Snippet: .. The available DNA methylation profiles were derived using Illumina 450K/EPIC arrays, which are not comprehensive despite their coverage throughout the genome. ..



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    Comparison of candidate gene studies, microarrays, and methylome sequencing approaches for <t>DNA</t> <t>methylation</t> biomarker studies. Genome CpG coverage varies significantly across methods. Candidate gene approaches can typically assess tens to thousands of CpG sites, microarrays (such as Illumina 450 k and 850 k platforms) cover hundreds of thousands of CpG sites, and methylome-wide approaches capture nearly all 29.4 million CpG sites within the human genome (hg38, autosomes, X and Y). Base- and strand-level resolution highlights the ability to measure methylation at single-CpG resolution on individual DNA strands, and is only practically feasible with methylome sequencing. Microarrays provide site-level resolution based on reference genome sequence, but do not typically distinguish between strands, while candidate gene approaches are limited to specific loci typically without strand information. The potential for participant re-identification increases with the scale and resolution of the data. Sequencing-based methods pose a higher risk due to the comprehensive and individual-specific nature of the genomic data, requiring robust data governance and privacy protections. Microarrays and candidate gene studies present lower re-identification risks, as they capture less data and provide limited genomic context. Relative cost per sample reflects the resources needed for data production and analysis. Candidate gene approaches are the most cost-effective, while microarrays offer a balance of affordability, with methylome sequencing being the most costly due to sequencing and computational demands. Raw data sizes illustrate the storage demands of each method. Candidate gene studies generate minimal data (<10 MB per sample), while microarrays produce 16–20 MB per sample. In contrast, methylomes at 30x coverage produce approximately 110 GB of raw data per sample (compressed FASTQ format) and about 62 GB of mapped data (CRAM format), accounting for ∼10% data loss through PCR duplicates and read filtering, however this can be highly variable. The computational resources required increase with data complexity. Candidate gene and <t>microarray</t> studies can typically be processed on desktop computers or small servers, while methylome analyses often require servers or high-performance computing (HPC) environments. The shift to advanced computing infrastructure is driven by the large datasets and computationally intensive analyses associated with sequencing-based studies.
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    Image Search Results


    Comparison of candidate gene studies, microarrays, and methylome sequencing approaches for DNA methylation biomarker studies. Genome CpG coverage varies significantly across methods. Candidate gene approaches can typically assess tens to thousands of CpG sites, microarrays (such as Illumina 450 k and 850 k platforms) cover hundreds of thousands of CpG sites, and methylome-wide approaches capture nearly all 29.4 million CpG sites within the human genome (hg38, autosomes, X and Y). Base- and strand-level resolution highlights the ability to measure methylation at single-CpG resolution on individual DNA strands, and is only practically feasible with methylome sequencing. Microarrays provide site-level resolution based on reference genome sequence, but do not typically distinguish between strands, while candidate gene approaches are limited to specific loci typically without strand information. The potential for participant re-identification increases with the scale and resolution of the data. Sequencing-based methods pose a higher risk due to the comprehensive and individual-specific nature of the genomic data, requiring robust data governance and privacy protections. Microarrays and candidate gene studies present lower re-identification risks, as they capture less data and provide limited genomic context. Relative cost per sample reflects the resources needed for data production and analysis. Candidate gene approaches are the most cost-effective, while microarrays offer a balance of affordability, with methylome sequencing being the most costly due to sequencing and computational demands. Raw data sizes illustrate the storage demands of each method. Candidate gene studies generate minimal data (<10 MB per sample), while microarrays produce 16–20 MB per sample. In contrast, methylomes at 30x coverage produce approximately 110 GB of raw data per sample (compressed FASTQ format) and about 62 GB of mapped data (CRAM format), accounting for ∼10% data loss through PCR duplicates and read filtering, however this can be highly variable. The computational resources required increase with data complexity. Candidate gene and microarray studies can typically be processed on desktop computers or small servers, while methylome analyses often require servers or high-performance computing (HPC) environments. The shift to advanced computing infrastructure is driven by the large datasets and computationally intensive analyses associated with sequencing-based studies.

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    Figure Lengend Snippet: Comparison of candidate gene studies, microarrays, and methylome sequencing approaches for DNA methylation biomarker studies. Genome CpG coverage varies significantly across methods. Candidate gene approaches can typically assess tens to thousands of CpG sites, microarrays (such as Illumina 450 k and 850 k platforms) cover hundreds of thousands of CpG sites, and methylome-wide approaches capture nearly all 29.4 million CpG sites within the human genome (hg38, autosomes, X and Y). Base- and strand-level resolution highlights the ability to measure methylation at single-CpG resolution on individual DNA strands, and is only practically feasible with methylome sequencing. Microarrays provide site-level resolution based on reference genome sequence, but do not typically distinguish between strands, while candidate gene approaches are limited to specific loci typically without strand information. The potential for participant re-identification increases with the scale and resolution of the data. Sequencing-based methods pose a higher risk due to the comprehensive and individual-specific nature of the genomic data, requiring robust data governance and privacy protections. Microarrays and candidate gene studies present lower re-identification risks, as they capture less data and provide limited genomic context. Relative cost per sample reflects the resources needed for data production and analysis. Candidate gene approaches are the most cost-effective, while microarrays offer a balance of affordability, with methylome sequencing being the most costly due to sequencing and computational demands. Raw data sizes illustrate the storage demands of each method. Candidate gene studies generate minimal data (<10 MB per sample), while microarrays produce 16–20 MB per sample. In contrast, methylomes at 30x coverage produce approximately 110 GB of raw data per sample (compressed FASTQ format) and about 62 GB of mapped data (CRAM format), accounting for ∼10% data loss through PCR duplicates and read filtering, however this can be highly variable. The computational resources required increase with data complexity. Candidate gene and microarray studies can typically be processed on desktop computers or small servers, while methylome analyses often require servers or high-performance computing (HPC) environments. The shift to advanced computing infrastructure is driven by the large datasets and computationally intensive analyses associated with sequencing-based studies.

    Article Snippet: The latest Illumina EPIC DNA methylation microarray (900 K) includes the addition of probes to study open chromatin as well as additional enhancer locations ( ).

    Techniques: Comparison, Sequencing, DNA Methylation Assay, Biomarker Discovery, Methylation, Microarray