How to handle minor allele frequency in methylation data?
Best insight from top research papers
To handle minor allele frequency (MAF) in methylation data, various approaches have been developed. GAMA, a high-performance MAF computation program with GPU acceleration, enables MAF computation for thousands of individuals, surpassing traditional methods in speed and efficiency . MS-NaME utilizes a highly-multiplexed approach to enrich minor DNA populations with aberrant methylation changes, enhancing the detection of low-level methylation alterations in clinical samples . For sensitive detection of low-level mosaic epimutations, epialleleR provides a computational framework that requires linkage to epihaplotype data, crucial for identifying subtle methylation events in tumor suppressor genes . Additionally, a novel approach using multiple low-frequency or rare variants collectively associated with methylation levels has shown promise in identifying regions significantly impacting DNA methylation, complementing single variant analyses . These methods collectively offer robust strategies to handle MAF in methylation data analysis.
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3 Citations | Not addressed in the paper. |
| Collapsed Methylation Quantitative Trait Loci analysis identifies low frequency and rare variants collectively associated with DNA methylation levels, enhancing detection power compared to single variant approaches. | |
24 Citations | To handle minor allele frequency in methylation data, utilize Methylation Specific Nuclease-assisted Minor-allele Enrichment (MS-NaME) technique, which enriches minor methylated or unmethylated alleles for analysis. |
2 Citations | To handle minor allele frequency in methylation data, utilize epialleleR, an R/Bioconductor package for sensitive allele-specific methylation analysis, emphasizing the importance of epihaplotype data linkage. |
12 Aug 2012 3 Citations | Not addressed in the paper. |
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