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      Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis.

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          Abstract

          High-throughput profiling of DNA methylation status of CpG islands is crucial to understand the epigenetic regulation of genes. The microarray-based Infinium methylation assay by Illumina is one platform for low-cost high-throughput methylation profiling. Both Beta-value and M-value statistics have been used as metrics to measure methylation levels. However, there are no detailed studies of their relations and their strengths and limitations.

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          Principles and challenges of genomewide DNA methylation analysis.

          Peter Laird (2010)
          Methylation of cytosine bases in DNA provides a layer of epigenetic control in many eukaryotes that has important implications for normal biology and disease. Therefore, profiling DNA methylation across the genome is vital to understanding the influence of epigenetics. There has been a revolution in DNA methylation analysis technology over the past decade: analyses that previously were restricted to specific loci can now be performed on a genome-scale and entire methylomes can be characterized at single-base-pair resolution. However, there is such a diversity of DNA methylation profiling techniques that it can be challenging to select one. This Review discusses the different approaches and their relative merits and introduces considerations for data analysis.
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            CpG island hypermethylation and tumor suppressor genes: a booming present, a brighter future.

            Manel Esteller (2002)
            We have come a long way since the first reports of the existence of aberrant DNA methylation in human cancer. Hypermethylation of CpG islands located in the promoter regions of tumor suppressor genes is now firmly established as an important mechanism for gene inactivation. CpG island hypermethylation has been described in almost every tumor type. Many cellular pathways are inactivated by this type of epigenetic lesion: DNA repair (hMLH1, MGMT), cell cycle (p16(INK4a), p15(INK4b), p14(ARF)), apoptosis (DAPK), cell adherence (CDH1, CDH13), detoxification (GSTP1), etc em leader However, we still know little of the mechanisms of aberrant methylation and why certain genes are selected over others. Hypermethylation is not an isolated layer of epigenetic control, but is linked to the other pieces of the puzzle such as methyl-binding proteins, DNA methyltransferases and histone deacetylase, but our understanding of the degree of specificity of these epigenetic layers in the silencing of specific tumor suppressor genes remains incomplete. The explosion of user-friendly technologies has given rise to a rapidly increasing list of hypermethylated genes. Careful functional and genetic studies are necessary to determine which hypermethylation events are truly relevant for human tumorigenesis. The development of CpG island hypermethylation profiles for every form of human tumors has yielded valuable pilot clinical data in monitoring and treating cancer patients based in our knowledge of DNA methylation. Basic and translational will both be needed in the near future to fully understand the mechanisms, roles and uses of CpG island hypermethylation in human cancer. The expectations are high.
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              Model-based variance-stabilizing transformation for Illumina microarray data

              Simon M Lin, Pan Du, Wolfgang Huber … (2008)
              Variance stabilization is a step in the preprocessing of microarray data that can greatly benefit the performance of subsequent statistical modeling and inference. Due to the often limited number of technical replicates for Affymetrix and cDNA arrays, achieving variance stabilization can be difficult. Although the Illumina microarray platform provides a larger number of technical replicates on each array (usually over 30 randomly distributed beads per probe), these replicates have not been leveraged in the current log2 data transformation process. We devised a variance-stabilizing transformation (VST) method that takes advantage of the technical replicates available on an Illumina microarray. We have compared VST with log2 and Variance-stabilizing normalization (VSN) by using the Kruglyak bead-level data (2006) and Barnes titration data (2005). The results of the Kruglyak data suggest that VST stabilizes variances of bead-replicates within an array. The results of the Barnes data show that VST can improve the detection of differentially expressed genes and reduce false-positive identifications. We conclude that although both VST and VSN are built upon the same model of measurement noise, VST stabilizes the variance better and more efficiently for the Illumina platform by leveraging the availability of a larger number of within-array replicates. The algorithms and Supplementary Data are included in the lumi package of Bioconductor, available at: www.bioconductor.org.
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                Author and article information

                Journal
                Journal ID (iso-abbrev): BMC Bioinformatics
                Title: BMC bioinformatics
                Publisher: Springer Science and Business Media LLC
                ISSN (Electronic): 1471-2105
                ISSN (Print): 1471-2105
                Publication date (Electronic): Nov 30 2010
                Volume: 11
                Affiliations
                [1 ] Northwestern University Biomedical Informatics Center (NUBIC), NUCATS, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA. dupan@northwestern.edu
                Article
                Publisher Item ID: 1471-2105-11-587
                DOI: 10.1186/1471-2105-11-587
                PMC ID: 3012676
                PubMed ID: 21118553
                SO-VID: 88943c3e-54fe-46b8-972f-418576d75f55
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