Probe signal correction for differential methylation hybridization experiments

BackgroundNon-biological signal (or noise) has been the bane of microarray analysis. Hybridization effects related to probe-sequence composition and DNA dye-probe interactions have been observed in differential methylation hybridization (DMH) microarray experiments as well as other effects inherent to the DMH protocol.ResultsWe suggest two models to correct for non-biologically relevant probe signal with an overarching focus on probe-sequence composition. The estimated effects are evaluated and the strengths of the models are considered in the context of DMH analyses.ConclusionThe majority of estimated parameters were statistically significant in all considered models. Model selection for signal correction is based on interpretation of the estimated values and their biological significance.

[1]  Rafael A. Irizarry,et al.  A Model-Based Background Adjustment for Oligonucleotide Expression Arrays , 2004 .

[2]  Rolf Ohlsson,et al.  The binding sites for the chromatin insulator protein CTCF map to DNA methylation-free domains genome-wide. , 2004, Genome research.

[3]  Shili Lin,et al.  Differential methylation hybridization: profiling DNA methylation with a high-density CpG island microarray. , 2009, Methods in molecular biology.

[4]  Terry Speed,et al.  Normalization of cDNA microarray data. , 2003, Methods.

[5]  Peter A. Jones,et al.  The fundamental role of epigenetic events in cancer , 2002, Nature Reviews Genetics.

[6]  Clifford A. Meyer,et al.  Model-based analysis of tiling-arrays for ChIP-chip , 2006, Proceedings of the National Academy of Sciences.

[7]  G. Klein,et al.  Modern criteria to determine the etiology of human carcinogens. , 2004, Seminars in cancer biology.

[8]  Chunlei Wu,et al.  Sequence dependence of cross-hybridization on short oligo microarrays , 2005, Nucleic acids research.

[9]  Michael L. Bittner,et al.  Microarrays: Optical Technologies and Informatics , 2001 .

[10]  Wei Li,et al.  Model-based analysis of two-color arrays (MA2C) , 2007, Genome Biology.

[11]  T. Huang,et al.  Methylation profiling of CpG islands in human breast cancer cells. , 1999, Human molecular genetics.

[12]  Andrew P Feinberg,et al.  The epigenetics of cancer etiology. , 2004, Seminars in cancer biology.

[13]  Terence P. Speed,et al.  Normalization for cDNA microarry data , 2001, SPIE BiOS.

[14]  W. Lam,et al.  Chromosome-wide and promoter-specific analyses identify sites of differential DNA methylation in normal and transformed human cells , 2005, Nature Genetics.

[15]  Jonathan D. Wren,et al.  Engineering in Genomics Cross-Hybridization on PCR-Spotted Microarrays , 2002 .

[16]  Jean-Jacques Daudin,et al.  Spotting effect in microarray experiments , 2004, BMC Bioinformatics.

[17]  S. Dudoit,et al.  Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. , 2002, Nucleic acids research.

[18]  A. Bird,et al.  Epigenetic regulation of gene expression: how the genome integrates intrinsic and environmental signals , 2003, Nature Genetics.

[19]  Peter A. Jones,et al.  Epigenetics in cancer. , 2010, Carcinogenesis.

[20]  J. Herman,et al.  Gene silencing in cancer in association with promoter hypermethylation. , 2003, The New England journal of medicine.

[21]  Jonathan D Wren,et al.  Cross-hybridization on PCR-spotted microarrays. , 2002, IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society.

[22]  T. Huang,et al.  Identifying Clinicopathological Association of DNA Hypermethylation in Cancers Using CpG Island Microarrays , 2005 .

[23]  Wen-Lin Kuo,et al.  A collection of breast cancer cell lines for the study of functionally distinct cancer subtypes. , 2006, Cancer cell.