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Provides a background correction method which uses a mixture of exponential and truncated normal distributions to flexibly model signal intensity and uses a truncated normal distribution to model background noise. Depending on data availability, three approaches are employed to estimate background normal distribution parameters using (i) internal chip negative controls, (ii) out-of-band Infinium I probe intensities or (iii) combined methylated and unmethylated intensities. Evaluation results in both duplicates and experimental standard samples showed that ENmix outperformed commonly used background subtraction methods in terms of improvement in replicability and accuracy as well as reducing probe design bias. After ENmix background correction the resulting data can be used with other commonly-used preprocessing methods including quantile normalization for between-sample normalization and BMIQ for further correction of probe-design bias.
A generative model to quantify DNA methylation modifications from any combination of bisulfite sequencing approaches, including reduced, oxidative, TET-assisted, chemical-modification assisted, and methylase-assisted bisulfite sequencing data. Lux models all cytosine modifications (C, 5mC, 5hmC, 5fC, and 5caC) simultaneously together with experimental parameters, including bisulfite conversion and oxidation efficiencies, as well as various chemical labeling and protection steps. We show that Lux improves the quantification and comparison of cytosine modification levels and that Lux can process any oxidized methylcytosine sequencing data sets to quantify all cytosine modifications.
Allows integrative analysis of different oxi-mC data sets with experimental parameters and arbitrary, complex experimental designs. The method is applicable in analysis of genome-wide, reduced representation and targeted bisulphite sequencing data. Comparisons to existing methods demonstrated that LuxGLM has similar or better differential methylation detection performance than existing tools on BS-seq data. Analysis of simulated data showed that LuxGLM can provide accurate estimates of DNA methylation modifications even when confounding factors are present. Moreover, for oxi-mC species measured using complex experimental designs, LuxGLM is superior in differential methylation analysis when compared with existing methods.
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