MethylSeqDesign: a framework for Methyl-Seq genome-wide power calculation and study design issues.

Bisulfite DNA methylation sequencing (Methyl-Seq) becomes one of the most important technologies to study methylation level difference at a genome-wide scale. Due to the complexity and large scale of methyl-Seq data, power calculation and study design method have not been developed. Here, we propose a "MethylSeqDesign" framework for power calculation and study design of Methyl-Seq experiments by utilizing information from pilot data. Differential methylation analysis is based on a beta-binomial model. Power calculation is achieved using mixture model fitting of p-values from pilot data and a parametric bootstrap procedure. To circumvent the issue of existing tens of millions of methylation sites, we focus on the inference of pre-specified targeted regions. The performance of the method was evaluated with simulations. Two real examples are analyzed to illustrate our method. An R package "MethylSeqDesign" to implement this method is publicly available.

[1]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[2]  A. Ferguson-Smith,et al.  DNA methylation in genomic imprinting, development, and disease , 2001, The Journal of pathology.

[3]  K. Robertson DNA methylation and human disease , 2005, Nature Reviews Genetics.

[4]  K. Conneely,et al.  A Bayesian hierarchical model to detect differentially methylated loci from single nucleotide resolution sequencing data , 2014, Nucleic acids research.

[5]  Tom H. Pringle,et al.  The human genome browser at UCSC. , 2002, Genome research.

[6]  J. Licht DNA Methylation Inhibitors in Cancer Therapy: The Immunity Dimension , 2015, Cell.

[7]  Gabor T. Marth,et al.  Scotty: a web tool for designing RNA-Seq experiments to measure differential gene expression , 2013, Bioinform..

[8]  S. Baylin,et al.  DNA methylation and gene silencing in cancer , 2005, Nature Clinical Practice Oncology.

[9]  T. Gingeras,et al.  Microarray-based DNA methylation profiling: technology and applications , 2022 .

[10]  M. Esteller Aberrant DNA methylation as a cancer-inducing mechanism. , 2005, Annual review of pharmacology and toxicology.

[11]  Yinglei Lai,et al.  A censored beta mixture model for the estimation of the proportion of non-differentially expressed genes , 2010, Bioinform..

[12]  Yongseok Park,et al.  MethylSig: a whole genome DNA methylation analysis pipeline , 2014, Bioinform..

[13]  W. Cho,et al.  DNA Methylation and Cancer Diagnosis , 2013, International journal of molecular sciences.

[14]  Andrew D. Smith,et al.  Using beta-binomial regression for high-precision differential methylation analysis in multifactor whole-genome bisulfite sequencing experiments , 2014, BMC Bioinformatics.

[15]  David B. Allison,et al.  Power and sample size estimation in high dimensional biology , 2004 .

[16]  A. Gnirke,et al.  Reduced representation bisulfite sequencing for comparative high-resolution DNA methylation analysis , 2005, Nucleic acids research.

[17]  Dong Xu,et al.  Hypomethylation coordinates antagonistically with hypermethylation in cancer development: a case study of leukemia , 2016, Human Genomics.

[18]  Steven N. Hart,et al.  Calculating Sample Size Estimates for RNA Sequencing Data , 2013, J. Comput. Biol..

[19]  David B. Allison,et al.  A mixture model approach for the analysis of microarray gene expression data , 2002 .

[20]  Adrian V. Lee,et al.  Targeted DNA Methylation Screen in the Mouse Mammary Genome Reveals a Parity-Induced Hypermethylation of Igf1r That Persists Long after Parturition , 2015, Cancer Prevention Research.

[21]  Hao Wu,et al.  PROPER: comprehensive power evaluation for differential expression using RNA-seq , 2015, Bioinform..

[22]  Jordana T Bell,et al.  Power and sample size estimation for epigenome-wide association scans to detect differential DNA methylation , 2015, International journal of epidemiology.

[23]  Rudolf Jaenisch,et al.  Role for DNA methylation in genomic imprinting , 1993, Nature.

[24]  Hao Wu,et al.  Differential methylation analysis for BS-seq data under general experimental design , 2016, Bioinform..