RnBeads (cid:21) Comprehensive Analysis of DNA Methylation Data

RnBeads is an R package for the comprehensive analysis of genome-wide DNA methylation data with single basepair resolution. Supported assays include the In(cid:28)nium 450k microarray, whole genome bisul(cid:28)te sequencing (WGBS), reduced representation bisul(cid:28)te sequencing (RRBS), other forms of enrichment bisul(cid:28)te sequencing, and any other large-scale method that can provide DNA methylation data at single basepair resolution (e.g. MeDIP-seq after suitable preprocessing). It builds upon a signi(cid:28)cant and ongoing community e(cid:27)ort to devise e(cid:27)ective algorithms for dealing with large-scale DNA methylation data and implements these methods in a user-friendly, high-throughput work(cid:29)ow, presenting the analysis results in a highly interpretable fashion. p-values from statistical modeling into a single score. The lower the combined rank, the higher is the degree of di(cid:27)erential methylation. I.e. when you sort the list according to their combined rank, the best ranking sites will be at the top of the list, and when you work your way towards the bottom of the list, false positives become more likely.

[1]  Nathan C. Sheffield,et al.  LOLA: enrichment analysis for genomic region sets and regulatory elements in R and Bioconductor , 2015, Bioinform..

[2]  A. Butte,et al.  Systematic pan-cancer analysis of tumour purity , 2015, Nature Communications.

[3]  Martin J. Aryee,et al.  Epigenome-wide association studies without the need for cell-type composition , 2014, Nature Methods.

[4]  K. Hansen,et al.  Functional normalization of 450k methylation array data improves replication in large cancer studies , 2014, Genome Biology.

[5]  E. Andres Houseman,et al.  Reference-free cell mixture adjustments in analysis of DNA methylation data , 2014, Bioinform..

[6]  Michael B. Stadler,et al.  Identification of active regulatory regions from DNA methylation data , 2013, Nucleic acids research.

[7]  Ruth Pidsley,et al.  A data-driven approach to preprocessing Illumina 450K methylation array data , 2013, BMC Genomics.

[8]  P. Laird,et al.  Low-level processing of Illumina Infinium DNA Methylation BeadArrays , 2013, Nucleic acids research.

[9]  Martin J. Aryee,et al.  A cell epigenotype specific model for the correction of brain cellular heterogeneity bias and its application to age, brain region and major depression , 2013, Epigenetics.

[10]  Francesco Marabita,et al.  A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinium 450 k DNA methylation data , 2012, Bioinform..

[11]  P. Laird,et al.  Bis-SNP: Combined DNA methylation and SNP calling for Bisulfite-seq data , 2012, Genome Biology.

[12]  Devin C. Koestler,et al.  DNA methylation arrays as surrogate measures of cell mixture distribution , 2012, BMC Bioinformatics.

[13]  Julie V. Harness,et al.  Recurrent variations in DNA methylation in human pluripotent stem cells and their differentiated derivatives. , 2012, Cell stem cell.

[14]  Andrew E. Jaffe,et al.  Bioinformatics Applications Note Gene Expression the Sva Package for Removing Batch Effects and Other Unwanted Variation in High-throughput Experiments , 2022 .

[15]  Thomas Lengauer,et al.  Genomic Distribution and Inter-Sample Variation of Non-CpG Methylation across Human Cell Types , 2011, PLoS genetics.

[16]  John D. Storey,et al.  Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis , 2007, PLoS genetics.

[17]  Robert Gentleman,et al.  Using GOstats to test gene lists for GO term association , 2007, Bioinform..

[18]  K. Makambi.  Weighted inverse chi-square method for correlated significance tests , 2003 .

[19]  M. Ashburner,et al.  Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.