RnBeads 2.0: comprehensive analysis of DNA methylation data

[1]  Georg Langs,et al.  The DNA methylation landscape of glioblastoma disease progression shows extensive heterogeneity in time and space , 2017, Nature Medicine.

[2]  Yan Zhou,et al.  Methylation-level inferences and detection of differential methylation with MeDIP-seq data , 2018, PloS one.

[3]  Björn Grüning,et al.  Strategies for analyzing bisulfite sequencing data , 2017, bioRxiv.

[4]  Yuan Tian,et al.  ChAMP: updated methylation analysis pipeline for Illumina BeadChips , 2017, Bioinform..

[5]  Shijie C. Zheng,et al.  Cell-type deconvolution in epigenome-wide association studies: a review and recommendations. , 2017, Epigenomics.

[6]  Nathan C. Sheffield,et al.  Epigenetik erklärt Vielfalt von Krebs bei Kindern , 2017, TumorDiagnostik & Therapie.

[7]  Nathan C. Sheffield,et al.  DNA methylation heterogeneity defines a disease spectrum in Ewing sarcoma , 2017, Nature Medicine.

[8]  Steven J. M. Jones,et al.  The International Human Epigenome Consortium: A Blueprint for Scientific Collaboration and Discovery , 2016, Cell.

[9]  Timothy J. Peters,et al.  Critical evaluation of the Illumina MethylationEPIC BeadChip microarray for whole-genome DNA methylation profiling , 2016, Genome Biology.

[10]  Ying-Chao Lin,et al.  Methods for identifying differentially methylated regions for sequence- and array-based data. , 2016, Briefings in functional genomics.

[11]  Francine E. Garrett-Bakelman,et al.  Distinct evolution and dynamics of epigenetic and genetic heterogeneity in acute myeloid leukemia , 2016, Nature Medicine.

[12]  Andrew E. Teschendorff,et al.  Stochastic epigenetic outliers can define field defects in cancer , 2016, BMC Bioinformatics.

[13]  M. Esteller,et al.  Validation of a DNA methylation microarray for 850,000 CpG sites of the human genome enriched in enhancer sequences , 2015, Epigenomics.

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

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

[16]  Ash A. Alizadeh,et al.  Toward understanding and exploiting tumor heterogeneity , 2015, Nature Medicine.

[17]  S. Horvath,et al.  HIV-1 Infection Accelerates Age According to the Epigenetic Clock , 2015, The Journal of infectious diseases.

[18]  P. Flicek,et al.  The Ensembl Regulatory Build , 2015, Genome Biology.

[19]  Nathan C. Sheffield,et al.  Epigenome Mapping Reveals Distinct Modes of Gene Regulation and Widespread Enhancer Reprogramming by the Oncogenic Fusion Protein EWS-FLI1 , 2015, Cell reports.

[20]  Michael Q. Zhang,et al.  Integrative analysis of 111 reference human epigenomes , 2015, Nature.

[21]  S. Horvath,et al.  DNA methylation age of blood predicts all-cause mortality in later life , 2015, Genome Biology.

[22]  S. Beck,et al.  Analysis pipelines and packages for Infinium HumanMethylation450 BeadChip (450k) data , 2015, Methods.

[23]  Michael J. Ziller,et al.  Locally disordered methylation forms the basis of intratumor methylome variation in chronic lymphocytic leukemia. , 2014, Cancer cell.

[24]  Thomas Lengauer,et al.  Comprehensive Analysis of DNA Methylation Data with RnBeads , 2014, Nature Methods.

[25]  A. Oshlack,et al.  DiffVar: a new method for detecting differential variability with application to methylation in cancer and aging , 2014, bioRxiv.

[26]  Nongluk Plongthongkum,et al.  Advances in the profiling of DNA modifications: cytosine methylation and beyond , 2014, Nature Reviews Genetics.

[27]  Rafael A. Irizarry,et al.  Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays , 2014, Bioinform..

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

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

[30]  Andrea Riebler,et al.  BayMeth: improved DNA methylation quantification for affinity capture sequencing data using a flexible Bayesian approach , 2013, Genome Biology.

[31]  S. Horvath.  DNA methylation age of human tissues and cell types , 2013, Genome Biology.

[32]  Alexandra M. Binder,et al.  Recommendations for the design and analysis of epigenome-wide association studies , 2013, Nature Methods.

[33]  A. Gnirke,et al.  Charting a dynamic DNA methylation landscape of the human genome , 2013, Nature.

[34]  Ulf Gyllensten,et al.  Continuous Aging of the Human DNA Methylome Throughout the Human Lifespan , 2013, PloS one.

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

[36]  R. Mitra,et al.  Laser capture microdissection–reduced representation bisulfite sequencing (LCM-RRBS) maps changes in DNA methylation associated with gonadectomy-induced adrenocortical neoplasia in the mouse , 2013, Nucleic acids research.

[37]  Jeffrey B. Cheng,et al.  Estimating absolute methylation levels at single-CpG resolution from methylation enrichment and restriction enzyme sequencing methods , 2013, RECOMB.

[38]  T. Ideker,et al.  Genome-wide methylation profiles reveal quantitative views of human aging rates. , 2013, Molecular cell.

[39]  Francine E. Garrett-Bakelman,et al.  methylKit: a comprehensive R package for the analysis of genome-wide DNA methylation profiles , 2012, Genome Biology.

[40]  C. Bock.  Analysing and interpreting DNA methylation data , 2012, Nature Reviews Genetics.

[41]  J. Kere,et al.  Differential DNA Methylation in Purified Human Blood Cells: Implications for Cell Lineage and Studies on Disease Susceptibility , 2012, PloS one.

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

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

[44]  Thomas Lengauer,et al.  BLUEPRINT to decode the epigenetic signature written in blood , 2012, Nature Biotechnology.

[45]  K. Gunderson,et al.  High density DNA methylation array with single CpG site resolution. , 2011, Genomics.

[46]  D. Balding,et al.  Epigenome-wide association studies for common human diseases , 2011, Nature Reviews Genetics.

[47]  Felix Krueger,et al.  Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications , 2011, Bioinform..

[48]  P. Laird.  Principles and challenges of genome-wide DNA methylation analysis , 2010, Nature Reviews Genetics.

[49]  Zachary D. Smith,et al.  Genome-scale DNA methylation mapping of clinical samples at single-nucleotide resolution , 2010, Nature Methods.

[50]  Lee E. Edsall,et al.  Human DNA methylomes at base resolution show widespread epigenomic differences , 2009, Nature.

[51]  G. Daley,et al.  Targeted bisulfite sequencing reveals changes in DNA methylation associated with nuclear reprogramming , 2009, Nature Biotechnology.

[52]  Madeleine P. Ball,et al.  Targeted and genome-scale methylomics reveals gene body signatures in human cell lines , 2009, Nature Biotechnology.

[53]  R. Durbin,et al.  A Bayesian deconvolution strategy for immunoprecipitation-based DNA methylome analysis , 2008, Nature Biotechnology.

[54]  I. Weissman,et al.  Stems Cells and the Pathways to Aging and Cancer , 2008, Cell.

[55]  Russ B. Altman,et al.  Missing value estimation methods for DNA microarrays , 2001, Bioinform..