MEDIPS: genome-wide differential coverage analysis of sequencing data derived from DNA enrichment experiments

Motivation: DNA enrichment followed by sequencing is a versatile tool in molecular biology, with a wide variety of applications including genome-wide analysis of epigenetic marks and mechanisms. A common requirement of these diverse applications is a comparison of read coverage between experimental conditions. The amount of samples generated for such comparisons ranges from few replicates to hundreds of samples per condition for epigenome-wide association studies. Consequently, there is an urgent need for software that allows for fast and simple processing and comparison of sequencing data derived from enriched DNA. Results: Here, we present a major update of the R/Bioconductor package MEDIPS, which allows for an arbitrary number of replicates per group and integrates sophisticated statistical methods for the detection of differential coverage between experimental conditions. Our approach can be applied to a diversity of quantitative sequencing data. In addition, our update adds novel functionality to MEDIPS, including correlation analysis between samples, and takes advantage of Bioconductor’s annotation databases to facilitate annotation of specific genomic regions. Availability and implementation: The latest version of MEDIPS is available as version 1.12.0 and part of Bioconductor 2.13. The package comes with a manual containing detailed description of its functionality and is available at http://www.bioconductor.org. Contact: lienhard@molgen.mpg.de Supplementary information: Supplementary data are available at Bioinformatics online.

[1]  Philipp Kapranov,et al.  Genome-wide mapping of 5-hydroxymethylcytosine in embryonic stem cells , 2011, Nature.

[2]  Ralf Herwig,et al.  DNA–Methylome Analysis of Mouse Intestinal Adenoma Identifies a Tumour-Specific Signature That Is Partly Conserved in Human Colon Cancer , 2013, PLoS genetics.

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

[4]  References , 1971 .

[5]  Cole Trapnell,et al.  Ultrafast and memory-efficient alignment of short DNA sequences to the human genome , 2009, Genome Biology.

[6]  Dario Strbenac,et al.  Evaluation of affinity-based genome-wide DNA methylation data: effects of CpG density, amplification bias, and copy number variation. , 2010, Genome research.

[7]  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.

[8]  Mark D. Robinson,et al.  edgeR: a Bioconductor package for differential expression analysis of digital gene expression data , 2009, Bioinform..

[9]  E. Birney,et al.  Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt , 2009, Nature Protocols.

[10]  David Serre,et al.  MBD-isolated Genome Sequencing provides a high-throughput and comprehensive survey of DNA methylation in the human genome , 2009, Nucleic acids research.

[11]  Ralf Herwig,et al.  Computational analysis of genome-wide DNA methylation during the differentiation of human embryonic stem cells along the endodermal lineage. , 2010, Genome research.

[12]  Bjoern Peters,et al.  Epigenomic analysis of primary human T cells reveals enhancers associated with TH2 memory cell differentiation and asthma susceptibility , 2014, Nature Immunology.