Two computational papers published

Klughammer J, Datlinger P, Printz D, Sheffield N, Farlik M, Hadler J, Fritsch G, Bock C (2015). Differential DNA methylation analysis without a reference genome. Cell Reports, doi:10.1016/j.celrep.2015.11.024.

Abstract: Genome-wide DNA methylation mapping uncovers epigenetic changes associated with animal development, environmental adaptation, and species evolution. To address the lack of high-throughput methods for DNA methylation analysis in non-model organisms, we developed an integrated approach for studying DNA methylation differences independent of a reference genome. Experimentally, our method relies on an optimized 96-well protocol for reduced representation bisulfite sequencing (RRBS), which we have validated in nine species (human, mouse, rat, cow, dog, chicken, carp, sea bass, and zebrafish). Bioinformatically, we developed the RefFreeDMA software to deduce ad hoc genomes directly from RRBS reads and to pinpoint differentially methylated regions between samples or groups of individuals ( The identified regions are interpreted using motif enrichment analysis and/or cross-mapping to annotated genomes. We validated our method by reference-free analysis of cell-typespecific DNA methylation in the blood of human, cow, and carp. In summary, we present a cost-effective method for epigenome analysis in ecology and evolution, which enables epigenome-wide association studies in natural populations and species without a reference genome. (PDF)

-> This paper describes a method for studying DNA methylation in wild populations and in species that lack a reference genome, enabling the widespread use of epigenome-wide association studies (EWAS) in the context of ecology and evolution. GenomeWeb posted a summary of the paper.


Sheffield NC, Bock C (2015). LOLA: Enrichment analysis for genomic region sets and regulatory elements in R and Bioconductor. Bioinformatics, doi: 10.1093/bioinformatics/btv612.

Abstract: Genomic datasets are often interpreted in the context of large-scale reference databases. One approach is to identify significantly overlapping gene sets, which works well for gene-centric data. However, many types of high-throughput data are based on genomic regions. Locus Overlap Analysis (LOLA) provides easy and automatable enrichment analysis for genomic region sets, thus facilitating the interpretation of functional genomics and epigenomics data. R package available in Bioconductor and on the following website: (PDF)

-> This paper describes an R package and database for enrichment analysis on sets of genomic regions. Roughly analogous to what GSEA does for gene sets, LOLA identifies significantly overlapping region sets for user-provided region sets derived from experiments such as ChIP-seq, BS-seq, DNase-seq, etc.

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