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New papers in Nature Methods

Schmidl C, Rendeiro AF, Sheffield NC, Bock C (2015). ChIPmentation: fast, robust, low-input ChIP-seq for histones and transcription factors. Nature Methods, doi:10.1038/nmeth.3542.

Abstract: Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is widely used to map histone marks and transcription factor binding throughout the genome. Here we present ChIPmentation, a method that combines chromatin immunoprecipitation with sequencing library preparation by tn5 transposase (‘tagmentation’). ChIPmentation introduces sequencing-compatible adaptors in a single-step reaction directly on bead-bound chromatin, which reduces time, cost and input requirements, thus providing a convenient and broadly useful alternative to existing ChIP-seq protocols.

http://www.nature.com/nmeth/journal/vaop/ncurrent/full/nmeth.3542.html (no journal subscription? Read it online for free or ask us to send you the PDF)

-> This paper describes a powerful alternative to one of the most widely used epigenomics assays. The new method, which is called ChIPmentation, provides much-improved speed, robustness, and cost-effectiveness compared to existing protocols for chromatin immunoprecipitation followed by next generation sequencing (ChIP-seq). GenomeWeb posted a nice summary of the paper.

 

Assenov Y, Müller F, Lutsik P, Walter J, Lengauer T, Bock C (2014). Comprehensive analysis of DNA methylation data with RnBeadsNature Methods, DOI: 10.1038/nmeth.3115.

Abstract: RnBeads is a software tool for large-scale analysis and interpretation of DNA methylation data, providing a user-friendly analysis workflow that yields detailed hypertext reports (http://rnbeads.mpi-inf.mpg.de/). Supported assays include whole-genome bisulfite sequencing, reduced representation bisulfite sequencing, Infinium microarrays and any other protocol that produces high-resolution DNA methylation data. Notable applications of RnBeads include the analysis of epigenome-wide association studies and epigenetic biomarker discovery in cancer cohorts.

http://www.nature.com/nmeth/journal/vaop/ncurrent/full/nmeth.3115.html (no journal subscription? Read it online for free or ask us to send you the PDF)

-> This paper describes a bioinformatic method and software for efficiently analyzing DNA methylation in large biomedical research projects.

 

In addition, there was a recent Technology Feature summarizing the lab's research on epigenome data analysis, visualization, and interpretation:

Marx V (2015). Visualizing epigenomic dataNature Methods, doi:10.1038/nmeth.3409

http://www.nature.com/nmeth/journal/v12/n6/full/nmeth.3409.html (PDF)

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