English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Chromatin ImmunoPrecipitation-sequencing (ChIP-seq) experiments have nowbecome routine in biology for the detection of protein binding sites. In thispaper, we present a Markov random field model for the joint analysis ofmultiple ChIP-seq experiments. The proposed model naturally accounts forspatial dependencies in the data, by assuming first order Markov properties,and for the large proportion of zero counts, by using zero-inflated mixturedistributions. In contrast to all other available implementations, the modelallows for the joint modelling of multiple experiments, by incorporating keyaspects of the experimental design. In particular, the model uses theinformation about replicates and about the different antibodies used in theexperiments. An extensive simulation study shows a lower false non-discoveryrate for the proposed method, compared to existing methods, at the same falsediscovery rate. Finally, we present an analysis on real data for the detectionof histone modifications of two chromatin modifiers from eight ChIP-seqexperiments, including technical replicates with different IP efficiencies.

Joint modelling of ChIP-seq data via a Markov random field model