Genome biologyBioinformaticsPeer-reviewed journal article
CRUP: A comprehensive framework to predict condition-specific regulatory units
Anna RamischiD, Verena HeinrichiD, Laura V. GlaseriD, Alisa FuchsiD, Xinyi YangiD, Philipp BenneriD, Robert Schöpflin, Na LiiD, Sarah KinkleyiD, Anja Römer-Hillmann, John Longinotto, Steffen HeyneiD, Beate Czepukojc, Sonja M. KessleriD, Alexandra K. KiemeriD, Cristina CadenasiD, Laura ArrigoniiD, Nina Gasparoni, Thomas MankeiD, Thomas PapiD, John Andrew PospisilikiD, Jan Georg HengstleriD, Jörn WalteriD, Sebastiaan H. MeijsingiD, Ho‐Ryun ChungiD, Martin VingroniD
Abstract
We present the software CRUP (Condition-specific Regulatory Units Prediction) to infer from epigenetic marks a list of regulatory units consisting of dynamically changing enhancers with their target genes. The workflow consists of a novel pre-trained enhancer predictor that can be reliably applied across cell lines and species, solely based on histone modification ChIP-seq data. Enhancers are subsequently assigned to different conditions and correlated with gene expression to derive regulatory units. We thoroughly test and then apply CRUP to a rheumatoid arthritis model, identifying enhancer-gene pairs comprising known disease genes as well as new candidate genes.\n\nAvailabilityhttps://github.com/VerenaHeinrich/CRUP
- organism: human
- assay: ChIP-seq
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 32
- FWCI
- 1.41
- Field percentile
- top 19%
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