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CRUP: a comprehensive framework to predict condition-specific regulatory units

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Ramisch,  Anna
Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Heinrich,  Verena
Gene regulation (Martin Vingron), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Glaser,  Laura V.
Mechanisms of Transcriptional Regulation (Sebastiaan H. Meijsing), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Benner,  Philipp
Gene regulation (Martin Vingron), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Schöpflin,  Robert
Research Group Development & Disease (Head: Stefan Mundlos), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Kinkley,  Sarah
Chromatin Structure and Function (Sarah Kinkley), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Meijsing,  Sebastiaan
Mechanisms of Transcriptional Regulation (Sebastiaan H. Meijsing), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Vingron,  Martin
Gene regulation (Martin Vingron), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Citation

Ramisch, A., Heinrich, V., Glaser, L. V., Fuchs, A., Yang, X., Benner, P., et al. (2019). CRUP: a comprehensive framework to predict condition-specific regulatory units. Genome Biology: Biology for the Post-Genomic Era, 20(1): 227. doi:10.1186/s13059-019-1860-7.


Cite as: https://hdl.handle.net/21.11116/0000-0005-61C5-F
Abstract
We present the software Condition-specific Regulatory Units Prediction (CRUP) 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 types 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.