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It remains challenging to predict regulatory variants in particular tissues or cell types due to highly context-specific gene regulation. By connecting large-scale epigenomic profiles to expression quantitative trait loci (eQTLs)

It remains challenging to predict regulatory variants in particular tissues or cell types due to highly context-specific gene regulation. By connecting large-scale epigenomic profiles to expression quantitative trait loci (eQTLs) in a wide range of human tissues/cell types, we identify critical chromatin features that predict variant regulatory potential. We present cepip, a joint likelihood framework, for estimating a variant’s regulatory probability in a context-dependent manner.

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    Date Created
    • 2017-03-16
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  • Text
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    Identifier
    • Digital object identifier: 10.1186/s13059-017-1177-3
    • Identifier Type
      International standard serial number
      Identifier Value
      1474-760X
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    Li, M. J., Li, M., Liu, Z., Yan, B., Pan, Z., Huang, D., . . . Wang, J. (2017). Cepip: context-dependent epigenomic weighting for prioritization of regulatory variants and disease-associated genes. Genome Biology, 18(1). doi:10.1186/s13059-017-1177-3

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