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- W2912321349 abstract "Prediction of cell type-specific, in vivo transcription factor binding sites is one of the central challenges in regulatory genomics. Here, we present our approach that earned a shared first rank in the “ENCODE-DREAM in vivo Transcription Factor Binding Site Prediction Challenge” in 2017. In post-challenge analyses, we benchmark the influence of different feature sets and find that chromatin accessibility and binding motifs are sufficient to yield state-of-the-art performance. Finally, we provide 682 lists of predicted peaks for a total of 31 transcription factors in 22 primary cell types and tissues and a user-friendly version of our approach, Catchitt, for download." @default.
- W2912321349 created "2019-02-21" @default.
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- W2912321349 date "2019-01-10" @default.
- W2912321349 modified "2023-10-16" @default.
- W2912321349 title "Accurate prediction of cell type-specific transcription factor binding" @default.
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- W2912321349 doi "https://doi.org/10.1186/s13059-018-1614-y" @default.
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