Abstract
Cys2His2 zinc-finger proteins (C2H2-ZFPs) constitute the largest class of human transcription factors (TFs) and also the least characterized one. Determining the DNA sequence preferences of C2H2-ZFPs is an important first step toward elucidating their roles in transcriptional regulation. Among the most promising approaches for obtaining the sequence preferences of C2H2-ZFPs are those that combine machine-learning predictions with in vivo binding maps of these proteins. Here, we provide a protocol and guidelines for predicting the DNA-binding preferences of C2H2-ZFPs from their amino acid sequences using a machine learning-based recognition code. This protocol also describes the tools and steps to combine these predictions with ChIP-seq data to remove inaccuracies, identify the zinc-finger domains within each C2H2-ZFP that engage with DNA in vivo, and pinpoint the genomic binding sites of the C2H2-ZFPs.
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Doğan, B., Najafabadi, H.S. (2018). Computational Methods for Analysis of the DNA-Binding Preferences of Cys2His2 Zinc-Finger Proteins. In: Liu, J. (eds) Zinc Finger Proteins. Methods in Molecular Biology, vol 1867. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-8799-3_2
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DOI: https://doi.org/10.1007/978-1-4939-8799-3_2
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