Dogan, BeratNajafabadi, Hamed S.2024-08-042024-08-042018978-1-4939-8799-3978-1-4939-8798-61064-37451940-6029https://doi.org/10.1007/978-1-4939-8799-3_2https://hdl.handle.net/11616/98453Cys(2)His(2) 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.eninfo:eu-repo/semantics/closedAccessCys(2)His(2) zinc-finger proteinsTranscription factorsDNA motifsChIP-seqMachine learningRecognition codeBook Chapter186715283015581210.1007/978-1-4939-8799-3_22-s2.0-85052703065Q3WOS:000458610500003N/A