| Chapter title |
Computational Methods for Analysis of the DNA-Binding Preferences of Cys2His2 Zinc-Finger Proteins
|
|---|---|
| Chapter number | 2 |
| Book title |
Zinc Finger Proteins
|
| Published in |
Methods in molecular biology, January 2018
|
| DOI | 10.1007/978-1-4939-8799-3_2 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-8798-6, 978-1-4939-8799-3
|
| Authors |
Berat Doğan, Hamed S. Najafabadi, Doğan, Berat, Najafabadi, Hamed S. |
| 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. |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 8 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 2 | 25% |
| Professor | 1 | 13% |
| Other | 1 | 13% |
| Student > Master | 1 | 13% |
| Student > Ph. D. Student | 1 | 13% |
| Other | 0 | 0% |
| Unknown | 2 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 4 | 50% |
| Computer Science | 1 | 13% |
| Unknown | 3 | 38% |