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PROGNOS / Predicted Report Of Genome-wide Nuclease Off-target Sites
Provides a user-friendly, web-based tool for rapid identification of potential nuclease off-target cleavage sites that can be evaluated using standard molecular biology techniques. The bioinformatics-based ranking algorithms in PROGNOS identify most nuclease off-target cleavage sites found by existing experimental methods. PROGNOS has relatively low false positive ratios and comparable false negative rates to experiment-based predictions, making it a robust method that can be readily implemented by most laboratories. Screening potential target sites using PROGNOS can facilitate the selection of superior nuclease target sites that minimize the number of likely genomic off-target sites. PROGNOS allows nuclease off-target analysis to become a routine component of nuclease design and testing, facilitating the discovery of new off-target sites for ZFNs and TALENs, which expand the off-target database and may improve future versions of the PROGNOS algorithms.
Predicts high throughput of optimal zinc finger protein for 9 bp DNA sequences of choice. ZifNN is inspired from an ensemble machine learning approach, and incorporates the predictions made by 100 parallel neural networks. It assumes synergistic mode of binding, thus capturing the molecular interactions between the DNA sequence and the zinc finger protein (ZFP) helices in greater detail. The tool is capable of domain adaptation, which allows it to make predictions about data points from a domain other than the one used for training the model.
Predicts recognition helices for C2H2 zinc fingers that bind to specific target DNA sites. ZiF-Predict is based on artificial neural network using an exhaustive dataset of zinc finger proteins (ZFP) and their target DNA triplets. It can be useful for researchers interested in designing specific zinc finger transcription factors and zinc finger nucleases for several biological and biomedical applications. The tool offers option for two or three zinc fingers to be predicted either in a modular or synergistic fashion for the input DNA sequence.
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