Y.L. and Y.C. designed the project. Y.L and J.C. processed and analyzed data. S.T. provided conceptual advice and technical expertise. All authors discussed the results and assisted in the preparation of the manuscript. The author(s) read and approved the final manuscript. Corresponding authors Kim HK, Kim Y, Lee S, Min S, Bae JY, Choi JW, et al. SpCas9 activity prediction by DeepSpCas9, a deep learning-based model with high generalization performance. Sci. Adv. 2019;5(11):eaax9249. https://doi.org/10.1126/sciadv.aax9249.

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Part of the Ultra Scape Streetscape System, which guarantees compatibility and reduces split liability Chow RD, Chen JS, Shen J, Chen S. A web tool for the design of prime-editing guide RNAs. Nat. Biomed. Eng. 2021;5(2):190–4. https://doi.org/10.1038/s41551-020-00622-8. The first PE data source was collected from the DeepPE paper [ 17], including 46,614 samples that are generated by high-throughput integration system. This PE data was divided into training and testing sets by the authors and we used the same data splits when building and evaluating the PE2 model.EASYPrime is a polymer modified cementitious slurry primer. The main paving essentials are. EASYPrime is an easy to apply high strength bonding agent for use when laying natural stone and porcelain paving. Clement K, Rees H, Canver MC, Gehrke JM, Farouni R, Hsu JY, et al. CRISPResso2 provides accurate and rapid genome editing sequence analysis. Nat. Biotechnol. 2019;37(3):224–6. https://doi.org/10.1038/s41587-019-0032-3. Yang L, Yang B, Chen J. One prime for all editing. Cell. 2019;179(7):1448–50. https://doi.org/10.1016/j.cell.2019.11.030. Ulirsch JC, Nandakumar SK, Wang L, Giani FC, Zhang X, Rogov P, et al. Systematic functional dissection of common genetic variation affecting red blood cell traits. Cell. 2016;165(6):1530–45. https://doi.org/10.1016/j.cell.2016.04.048.

EASYGrout - A high-quality slurry grout for use on porcelain. EASYGrout - A high-quality slurry grout for use on porcelain.

Easy-Prime applies a machine learning model (i.e., XGboost) that learns important PE design features from multiple published PE data sources to help researchers selecting the best candidate. Installation Had a fantastic experience from start to finish with Scott's, when purchasing porcelain paving and related supplies.To demonstrate the effectiveness of Easy-Prime, we used it to design PEs that can install mutations associated with different traits and diseases [ 25]. We were able to design 136,365 sets of pegRNA and ngRNA to install 89.5% of the 152,351 published GWAS hits (Additional file 1: Fig. S3a). Of those sets, 32,941 are PAM-disrupting edits and 26,873 are predicted to be highly efficient edits (with predicted efficiency of ≥ 40%). For each mutation, an average of 924 sets of pegRNA and ngRNA are searched and optimized (Additional file 1: Fig. S3b). And that’s not all: if you opt for the eDreams Prime Plus subscription plan you will be able to share your discounts with up to 9 people, even when you’re not travelling with them. If you decide to cancel now, keep in mind you will not have the opportunity to start a Prime free trial within the next 12 months. Take your time in trying out Prime before cancelling it. Yin H, Xue W, Anderson DG. CRISPR-Cas: a tool for cancer research and therapeutics. Nat. Rev. Clin. Oncol. 2019;16(5):281–95. https://doi.org/10.1038/s41571-019-0166-8. Li Yichao Chen Jingjing, Tsai Shengdar, Cheng Yong. Easy-Prime: a machine learning–based prime editor design tool. Datasets. Gene Expression Omnibus. 2021 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE175955.

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Lin Q, Zong Y, Xue C, Wang S, Jin S, Zhu Z, et al. Prime genome editing in rice and wheat. Nat. Biotechnol. 2020;38(5):582–5. https://doi.org/10.1038/s41587-020-0455-x.The relationship between RNA folding and PE efficiency has not been fully explored. In our models, the combination of RNA folding related features are the most important and the second most important feature for thePE3 and PE2 systems, which indicates that the pegRNA secondary structure is an important factor for PE. It has been reported that the C-base at the first position in the RTT can pair with G81 in the RNA scaffold [ 8], which affects the proper gRNA structure required for the interaction between Cas9 and gRNA [ 24] and leads to lower PE efficiency. Our model showed that the nucleotides at multiple positions in the RTT are important in predicting PE efficiency. To investigate this association further, we formulated the RNA-folding features as the RNA-folding disruption score, defined as the maximal pairing probability between a position in the RTT and the whole scaffold sequence (Fig. 2c). A higher score indicates a stronger interaction between the nucleotide and the RNA scaffold, which can potentially disrupt the RNA secondary structure. We calculated the correlation between the RNA-folding disruption score and the observed PE efficiency based on the data from original prime editing paper [ 8] for each of the first 16 positions in the RTT (Fig. 2d). The disruption scores for the first five positions showed significant reverse correlation with PE efficiency, indicating that those positions are important for overall PE efficiency. This correlation declines from the sixth to the tenth position and is no longer significant beyond the eleventh position, suggesting that the probability of interaction with scaffold sequences decreases as the distance increases. Optimized PE design by Easy-Prime Oligo features of the PBS and RTT are the sequence length and sequence GC content, which were directly calculated in Python. The PAM-disruption feature is a binary value where 1 means the target mutation disrupts the PAM sequence and 0 means otherwise. PE3b is a binary value where 1 means the spacer of the ngRNA matches to the newly edited sequence and 0 means otherwise. Target mutation features for the numbers of mismatches, insertions, and deletions were computed using skibio ( http://scikit-bio.org).