We are analyzing https://link.springer.com/article/10.1186/s12859-017-1697-6.

Title:
A systematic evaluation of nucleotide properties for CRISPR sgRNA design | BMC Bioinformatics
Description:
Background CRISPR is a versatile gene editing tool which has revolutionized genetic research in the past few years. Optimizing sgRNA design to improve the efficiency of target/DNA cleavage is critical to ensure the success of CRISPR screens. Results By borrowing knowledge from oligonucleotide design and nucleosome occupancy models, we systematically evaluated candidate features computed from a number of nucleic acid, thermodynamic and secondary structure models on real CRISPR datasets. Our results showed that taking into account position-dependent dinucleotide features improved the design of effective sgRNAs with area under the receiver operating characteristic curve (AUC) >0.8, and the inclusion of additional features offered marginal improvement (∼2% increase in AUC). Conclusion Using a machine-learning approach, we proposed an accurate prediction model for sgRNA design efficiency. An R package predictSGRNA implementing the predictive model is available at http://www.ams.sunysb.edu/~pfkuan/softwares.html#predictsgrna .
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Keywords {🔍}

sgrna, model, article, features, google, scholar, feature, results, sequence, genes, pubmed, design, efficiency, dinucleotide, auc, predictive, cas, properties, based, computed, target, tables, datasets, positiondependent, prediction, dataset, additional, spacer, dna, crisprcas, nucleotide, crispr, set, pairwise, correlation, ribosomal, algorithm, file, models, secondary, table, classes, performance, test, position, nonribosomal, azimuth, data, sgrnas, genome,

Topics {✒️}

/nbt/journal/v32/n3/full/nbt crispr-cas9–mediated gene inactivation position-dependent mono-nucleotide composition position-dependent mono-nucleotide models model-based background adjustment position-dependent mono-nucleotide position dependent mono-nucleotide machine-learning approaches similar crispr/cas9 guide efficiency crispr/cas9 knockout screens k-tuple nucleotide frequencies hybrid-ss-min program position-dependent dinucleotide models short synthetic rna position-dependent dinucleotide model evaluate intra-platform consistency silico predictive modeling evaluate inter-platform consistency genome-wide sirna library crispr/cas9 screens datasets single guide rna genome-wide binding crispr endonuclease cas9 =\frac {tp}{tp+fn} =\frac {tn}{tn+fp} cas9 protein acts full size image article download pdf sequence-dependent dna structure machine-learning approach org/content/25/8/1147/suppl/dc1 org/content/343/6166/80/suppl/dc1 genome engineering scott powers crispr-cas9 screens crispr/cas9 screens =1}^{l-j-1}\sum\limits_{ nucleotide composition features mammalian vector-based rnai crispr-cas9 system position-dependent dinucleotide gradient-boosted regression trees full access revolutionized genetic research protospacer adjacent motif protein-dna twist proteins pei fen kuan _{1}={\sum \nolimits }_{ position-dependent sequence

Schema {🗺️}

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         description:CRISPR is a versatile gene editing tool which has revolutionized genetic research in the past few years. Optimizing sgRNA design to improve the efficiency of target/DNA cleavage is critical to ensure the success of CRISPR screens. By borrowing knowledge from oligonucleotide design and nucleosome occupancy models, we systematically evaluated candidate features computed from a number of nucleic acid, thermodynamic and secondary structure models on real CRISPR datasets. Our results showed that taking into account position-dependent dinucleotide features improved the design of effective sgRNAs with area under the receiver operating characteristic curve (AUC) >0.8, and the inclusion of additional features offered marginal improvement (∼2% increase in AUC). Using a machine-learning approach, we proposed an accurate prediction model for sgRNA design efficiency. An R package predictSGRNA implementing the predictive model is available at http://www.ams.sunysb.edu/~pfkuan/softwares.html#predictsgrna .
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      headline:A systematic evaluation of nucleotide properties for CRISPR sgRNA design
      description:CRISPR is a versatile gene editing tool which has revolutionized genetic research in the past few years. Optimizing sgRNA design to improve the efficiency of target/DNA cleavage is critical to ensure the success of CRISPR screens. By borrowing knowledge from oligonucleotide design and nucleosome occupancy models, we systematically evaluated candidate features computed from a number of nucleic acid, thermodynamic and secondary structure models on real CRISPR datasets. Our results showed that taking into account position-dependent dinucleotide features improved the design of effective sgRNAs with area under the receiver operating characteristic curve (AUC) >0.8, and the inclusion of additional features offered marginal improvement (∼2% increase in AUC). Using a machine-learning approach, we proposed an accurate prediction model for sgRNA design efficiency. An R package predictSGRNA implementing the predictive model is available at http://www.ams.sunysb.edu/~pfkuan/softwares.html#predictsgrna .
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         Algorithms
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