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sgRNACNN: identifying sgRNA on-target activity in four crops using ensembles of convolutional neural networks

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Abstract

Key message

We proposed an ensemble convolutional neural network model to identify sgRNA high on-target activity in four crops and we used one-hot encoding and k-mers for sequence encoding.

Abstract

As an important component of the CRISPR/Cas9 system, single-guide RNA (sgRNA) plays an important role in gene redirection and editing. sgRNA has played an important role in the improvement of agronomic species, but there is a lack of effective bioinformatics tools to identify the activity of sgRNA in agronomic species. Therefore, it is necessary to develop a method based on machine learning to identify sgRNA high on-target activity. In this work, we proposed a simple convolutional neural network method to identify sgRNA high on-target activity. Our study used one-hot encoding and k-mers for sequence data conversion and a voting algorithm for constructing the convolutional neural network ensemble model sgRNACNN for the prediction of sgRNA activity. The ensemble model sgRNACNN was used for predictions in four crops: Glycine max, Zea mays, Sorghum bicolor and Triticum aestivum. The accuracy rates of the four crops in the sgRNACNN model were 82.43%, 80.33%, 78.25% and 87.49%, respectively. The experimental results showed that sgRNACNN realizes the identification of high on-target activity sgRNA of agronomic data and can meet the demands of sgRNA activity prediction in agronomy to a certain extent. These results have certain significance for guiding crop gene editing and academic research. The source code and relevant dataset can be found in the following link: https://github.com/nmt315320/sgRNACNN.git.

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Code availability

https://github.com/nmt315320/sgRNACNN.git.

Abbreviations

ACC:

accuracy

AUC:

area under curve

BN:

batch normalization

CNN:

convolutional neural networks

LSTM:

long short-term memory

MCC:

Matthews correlation coefficient

NB:

Naïve Bayes

PAM:

protospacer adjacent motif

RF:

random forest

RNN:

recurrent neural networks

SE:

sensitivity

sgRNA:

single-guide RNA

SP:

specificity

SVM:

support vector machine

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Acknowledgements

The authors are very much indebted to the anonymous reviewers, whose constructive comments are very helpful for strengthening the presentation of this paper. The work was supported by the National Natural Science Foundation of China (Nos. 91935302, 61922020, 61771331).

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Authors and Affiliations

  1. Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China

    Mengting Niu & Quan Zou

  2. Department of System Integration, Sparebanken Vest, Bergen, Norway

    Yuan Lin

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  1. Mengting Niu
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  2. Yuan Lin
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Contributions

M.N. conceived the algorithm, carried out analyses, prepared the data sets, carried out experiments, and wrote the manuscript. Y.L.: coordinated the study and project administration. Q.Z.: supervision and funding acquisition. All authors have read and approved the manuscript for submission.

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Correspondence to Yuan Lin or Quan Zou.

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Niu, M., Lin, Y. & Zou, Q. sgRNACNN: identifying sgRNA on-target activity in four crops using ensembles of convolutional neural networks. Plant Mol Biol 105, 483–495 (2021). https://doi.org/10.1007/s11103-020-01102-y

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