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CRISPR off-target effect prediction method based on deep learning

An off-target effect and deep learning technology, applied in the field of bioinformatics, can solve problems such as low efficiency and inability to effectively use genome prior information, and achieve the effect of improving accuracy

Active Publication Date: 2020-06-09
HUNAN UNIV
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Problems solved by technology

However, at present, the prediction of off-target effects of CRISPR mainly adopts artificial methods such as biological information and experimental experience, and the efficiency is very low.
On the other hand, the current automated methods cannot effectively use the prior information of the genome, nor can they deal with the problem of label imbalance in current experimental data.

Method used

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  • CRISPR off-target effect prediction method based on deep learning
  • CRISPR off-target effect prediction method based on deep learning
  • CRISPR off-target effect prediction method based on deep learning

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Embodiment Construction

[0048] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0049] like figure 1 As shown, the steps of the method are described in detail below in conjunction with an example:

[0050] A method for predicting off-target effects of CRISPR based on deep learning, comprising the following steps:

[0051] The first step is to start filtering all the genes of the human genome hg19: In order to avoid the reduction of subsequent prediction accuracy caused by too large pre-trained data, it is necessary to filter out the data irrelevant to the task first, and find out all PAM sequences as NGG in a targeted manner , and then predict the sequence pair whose PAM sequence is NGG;

[0052] The second step is to preprocess the original corpus, which is divided into the following four steps:

[0053] 1. First split the sequence with spaces as intervals;

[0054] 2. Construct sequence sample pairs and randomly combine the two...

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Abstract

The invention belongs to the field of bioinformatics, and discloses a CRISPR off-target effect prediction method based on deep learning. According to the method, information of a human genome is utilized by using a BERT model, priori information of the genome is effectively utilized, the data is effectively reinforced, and obtained characteristics are input to a LightGBM method for training and predication. The problems of small data volume and data imbalance are solved, effective prediction of the CRISPR off-target effect is realized, and the CRISPR off-target effect prediction method has high popularization and application values.

Description

technical field [0001] The invention relates to a method for predicting off-target effects of CRISPR based on deep learning, belonging to the field of bioinformatics. Background technique [0002] The gene editing technology mediated by the CRISPR / Cas9 system is the third-generation "genome fixed-site editing technology" after zinc finger nucleases and transcription activator-like effector nucleases, which can edit and modify DNA sequences at specific positions . In recent years, CRISPR / Cas9 technology has been mainly used in gene knockout, gene knockin, deletion of large DNA fragments, transcription regulation, gene detection, and gene marking. However, there are still many scientific problems to be studied in this technology. For example, CRISPR / Cas9 is a single-chain enzyme, which is inherently unstable and prone to mutations leading to off-target effects. Therefore, overcoming off-target effects and improving genome editing efficiency have become urgent problems for r...

Claims

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Application Information

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IPC IPC(8): G16B15/30G16B30/00G16B40/00
CPCG16B15/30G16B30/00G16B40/00
Inventor 彭绍亮陈东舒文杰李肯立骆嘉伟刘云浩刘凡刘阳辉刘浩
Owner HUNAN UNIV
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