Cargando…

Deep learning improves the ability of sgRNA off-target propensity prediction

BACKGROUND: CRISPR/Cas9 system, as the third-generation genome editing technology, has been widely applied in target gene repair and gene expression regulation. Selection of appropriate sgRNA can improve the on-target knockout efficacy of CRISPR/Cas9 system with high sensitivity and specificity. How...

Descripción completa

Detalles Bibliográficos
Autores principales: Liu, Qiaoyue, Cheng, Xiang, Liu, Gan, Li, Bohao, Liu, Xiuqin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7011380/
https://www.ncbi.nlm.nih.gov/pubmed/32041517
http://dx.doi.org/10.1186/s12859-020-3395-z
_version_ 1783496058010075136
author Liu, Qiaoyue
Cheng, Xiang
Liu, Gan
Li, Bohao
Liu, Xiuqin
author_facet Liu, Qiaoyue
Cheng, Xiang
Liu, Gan
Li, Bohao
Liu, Xiuqin
author_sort Liu, Qiaoyue
collection PubMed
description BACKGROUND: CRISPR/Cas9 system, as the third-generation genome editing technology, has been widely applied in target gene repair and gene expression regulation. Selection of appropriate sgRNA can improve the on-target knockout efficacy of CRISPR/Cas9 system with high sensitivity and specificity. However, when CRISPR/Cas9 system is operating, unexpected cleavage may occur at some sites, known as off-target. Presently, a number of prediction methods have been developed to predict the off-target propensity of sgRNA at specific DNA fragments. Most of them use artificial feature extraction operations and machine learning techniques to obtain off-target scores. With the rapid expansion of off-target data and the rapid development of deep learning theory, the existing prediction methods can no longer satisfy the prediction accuracy at the clinical level. RESULTS: Here, we propose a prediction method named CnnCrispr to predict the off-target propensity of sgRNA at specific DNA fragments. CnnCrispr automatically trains the sequence features of sgRNA-DNA pairs with GloVe model, and embeds the trained word vector matrix into the deep learning model including biLSTM and CNN with five hidden layers. We conducted performance verification on the data set provided by DeepCrispr, and found that the auROC and auPRC in the “leave-one-sgRNA-out” cross validation could reach 0.957 and 0.429 respectively (the Pearson value and spearman value could reach 0.495 and 0.151 respectively under the same settings). CONCLUSION: Our results show that CnnCrispr has better classification and regression performance than the existing states-of-art models. The code for CnnCrispr can be freely downloaded from https://github.com/LQYoLH/CnnCrispr.
format Online
Article
Text
id pubmed-7011380
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-70113802020-02-14 Deep learning improves the ability of sgRNA off-target propensity prediction Liu, Qiaoyue Cheng, Xiang Liu, Gan Li, Bohao Liu, Xiuqin BMC Bioinformatics Methodology Article BACKGROUND: CRISPR/Cas9 system, as the third-generation genome editing technology, has been widely applied in target gene repair and gene expression regulation. Selection of appropriate sgRNA can improve the on-target knockout efficacy of CRISPR/Cas9 system with high sensitivity and specificity. However, when CRISPR/Cas9 system is operating, unexpected cleavage may occur at some sites, known as off-target. Presently, a number of prediction methods have been developed to predict the off-target propensity of sgRNA at specific DNA fragments. Most of them use artificial feature extraction operations and machine learning techniques to obtain off-target scores. With the rapid expansion of off-target data and the rapid development of deep learning theory, the existing prediction methods can no longer satisfy the prediction accuracy at the clinical level. RESULTS: Here, we propose a prediction method named CnnCrispr to predict the off-target propensity of sgRNA at specific DNA fragments. CnnCrispr automatically trains the sequence features of sgRNA-DNA pairs with GloVe model, and embeds the trained word vector matrix into the deep learning model including biLSTM and CNN with five hidden layers. We conducted performance verification on the data set provided by DeepCrispr, and found that the auROC and auPRC in the “leave-one-sgRNA-out” cross validation could reach 0.957 and 0.429 respectively (the Pearson value and spearman value could reach 0.495 and 0.151 respectively under the same settings). CONCLUSION: Our results show that CnnCrispr has better classification and regression performance than the existing states-of-art models. The code for CnnCrispr can be freely downloaded from https://github.com/LQYoLH/CnnCrispr. BioMed Central 2020-02-10 /pmc/articles/PMC7011380/ /pubmed/32041517 http://dx.doi.org/10.1186/s12859-020-3395-z Text en © The Author(s). 2020 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Methodology Article
Liu, Qiaoyue
Cheng, Xiang
Liu, Gan
Li, Bohao
Liu, Xiuqin
Deep learning improves the ability of sgRNA off-target propensity prediction
title Deep learning improves the ability of sgRNA off-target propensity prediction
title_full Deep learning improves the ability of sgRNA off-target propensity prediction
title_fullStr Deep learning improves the ability of sgRNA off-target propensity prediction
title_full_unstemmed Deep learning improves the ability of sgRNA off-target propensity prediction
title_short Deep learning improves the ability of sgRNA off-target propensity prediction
title_sort deep learning improves the ability of sgrna off-target propensity prediction
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7011380/
https://www.ncbi.nlm.nih.gov/pubmed/32041517
http://dx.doi.org/10.1186/s12859-020-3395-z
work_keys_str_mv AT liuqiaoyue deeplearningimprovestheabilityofsgrnaofftargetpropensityprediction
AT chengxiang deeplearningimprovestheabilityofsgrnaofftargetpropensityprediction
AT liugan deeplearningimprovestheabilityofsgrnaofftargetpropensityprediction
AT libohao deeplearningimprovestheabilityofsgrnaofftargetpropensityprediction
AT liuxiuqin deeplearningimprovestheabilityofsgrnaofftargetpropensityprediction