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使用语言模型在组织特异性转录组中预测CRISPR基编辑器的RNA离目标的风险预测

Kazuki Nakamae¹, Takayuki Suzuki², Sora Yonezawa²

¹Genome Editing Innovation Center, Hiroshima University, Higashi-Hiroshima 739-0046, Japan.

International journal of molecular sciences

|February 26, 2025

概括

我们开发了PiCTURE,这是一个计算工具,可以检测来自细胞基编辑器 (CBE) 的意外RNA编辑. PiCTURE中的机器学习模型提高了准确性,帮助开发更安全的基因组编辑疗法.

科学领域:

基因组学就是基因组学.
分子生物学分子生物学
生物信息学是一种生物信息学.

背景情况:

细胞酶基编辑器 (CBEs) 提供精确的基因编辑,没有双链断裂.
无意的RNA非标效应是CBEs的一个重大问题,研究不足.

研究的目的:

开发一个标准化的计算管道,PiCTURE,用于检测和量化CBE诱导的RNA目标外事件.
为了识别正规和非正规的RNA目标外基因.
创建机器学习模型来预测RNA目标外风险.

主要方法:

开发了CRISPR诱导的全转录组非故意RNA编辑 (PiCTURE) 的管道.
识别了正规的ACW和更广泛的WCWRNA目标外基因.
训练了基于DNABERT-2的机器学习模型 (STL,SNL) 进行非目标预测.
与PROTECTiO管道集成的PiCTURE用于组织特异性风险评估.

主要成果:

PiCTURE有效地检测和量化转录组范围内的CBE诱导的RNA脱事件.
机器学习模型在准确性和回忆方面明显优于仅基于动机的方法.
组织特异性分析显示了不同的非目标风险,结肠和肺部的负担高于大脑和卵巢.

结论:

PiCTURE提供了一个全面的框架,用于基准编辑中的RNA目标外分析.
先进的机器学习分类器对于评估CBE安全至关重要.
这些发现为开发更安全的基因组编辑疗法提供了洞察力.

关键词:

克里斯普尔是什么意思?克里斯普尔是什么意思?保护保护保护图片图片图片脱基RNA的非目标效应基础编辑基础编辑机器学习是机器学习.组织特异性的转录组.

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