Center for Bio/Molecular Science & Engineering, U.S. Naval Research Laboratory, Washington, USA.
U.S. Department of Agriculture, Riverdale, MD, USA.
Sci Rep. 2023 Apr 20;13(1):6506. doi: 10.1038/s41598-023-33494-4.
The design of minimum CRISPR RNA (crRNA) sets for detection of diverse RNA targets using sequence degeneracy has not been systematically addressed. We tested candidate degenerate Cas13a crRNA sets designed for detection of diverse RNA targets (Lassa virus). A decision tree machine learning (ML) algorithm (RuleFit) was applied to define the top attributes that determine the specificity of degenerate crRNAs to elicit collateral nuclease activity. Although the total number of mismatches (0-4) is important, the specificity depends as well on the spacing of mismatches, and their proximity to the 5' end of the spacer. We developed a predictive algorithm for design of candidate degenerate crRNA sets, allowing improved discrimination between "included" and "excluded" groups of related target sequences. A single degenerate crRNA set adhering to these rules detected representatives of all Lassa lineages. Our general ML approach may be applied to the design of degenerate crRNA sets for any CRISPR/Cas system.
用于检测多种 RNA 靶标的最小 CRISPR RNA（crRNA）集的设计尚未得到系统解决。我们测试了针对不同 RNA 靶标（拉沙病毒）检测而设计的候选简并 Cas13a crRNA 集。决策树机器学习（ML）算法（RuleFit）用于定义确定简并 crRNA 引发旁系核酸酶活性特异性的主要属性。尽管总错配数（0-4）很重要，但特异性也取决于错配的间距及其与间隔区 5'端的接近程度。我们开发了一种候选简并 crRNA 集设计的预测算法，允许在相关靶序列的“包含”和“排除”组之间进行更好的区分。一个遵循这些规则的单一简并 crRNA 集可以检测到所有拉沙病毒谱系的代表。我们的通用 ML 方法可应用于任何 CRISPR/Cas 系统的简并 crRNA 集的设计。
