Rocchi Ettore, Magnani Federico, Castellani Gastone, Carusillo Antonio, Tarozzi Martina
Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy.
IRCCS University Hospital of Bologna, Bologna, Italy.
Front Genome Ed. 2025 Jul 24;7:1571023. doi: 10.3389/fgeed.2025.1571023. eCollection 2025.
With the growing number of Cas9 nucleases available to genetic engineers, selecting the most suitable one for a given application can be challenging. A major complication arises from the differing protospacer adjacent motif (PAM) sequence requirements of each Cas9 variant, which makes direct comparisons difficult. To ensure a fair comparison, it is essential to identify common target sites that are not biased by the natural genetic landscape of the chosen target.
To address this challenge, we developed CATS (Comparing Cas9 Activities by Target Superimposition), a novel bioinformatic tool. CATS automates the detection of overlapping PAM sequences across different Cas9 nucleases and identifies allele-specific targets, particularly those arising from pathogenic mutations. One of the key parameters in CATS is the proximity of PAM sites, which helps minimize sequence composition bias. The tool integrates data from continuously updated sources and includes ClinVar information to facilitate the targeting of disease-causing mutations.
CATS significantly reduces the time and effort required for CRISPR/Cas9 experimental design. It streamlines the comparison of Cas9 nucleases with different PAM requirements, enabling researchers to select the most appropriate nuclease for their specific target. The tool's automation, speed, and user-friendly interface make it accessible to researchers regardless of their computational expertise.
By enabling the identification of overlapping PAMs and allele-specific targets, CATS supports the implementation of Cas9-based applications in both research and clinical settings. Its ability to incorporate genetic variants makes it particularly useful for designing therapeutic approaches that selectively target mutated alleles while sparing healthy ones. Ultimately, CATS contributes to the development of more effective and precise genetic therapies.
随着基因工程师可获得的Cas9核酸酶数量不断增加，为特定应用选择最合适的Cas9核酸酶可能具有挑战性。一个主要的复杂因素是每个Cas9变体对原间隔相邻基序（PAM）序列的要求不同，这使得直接比较变得困难。为了确保公平比较，识别不受所选靶标的自然遗传背景影响的共同靶位点至关重要。
为应对这一挑战，我们开发了一种新型生物信息学工具CATS（通过靶标叠加比较Cas9活性）。CATS可自动检测不同Cas9核酸酶之间重叠的PAM序列，并识别等位基因特异性靶标，尤其是那些由致病突变产生的靶标。CATS的关键参数之一是PAM位点的接近程度，这有助于最小化序列组成偏差。该工具整合了来自不断更新来源的数据，并包括ClinVar信息，以促进对致病突变的靶向。
CATS显著减少了CRISPR/Cas9实验设计所需的时间和精力。它简化了对具有不同PAM要求的Cas9核酸酶的比较，使研究人员能够为其特定靶标选择最合适的核酸酶。该工具的自动化、速度和用户友好界面使研究人员无论其计算专业知识如何都能使用。
通过能够识别重叠的PAM和等位基因特异性靶标，CATS支持在研究和临床环境中实施基于Cas9的应用。它纳入遗传变异的能力使其对于设计选择性靶向突变等位基因同时保留健康等位基因的治疗方法特别有用。最终，CATS有助于开发更有效、精确的基因疗法。
