标题： 基于k-mer的SARS-CoV-2变异体识别方法 显示英文标题

标题： A k-mer Based Approach for SARS-CoV-2 Variant Identification

摘要： 随着新型冠状病毒（COVID-19）在全球的迅速传播及其持续变异，设计一个系统来识别不同的已知（和未知）SARS-CoV-2变种至关重要。 识别特定变种有助于了解和建模它们的传播模式，设计有效的缓解策略，并防止未来的爆发。 它在研究已知疫苗对每个变种的有效性以及建模突破性感染的可能性方面也起着关键作用。 众所周知，刺突蛋白包含了与冠状病毒变种相关的大部分信息/变异。 在本文中，我们使用刺突序列来对人类中的冠状病毒不同变种进行分类。 我们表明，保留氨基酸的顺序有助于底层分类器实现更好的性能。 我们还表明，仅使用少量训练样本（$1\%$的数据）就可以训练我们的模型以超越基线算法。 最后，我们展示了在识别变种中起关键作用的不同氨基酸的重要性，以及它们如何与美国疾病控制与预防中心（CDC）报告的氨基酸相吻合。 显示英文摘要

摘要： With the rapid spread of the novel coronavirus (COVID-19) across the globe and its continuous mutation, it is of pivotal importance to design a system to identify different known (and unknown) variants of SARS-CoV-2. Identifying particular variants helps to understand and model their spread patterns, design effective mitigation strategies, and prevent future outbreaks. It also plays a crucial role in studying the efficacy of known vaccines against each variant and modeling the likelihood of breakthrough infections. It is well known that the spike protein contains most of the information/variation pertaining to coronavirus variants. In this paper, we use spike sequences to classify different variants of the coronavirus in humans. We show that preserving the order of the amino acids helps the underlying classifiers to achieve better performance. We also show that we can train our model to outperform the baseline algorithms using only a small number of training samples ($1\%$ of the data). Finally, we show the importance of the different amino acids which play a key role in identifying variants and how they coincide with those reported by the USA's Centers for Disease Control and Prevention (CDC).