School of Natural Sciences, University of Lincoln, Brayford Pool, Lincoln LN6 7TS, UK.
AttoGroup Limited, Scottow Enterprise Park, Badersfield, Norwich NR10 5FB, UK.
Genes (Basel). 2024 Aug 28;15(9):1133. doi: 10.3390/genes15091133.
Short Tandem Repeat (STR) testing via capillary electrophoresis is undoubtedly the most popular forensic genetic testing method. However, its low multiplexing capabilities and limited performance with challenging samples are among the factors pushing scientists towards new technologies. Next-generation sequencing (NGS) methods overcome some of these limitations while also enabling the testing of Single-Nucleotide Polymorphisms (SNPs). Nonetheless, these methods are still under optimization, and their adoption into practice is limited. Among the available kits, Thermo Fisher Scientific (Waltham, MA, USA) produces three Precision ID Panels: GlobalFiler NGS STR, Identity, and Ancestry. A clear review of these kits, providing information useful for the promotion of their use, is, however, lacking. To close the gap, a literature review was performed to investigate the popularity, applications, and performance of these kits. Following the PRISMA guidelines, 89 publications produced since 2015 were identified. China was the most active country in the field, and the Identity Panel was the most researched. All kits appeared robust and useful for low-quality and low-quantity samples, while performance with mixtures varied. The need for more population data was highlighted, as well as further research surrounding variables affecting the quality of the sequencing results.
短串联重复序列(STR)检测通过毛细管电泳无疑是最流行的法医遗传学检测方法。然而,其低多重检测能力和对有挑战性样本的有限性能是推动科学家们寻求新技术的因素之一。新一代测序(NGS)方法克服了其中的一些限制,同时也能够检测单核苷酸多态性(SNP)。尽管如此,这些方法仍在优化中,其在实践中的应用受到限制。在现有的试剂盒中,赛默飞世尔科技(美国马萨诸塞州沃尔瑟姆)生产了三种 Precision ID 面板:GlobalFiler NGS STR、Identity 和 Ancestry。然而,对于这些试剂盒,缺乏明确的审查,没有提供有关推广其使用的有用信息。为了弥补这一差距,进行了文献综述,以调查这些试剂盒的普及程度、应用和性能。根据 PRISMA 指南,确定了自 2015 年以来发表的 89 篇出版物。中国是该领域最活跃的国家,而 Identity 面板是研究最多的。所有试剂盒似乎都稳健且适用于低质量和低数量的样本,而混合物的性能则有所不同。强调需要更多的人群数据,以及围绕影响测序结果质量的变量的进一步研究。