Pathania Amit
MM Engineering College, Maharishi Markandeshwar University, Mullana-Ambala, Haryana, India.
Methods Mol Biol. 2025;2927:251-258. doi: 10.1007/978-1-0716-4546-8_14.
In human genomic studies, on average, 10% of next-generation sequencing (NGS) reads fail to align with the human reference genome. These unmapped reads vary across samples and have three main potential sources. First, they could represent contamination introduced during sample processing or from the sequencing technology itself. Second, these sequences might originate from microorganisms, like viruses, bacteria, and fungi, that have coevolved with humans and residing within humans. These natural inhabitants of the human body make up the human microbiota. During taking the human cell samples, the microbiota of the surroundings can infect the human samples. Third, these reads could come from active or dormant pathogens residing in the taken human cell samples, like viruses. Research shows that the composition of these microbial species changes with the health and condition of human tissues. In this study, author proposes that unmapped reads may serve as indicators of the pathological state of various tissues and cell types. An outline is marked for experimental approaches to test these ideas and explore the potential of these reads as diagnostic markers.
在人类基因组研究中，平均而言，10%的下一代测序（NGS）读数无法与人类参考基因组比对。这些未映射的读数因样本而异，主要有三个潜在来源。首先，它们可能代表样本处理过程中或测序技术本身引入的污染。其次，这些序列可能源自与人类共同进化并存在于人体内的微生物，如病毒、细菌和真菌。这些人体的天然寄居者构成了人类微生物群。在采集人类细胞样本时，周围环境中的微生物群可能会感染人类样本。第三，这些读数可能来自采集的人类细胞样本中存在的活跃或休眠病原体，如病毒。研究表明，这些微生物物种的组成会随着人体组织的健康状况而变化。在本研究中，作者提出未映射的读数可能作为各种组织和细胞类型病理状态的指标。文中还概述了用于测试这些想法并探索这些读数作为诊断标志物潜力的实验方法。
