CIB, Centro de Investigaciones Biológicas Margarita Salas (CSIC), Madrid, Spain.
Sequentia Biotech SL, Barcelona, Spain.
PLoS One. 2023 Feb 8;18(2):e0280391. doi: 10.1371/journal.pone.0280391. eCollection 2023.
Microbiomes have been the focus of a substantial research effort in the last decades. The composition of microbial populations is normally determined by comparing DNA sequences sampled from those populations with the sequences stored in genomic databases. Therefore, the amount of information available in databanks should be expected to constrain the accuracy of microbiome analyses. Albeit normally ignored in microbiome studies, this constraint could severely compromise the reliability of microbiome data. To test this hypothesis, we generated virtual bacterial populations that exhibit the ecological structure of real-world microbiomes. Confronting the analyses of virtual microbiomes with their original composition revealed critical issues in the current approach to characterizing microbiomes, issues that were empirically confirmed by analyzing the microbiome of Galleria mellonella larvae. To reduce the uncertainty of microbiome data, the effort in the field must be channeled towards significantly increasing the amount of available genomic information and optimizing the use of this information.
在过去的几十年里，微生物组一直是大量研究的焦点。微生物种群的组成通常通过将从这些种群中采样的 DNA 序列与基因组数据库中存储的序列进行比较来确定。因此，数据库中可用的信息量应该限制微生物组分析的准确性。尽管在微生物组研究中通常被忽略，但这种限制可能会严重影响微生物组数据的可靠性。为了验证这一假设，我们生成了虚拟细菌种群，这些种群表现出真实世界微生物组的生态结构。将虚拟微生物组的分析与其原始组成进行对比，揭示了当前微生物组特征描述方法中的关键问题，通过分析家蚕幼虫的微生物组，这些问题得到了经验上的证实。为了降低微生物组数据的不确定性，该领域的工作必须集中在显著增加可用基因组信息量和优化该信息的使用上。
