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Using populations of human and microbial genomes for organism detection in metagenomes

机译:使用人类和微生物基因组群体进行元基因组检测

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Identifying causative disease agents in human patients from shotgun metagenomic sequencing (SMS) presents a powerful tool to apply when other targeted diagnostics fail. Numerous technical challenges remain, however, before SMS can move beyond the role of research tool. Accurately separating the known and unknown organism content remains difficult, particularly when SMS is applied as a last resort. The true amount of human DNA that remains in a sample after screening against the human reference genome and filtering nonbiological components left from library preparation has previously been underreported. In this study, we create the most comprehensive collection of microbial and reference-free human genetic variation available in a database optimized for efficient metagenomic search by extracting sequences from GenBank and the 1000 Genomes Project. The results reveal new human sequences found in individual Human Microbiome Project (HMP) samples. Individual samples contain up to 95% human sequence, and 4% of the individual HMP samples contain 10% or more human reads. Left unidentified, human reads can complicate and slow down further analysis and lead to inaccurately labeled microbial taxa and ultimately lead to privacy concerns as more human genome data is collected.
机译:通过shot弹枪宏基因组测序(SMS)识别人类患者的病原体,是在其他靶向诊断失败时可以应用的强大工具。但是,在SMS超越研究工具的作用之前,仍然存在许多技术挑战。准确地区分已知和未知生物含量仍然很困难,尤其是在最后使用SMS的情况下。以前,人们对人类参考基因组进行筛选并过滤出文库制备过程中遗留的非生物成分后,残留在样品中的人类DNA的真实数量未被报道。在这项研究中,我们通过从GenBank和1000 Genomes Project中提取序列,创建了最全面的微生物和无参考人类遗传变异集合,可在针对有效宏基因组搜索而优化的数据库中找到。结果揭示了在单个人类微生物组计划(HMP)样品中发现的新人类序列。单个样本最多包含95%的人类序列,而4%的单个HMP样本包含10%或更多的人类读数。不明身份的人类读物可能会使复杂的分析复杂化并减慢其速度,并导致标记的微生物分类单元不正确,并最终随着收集更多的人类基因组数据而引起隐私问题。

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