five

KatharoSeq enables high-throughput microbiome analysis from low-biomass samples

收藏
NIAID Data Ecosystem2026-03-10 收录
下载链接:
https://www.ncbi.nlm.nih.gov/bioproject/PRJEB24007
下载链接
链接失效反馈
官方服务:
资源简介:
Microbiome analyses of low-biomass samples are challenging because of contamination and inefficient amplification, leading many investigators to employ low-throughput methods with minimal controls. We developed a new automated protocol, KatharoSeq (from the Greek katharos, “clean”), that outperforms single tube extractions while processing four times faster. KatharoSeq incorporates positive and negative controls to reveal the whole bacterial community from inputs of as little as 50 cells, and correctly identifies 90.6% (S.E. 0.013) of reads from 500 cells. To demonstrate the broad utility of KatharoSeq, we performed 16S rRNA amplicon (“16S” below) and shotgun metagenome (“shotgun” below) analyses of the Jet Propulsion Lab Spacecraft Assembly Facility, SAF, (n=192, 96), 52 rooms from a Neonatal Intensive Care Unit, NICU, (n=388, 384), and an endangered abalone rearing facility (n=192, 192), obtaining spatially resolved, unique microbiomes, reproducible across hundreds of samples. The SAF, our primary focus, contains thirty two sOTUs (sub-OTUs, defined as exact sequence matches and their inferred variants identified by the deblur algorithm with four (Acinetobacter lwoffi, Paracoccus marcusii, Mycobacterium sp., and Novosphingobium) being present in over 75 % of the samples. Using microbial spatial topography, the most abundant cleanroom contaminant, A. lwoffi, is related to human foot traffic exposure. In the NICU, we predict a patient disease outcome from the built environment, and in the abalone facility, we show that microbial communities reflect the marine environment rather than human input. Consequently, we demonstrate the feasibility and utility of large-scale, low biomass metagenomics analyses using the KatharoSeq protocol.
创建时间:
2017-12-22
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作