小柯机器人

美国哥伦比亚大学Tal Korem等研究人员合作发现，利用SCRuB建立的污染源模型可改善来自微生物组数据的癌症表型预测。相关论文于2023年3月16日在线发表在《自然—生物技术》杂志上。

研究人员提出了微生物组污染清除的源头追踪（SCRuB），这是一种概率性的计算去污方法，它结合了多个样品和对照的共享信息，以精确识别和清除污染。研究人员在多个数据驱动的模拟和实验中验证了SCRuB的准确性，包括诱导污染，并证明它比最先进的方法平均高出15-20倍。研究人员展示了SCRuB在多个生态系统、数据类型和测序深度方面的稳健性。SCRuB展示了其在微生物组研究中的适用性，它促进了对宿主表型的改进预测，最明显的是利用被净化的肿瘤微生物组数据对黑色素瘤患者的治疗反应进行预测。

据介绍，基于测序的微生物群落分析方法很容易受到污染，这可能会掩盖生物信号或产生假象。使用对照组进行计算去污的方法是常规使用的，但不能最佳地利用不同样品的共享信息，也不能处理仅部分来自于污染或生物材料泄漏到对照组的分类群。

附：英文原文

Title: Contamination source modeling with SCRuB improves cancer phenotype prediction from microbiome data

Author: Austin, George I., Park, Heekuk, Meydan, Yoli, Seeram, Dwayne, Sezin, Tanya, Lou, Yue Clare, Firek, Brian A., Morowitz, Michael J., Banfield, Jillian F., Christiano, Angela M., Peer, Itsik, Uhlemann, Anne-Catrin, Shenhav, Liat, Korem, Tal

Issue&Volume: 2023-03-16

Abstract: Sequencing-based approaches for the analysis of microbial communities are susceptible to contamination, which could mask biological signals or generate artifactual ones. Methods for in silico decontamination using controls are routinely used, but do not make optimal use of information shared across samples and cannot handle taxa that only partially originate in contamination or leakage of biological material into controls. Here we present Source tracking for Contamination Removal in microBiomes (SCRuB), a probabilistic in silico decontamination method that incorporates shared information across multiple samples and controls to precisely identify and remove contamination. We validate the accuracy of SCRuB in multiple data-driven simulations and experiments, including induced contamination, and demonstrate that it outperforms state-of-the-art methods by an average of 15–20 times. We showcase the robustness of SCRuB across multiple ecosystems, data types and sequencing depths. Demonstrating its applicability to microbiome research, SCRuB facilitates improved predictions of host phenotypes, most notably the prediction of treatment response in melanoma patients using decontaminated tumor microbiome data.

DOI: 10.1038/s41587-023-01696-w

Source: https://www.nature.com/articles/s41587-023-01696-w

Nature Biotechnology：《自然—生物技术》，创刊于1996年。隶属于施普林格·自然出版集团，最新IF：68.164
官方网址：https://www.nature.com/nbt/
投稿链接：https://mts-nbt.nature.com/cgi-bin/main.plex