Supervised Machine Learning Enables Geospatial Microbial Provenance.
Supervised Machine Learning Enables Geospatial Microbial Provenance.
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作者：
最近，具有地理空间元数据的公开宏基因组数据集的增加使得确定来自世界各地的特定位置的微生物指纹成为可能。这种指纹可用于比较环境研究中的微生物生态位，以及在法医科学和公共卫生领域的应用。为了确定环境宏基因组的区域特异性，我们检查了来自MetaSUB联盟数据的4305个散弹枪测序样本-这是最广泛的城市微生物组公共收集，跨越60个不同的城市，30个国家和6大洲。我们能够在分类分类上使用监督机器学习（SML）识别城市特定的微生物指纹，并且我们还比较了10个SML分类器的性能。然后，我们进一步评估了五种精度最高的算法，城市和大陆的精度分别为85-89%到90-94%。此后，我们利用这些结果开发了Cassandra，这是一个基于随机森林的分类器，可以识别生物指示物种，以帮助指纹识别，并可以推断每个站点的高阶微生物相互作用。我们在Tara Oceans数据集（最大的海洋微生物基因组集）上进一步测试了Cassandra算法，该算法对海洋样本位置的分类准确率为83%。这些结果和代码显示了SML方法和Cassandra在海洋和城市环境中识别生物指示物种的实用性，可以帮助指导正在进行的生物示踪、环境监测和微生物取证（MF）工作。
The recent increase in publicly available metagenomic datasets with geospatial metadata has made it possible to determine location-specific, microbial fingerprints from around the world. Such fingerprints can be useful for comparing microbial niches for environmental research, as well as for applications within forensic science and public health. To determine the regional specificity for environmental metagenomes, we examined 4305 shotgun-sequenced samples from the MetaSUB Consortium dataset—the most extensive public collection of urban microbiomes, spanning 60 different cities, 30 countries, and 6 continents. We were able to identify city-specific microbial fingerprints using supervised machine learning (SML) on the taxonomic classifications, and we also compared the performance of ten SML classifiers. We then further evaluated the five algorithms with the highest accuracy, with the city and continental accuracy ranging from 85–89% to 90–94%, respectively. Thereafter, we used these results to develop Cassandra, a random-forest-based classifier that identifies bioindicator species to aid in fingerprinting and can infer higher-order microbial interactions at each site. We further tested the Cassandra algorithm on the Tara Oceans dataset, the largest collection of marine-based microbial genomes, where it classified the oceanic sample locations with 83% accuracy. These results and code show the utility of SML methods and Cassandra to identify bioindicator species across both oceanic and urban environments, which can help guide ongoing efforts in biotracing, environmental monitoring, and microbial forensics (MF).
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DOI：
10.1016/s2666-5247(21)00039-2
发表时间：
2021-04
期刊：
The Lancet. Microbe
影响因子：
--
作者：
Afshinnekoo E;Bhattacharya C;Burguete-García A;Castro-Nallar E;Deng Y;Desnues C;Dias-Neto E;Elhaik E;Iraola G;Jang S;Łabaj PP;Mason CE;Nagarajan N;Poulsen M;Prithiviraj B;Siam R;Shi T;Suzuki H;Werner J;Zambrano MM;Bhattacharyya M;MetaSUB Consortium
通讯作者：
MetaSUB Consortium
影响因子：
7.7
作者：
Beghini F;McIver LJ;Blanco-Míguez A;Dubois L;Asnicar F;Maharjan S;Mailyan A;Manghi P;Scholz M;Thomas AM;Valles-Colomer M;Weingart G;Zhang Y;Zolfo M;Huttenhower C;Franzosa EA;Segata N
通讯作者：
Segata N
影响因子：
7.7
作者：
Cordier, Tristan;Forster, Dominik;Pawlowski, Jan
通讯作者：
Pawlowski, Jan
DOI：
10.1152/ajpgi.00059.2021
发表时间：
2021-08-01
影响因子：
4.5
作者：
Basu, Srijani;Liu, Catherine;Dannenberg, Andrew J.
通讯作者：
Dannenberg, Andrew J.
影响因子：
3
作者：
Couronné R;Probst P;Boulesteix AL
通讯作者：
Boulesteix AL