PMID- 35898910
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20220729
IS  - 1664-302X (Print)
IS  - 1664-302X (Electronic)
IS  - 1664-302X (Linking)
VI  - 13
DP  - 2022
TI  - Machine Learning Prediction of Nitrification From Ammonia- and Nitrite-Oxidizer 
      Community Structure.
PG  - 899565
LID - 10.3389/fmicb.2022.899565 [doi]
LID - 899565
AB  - Accurately modeling nitrification and understanding the role specific ammonia- or 
      nitrite-oxidizing taxa play in it are of great interest and importance to 
      microbial ecologists. In this study, we applied machine learning to 16S rRNA 
      sequence and nitrification potential data from an experiment examining 
      interactions between cropping systems and rhizosphere on microbial community 
      assembly and nitrogen cycling processes. Given the high dimensionality of 
      microbiome datasets, we only included nitrifers since only a few taxa are capable 
      of ammonia and nitrite oxidation. We compared the performance of linear and 
      nonlinear algorithms with and without qPCR measures of bacterial and archaea 
      ammonia monooxygenase subunit A (amoA) gene abundance. Our feature selection 
      process facilitated the identification of taxons that are most predictive of 
      nitrification and to compare habitats. We found that Nitrosomonas and Nitrospirae 
      were more frequently identified as important predictors of nitrification in 
      conventional systems, whereas Thaumarchaeota were more important predictors in 
      diversified systems. Our results suggest that model performance was not 
      substantively improved by incorporating additional time-consuming and expensive 
      qPCR data on amoA gene abundance. We also identified several clades of nitrifiers 
      important for nitrification in different cropping systems, though we were unable 
      to detect system- or rhizosphere-specific patterns in OTU-level biomarkers for 
      nitrification. Finally, our results highlight the inherent risk of combining data 
      from disparate habitats with the goal of increasing sample size to avoid 
      overfitting models. This study represents a step toward developing machine 
      learning approaches for microbiome research to identify nitrifier ecotypes that 
      may be important for distinguishing ecotypes with defining roles in different 
      habitats.
CI  - Copyright (c) 2022 Lee, Amini, Hu and Halverson.
FAU - Lee, Conard
AU  - Lee C
AD  - Interdepartmental Microbiology Graduate Program, Iowa State University, Ames, IA, 
      United States.
AD  - Department of Plant Pathology and Microbiology, Iowa State University, Ames, IA, 
      United States.
FAU - Amini, Fatemeh
AU  - Amini F
AD  - Department of Industrial and Manufacturing Engineering, Iowa State University, 
      Ames, IA, United States.
FAU - Hu, Guiping
AU  - Hu G
AD  - Department of Industrial and Manufacturing Engineering, Iowa State University, 
      Ames, IA, United States.
FAU - Halverson, Larry J
AU  - Halverson LJ
AD  - Interdepartmental Microbiology Graduate Program, Iowa State University, Ames, IA, 
      United States.
AD  - Department of Plant Pathology and Microbiology, Iowa State University, Ames, IA, 
      United States.
LA  - eng
PT  - Journal Article
DEP - 20220711
PL  - Switzerland
TA  - Front Microbiol
JT  - Frontiers in microbiology
JID - 101548977
PMC - PMC9309558
OTO - NOTNLM
OT  - ammonia-oxidizers
OT  - machine learning
OT  - microbiome
OT  - nitrification
OT  - nitrifiers
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2022/07/29 06:00
MHDA- 2022/07/29 06:01
PMCR- 2022/07/11
CRDT- 2022/07/28 02:17
PHST- 2022/03/18 00:00 [received]
PHST- 2022/06/02 00:00 [accepted]
PHST- 2022/07/28 02:17 [entrez]
PHST- 2022/07/29 06:00 [pubmed]
PHST- 2022/07/29 06:01 [medline]
PHST- 2022/07/11 00:00 [pmc-release]
AID - 10.3389/fmicb.2022.899565 [doi]
PST - epublish
SO  - Front Microbiol. 2022 Jul 11;13:899565. doi: 10.3389/fmicb.2022.899565. 
      eCollection 2022.