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Microbiome composition and implications for ballast water classification using machine learning.

William A Gerhard1, Claudia K Gunsch1

  • 1Duke University, Department of Civil and Environmental Engineering, 121 Hudson Hall, Durham, NC 27708-0287, United States.

The Science of the Total Environment
|July 22, 2019
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Summary
This summary is machine-generated.

Ballast water microbial communities were analyzed using high-throughput sequencing and machine learning. The 16S rRNA gene with the SILVA database showed the highest accuracy for classifying ballast water samples.

Keywords:
16S rRNA gene18S rRNA geneFungal ITS regionInter-kingdom interactionsIntra-kingdom interactionsMicrobial community correlation

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Environmental Science

Background:

  • Ballast water is a primary vector for the global spread of microorganisms, posing risks to human and environmental health.
  • Monitoring ballast water is crucial for preventing the introduction of invasive species and pathogens.

Purpose of the Study:

  • To investigate bacterial and fungal microbiomes in ballast water using high-throughput sequencing (HTS) and machine learning.
  • To identify associations between different genetic markers (16S and 18S rRNA, ITS) and reference databases (SILVA, UNITE).
  • To evaluate the accuracy of these markers and databases in classifying ballast water based on residence time and location.

Main Methods:

  • High-throughput sequencing (HTS) was employed to analyze the bacterial (16S rRNA) and fungal (18S rRNA, ITS) communities.
  • The SILVA v132 and UNITE reference databases were used for taxonomic assignment.
  • Machine learning models were developed to assess classification accuracy for ballast water properties.

Main Results:

  • A high correlation (0.74) was observed between bacterial communities identified by Silva_16S and fungal communities by UNITE_ITS.
  • Inter-kingdom correlations were more frequent than intra-kingdom correlations (p=0.032).
  • The 16S rRNA gene with the SILVA database achieved significantly higher classification accuracy (0.843) compared to UNITE (0.614) (p<0.001).

Conclusions:

  • The 16S rRNA gene and SILVA database are recommended for future research aiming to classify ballast water by location or residence time.
  • Further research is needed to understand inter-kingdom microbial interactions in ballast water.
  • Curating additional sequencing regions or the UNITE database for aquatic environments could enhance their utility.