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A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
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Massive metagenomic data analysis using abundance-based machine learning.

Zachary N Harris1, Eliza Dhungel2, Matthew Mosior2

  • 1Department of Biology, Saint Louis University, Saint Louis, MO, 63103, USA.

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|August 3, 2019
PubMed
Summary
This summary is machine-generated.

Machine learning accurately profiles microbial communities in public transit systems. Both read-based and assembly-based methods show high prediction accuracy for metagenomic samples, aiding in sample origin identification.

Keywords:
CAMDAMachine learningMetaSUBMetagenomicsTaxonomy profiling

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Computational Biology

Background:

  • Metagenomics analyzes microbial communities in natural environments.
  • The MetaSUB International Consortium studied subway microbial communities globally.
  • This data was used for an open challenge in data analysis and sample identification.

Purpose of the Study:

  • To distinguish metagenomic profiles across different cities.
  • To accurately predict unknown samples using machine learning.
  • To evaluate read-based and assembly-based analysis approaches.

Main Methods:

  • Utilized machine learning techniques, specifically random forest classifiers.
  • Developed a read-based taxonomy profiling method.
  • Developed a reduced representation assembly-based method.

Main Results:

  • Random forest models achieved 91% accuracy for read-based profiling.
  • Assembly-based models reached 90% accuracy.
  • Both methods showed similar accuracy but struggled with the most abundant label.

Conclusions:

  • Read-based and assembly-based approaches are effective for metagenomics data analysis.
  • Reduced representation assembly-based methods offer high-accuracy prediction.
  • Metagenomic samples can be traced to their location using microbial composition and machine learning.