Systematic evaluation of supervised machine learning for sample origin prediction using metagenomic sequencing data.

ABSTRACT:

Background

The advent of metagenomic sequencing provides microbial abundance patterns that can be leveraged for sample origin prediction. Supervised machine learning classification approaches have been reported to predict sample origin accurately when the origin has been previously sampled. Using metagenomic datasets provided by the 2019 CAMDA challenge, we evaluated the influence of variable technical, analytical and machine learning approaches for result interpretation and novel source prediction.

Results

Comparison between 16S rRNA amplicon and shotgun sequencing approaches as well as metagenomic analytical tools showed differences in normalized microbial abundance, especially for organisms present at low abundance. Shotgun sequence data analyzed using Kraken2 and Bracken

SUBMITTER: Chen JC 

PROVIDER: S-EPMC7731568 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature