Unraveling City-Specific Microbial Signatures and Identifying Sample Origins for the Data From CAMDA 2020 Metagenomic Geolocation Challenge

Abstract

The composition of microbial communities has been known to be location-specific. Investigating the microbial composition across different cities enables us to unravel city-specific microbial signatures and further predict the origin of unknown samples. As part of the CAMDA 2020 Metagenomic Geolocation Challenge, MetaSUB provided the whole genome shotgun (WGS) metagenomics data from samples across 28 cities along with non-microbial city data for 23 of these cities. In our solution to this challenge, we implemented feature selection, normalization, clustering and three methods of machine learning to classify the cities based on their microbial compositions. Of the three methods, multilayer perceptron obtained the best performance with an error rate of 19.60% based on whether the correct city received the highest or second highest number of votes for the test data contained in the main dataset. We then trained the model to predict the origins of samples from the mystery dataset by including these samples with the additional group label of “mystery.” The mystery dataset compromised of samples collected from a subset of the cities in the main dataset as well as samples collected from new cities. For samples from cities that belonged to the main dataset, error rates ranged from 18.18 to 72.7%. For samples from new cities that did not belong to the main dataset, 57.7% of the test samples could be correctly labeled as “mystery” samples. Furthermore, we also predicted some of the non-microbial features for the mystery samples from the cities that did not belong to main dataset to draw inferences and narrow the range of the possible sample origins using a multi-output multilayer perceptron algorithm.

Where environmental microbiome meets its host: Subway and passenger microbiome relationships

“…The subway‐related identity of a city has been profiled in different ways by analysing 16S rRNA amplicons and metagenomes (Walker et al, 2018; Zhu et al, 2019). The differences in alpha diversity allow us to predict climatic characteristics of the samples, such as coastal proximity, temperature, and humidity (Zhang et al, 2021). A decrease in diversity has also been observed in cities farther from the equator (Danko et al, 2021).…”

Multiblock partial least squares and rank aggregation: Applications to detection of bacteriophages associated with antimicrobial resistance in the presence of potential confounding factors

“…This challenge exhorts a comprehensive examination of anti‐microbial resistance (AMR) patterns in this vast metagenomic surveillance data, attracting dedicated researchers striving to uncover these complex interactions. Unlike previous studies that have predominantly focused on geolocation prediction 4–6 or spatial modelling 7,8 of such patterns, our research forges a new path by delving into the uncharted territory of bacteriophages' role in orchestrating AMR dissemination. Bacteriophages, also known as phages, are viruses that prey on bacteria 9 .…”