Exploration and retrieval of whole-metagenome sequencing samples

TL;DR

This paper introduces a content-based method for exploring and retrieving whole metagenome sequencing samples using a distributed string mining framework to extract informative sequence features, enabling accurate sample comparison.

Contribution

It presents a novel unsupervised approach leveraging distributed string mining to efficiently compare and retrieve metagenomic samples based on sequence content.

Findings

Effective discrimination of different body sites.

Enrichment of diseased samples in query results.

High accuracy in sample comparison.

Abstract

Over the recent years, the field of whole metagenome shotgun sequencing has witnessed significant growth due to the high-throughput sequencing technologies that allow sequencing genomic samples cheaper, faster, and with better coverage than before. This technical advancement has initiated the trend of sequencing multiple samples in different conditions or environments to explore the similarities and dissimilarities of the microbial communities. Examples include the human microbiome project and various studies of the human intestinal tract. With the availability of ever larger databases of such measurements, finding samples similar to a given query sample is becoming a central operation. In this paper, we develop a content-based exploration and retrieval method for whole metagenome sequencing samples. We apply a distributed string mining framework to efficiently extract all informative sequence $k$-mers from a pool of metagenomic samples and use them to measure the dissimilarity between two samples. We evaluate the performance of the proposed approach on two human gut metagenome data sets as well as human microbiome project metagenomic samples. We observe significant enrichment for diseased gut samples in results of queries with another diseased sample and very high accuracy in discriminating between different body sites even though the method is unsupervised. A software implementation of the DSM framework is available at https://github.com/HIITMetagenomics/dsm-framework