META$^\mathbf{2}$: Memory-efficient taxonomic classification and abundance estimation for metagenomics with deep learning

TL;DR

This paper introduces META$^ extbf{2}$, a memory-efficient deep learning approach for taxonomic classification and abundance estimation in metagenomics, leveraging locality-sensitive hashing and multiple instance learning to outperform existing methods.

Contribution

The paper presents a novel memory-efficient deep learning method combining locality-sensitive hashing and MIL for improved taxonomic classification and abundance estimation in metagenomics.

Findings

Outperforms conventional mapping-based methods within fixed memory constraints.

Utilizes MIL with permutation-invariant pooling to exploit co-occurrence patterns.

Achieves higher accuracy in predicting taxa distribution at higher taxonomic ranks.

Abstract

Metagenomic studies have increasingly utilized sequencing technologies in order to analyze DNA fragments found in environmental samples.One important step in this analysis is the taxonomic classification of the DNA fragments. Conventional read classification methods require large databases and vast amounts of memory to run, with recent deep learning methods suffering from very large model sizes. We therefore aim to develop a more memory-efficient technique for taxonomic classification. A task of particular interest is abundance estimation in metagenomic samples. Current attempts rely on classifying single DNA reads independently from each other and are therefore agnostic to co-occurence patterns between taxa. In this work, we also attempt to take these patterns into account. We develop a novel memory-efficient read classification technique, combining deep learning and locality-sensitive hashing. We show that this approach outperforms conventional mapping-based and other deep learning methods for single-read taxonomic classification when restricting all methods to a fixed memory footprint. Moreover, we formulate the task of abundance estimation as a Multiple Instance Learning (MIL) problem and we extend current deep learning architectures with two different types of permutation-invariant MIL pooling layers: a) deepsets and b) attention-based pooling. We illustrate that our architectures can exploit the co-occurrence of species in metagenomic read sets and outperform the single-read architectures in predicting the distribution over taxa at higher taxonomic ranks.