META$^\mathbf{2}$: Memory-efficient taxonomic classification and abundance estimation for metagenomics with deep learning
This work addresses memory efficiency and co-occurrence pattern utilization in metagenomic analysis, offering incremental improvements for researchers in genomics and bioinformatics.
The authors tackled the problem of memory-intensive taxonomic classification and abundance estimation in metagenomics by developing a memory-efficient deep learning method that uses locality-sensitive hashing and Multiple Instance Learning with permutation-invariant pooling layers. They showed that their approach outperforms conventional and other deep learning methods in classification under fixed memory constraints and improves abundance estimation by exploiting co-occurrence patterns, achieving better performance at higher taxonomic ranks.
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.