Metagenomic studies have increasingly utilized sequencing technologies in order to analyze DNA fragments found in environmental samples. It can provide useful insights for studying the interactions between hosts and microbes (Methé et al., 2012;Qin et al., 2010), infectious disease proliferation (Chiu & Miller, 2019), and novel species discovery (Nayfach et al., 2019). One important step in this analysis is the taxonomic classification of those DNA fragments. Of particular interest is the determination of the distribution of the taxa of microbes in metagenomic samples. Recent attempts using deep learning focus on architectures that classify single DNA reads independently from each other. In this work, we attempt to solve the task of directly predicting the distribution over the taxa of whole metagenomic read sets. We formulate this task as a Multiple Instance Learning (MIL) problem. We extend architectures used in single-read taxonomic classification with two different types of permutation-invariant MIL pooling layers: a) deepsets and b) attentionbased pooling. We illustrate that our architecture can exploit the co-occurrence of species in metagenomic read sets and outperforms the single-read architectures in predicting the distribution over the taxa at higher taxonomic ranks.
~The authors propose the addition of multiple instance learning mechanism to existing deep learning models to predict taxonomic labels from metagenomic sequences.~
I appreciate the focus area and importance of the problem the authors have outlined. However, I do not think the authors have achieved the conclusions they mention on page 2, as well as other issues throughout the work. I also think the inclusion of the multiple instance learning framework is incremental and does not provide sufficient benefit.
“A new method to generate synthetic read sets with realistic co-occurence patterns from collections of reference genomes”. I do not think there was any systematic analysis of the parameterization of their generative framework. I would appreciate empirical comparison of previous synthetic read generation techniques to the current proposed framework. Also, there is no comparison of the generative framework to real data. How are the parameters chosen in section 3.1.1? Finally, how the authors propose to alleviate bias of composition of databases? Rare species that may be present in abundance in metagenomic data may be swamped out by more common species sequenced again and again in databases.
“A thorough empirical assessment of our proposed model, showing superior performance in prediction the distributions of higher level taxa from read sets.” A comparison to existing alignment-based methods is absolutely required for this work. The authors of GeNet compare to state-of-art Kraken and Centrifuge, and when reading Rojas-Carulla et al., these models have still performed worse than Kraken and Centrifuge, and that should be reported in your assessment.
A few minor points:
-More description of your neural network architecture is needed. I’m not sure what a “ResNet-like neural network” actually means, and how something built for images deals with sequences. There are also different ResNets with different numbers of parameters.
-Why isn’t a 1D convolutional neural network used to process the input sequences? This would make the sequences translation invariant, and would have a similar effect to working with kmers, where k = convolution width.
-It may be useful to interpret the attention mechanism to understand which reads are likely influencing the decision of taxonomic assignment.