Comparison between non-pulmonary and pulmonary immune responses in a HIV decedent who succumbed to COVID-19

Abstract

Fast mapping of sequencing reads to taxonomic clades is a crucial step in metagenomics, which however raises computational challenges as the numbers of reads and of taxonomic clades increases. Besides alignment-based methods, which are accurate but computational costly, faster compositional approaches have recently been proposed to predict the taxonomic clade of a read based on the set of k-mers it contains. Machine learning-based compositional approaches, in particular, have recently reached accuracies similar to alignment-based models, while being considerably faster. It has been observed that the accuracy of these models increases with the length k of the k-mers they use, however existing methods are limited to handle k-mers of lengths up to k = 12 or 13 because of their large memory footprint needed to store the model coefficients for each possible k-mer. In order to explore the performance of machine learning-based compositional approaches for longer k-mers than currently possible, we propose to reduce the memory footprint of these methods by binning together k-mers that appear together in the sequencing reads used to train the models. We achieve this binning by learning a vector embedding for the vertices of a compacted de Bruijn graph, allowing us to embed any DNA sequence in a low-dimensional vector space where a machine learning system can be trained. The resulting method, which we call Brume, allows us to train compositional machine learning-based models with k-mers of length up to k = 31. We show on two metagenomics benchmark that Brume reaches better performance than previously achieved, thanks to the use of longer k-mers.