Abstract
Clinical metagenomics is a powerful diagnostic tool, as it offers an open view into all DNA in a patient's sample. This allows the detection of pathogens that would slip through the cracks of classical specific assays. However, due to this unspecific nature of metagenomic sequencing, a huge amount of unspecific data is generated during the sequencing itself and the diagnosis only takes place at the data analysis stage where relevant sequences are filtered out. Typically, this is done by comparison to reference databases. While this approach has been optimized over the past years and works well to detect pathogens that are represented in the used databases, a common challenge in analysing a metagenomic patient sample arises when no pathogen sequences are found: How to determine whether truly no evidence of a pathogen is present in the data or whether the pathogen's genome is simply absent from the database and the sequences in the dataset could thus not be classified? Here, we present a novel approach to this problem of detecting novel pathogens in metagenomic datasets by classifying the (segments of) proteins encoded by the sequences in the datasets. We train a neural network on the sequences of coding sequences, labeled by taxonomic domain, and use this neural network to predict the taxonomic classification of sequences that can not be classified by comparison to a reference database, thus facilitating the detection of potential novel pathogens.
Highlights
Over the past one and a half decades, Generation Sequencing (NGS) has revolutionized genomics and adjacent fields of research
We have shown that a taxonomic classification on the domain level based on short sections of the amino acid sequence of an organism’s proteins is possible using a transformer neural network without relying on a reference database
We have demonstrated that it is possible to determine the frame of a short DNA sequence within an ORF using a transformer neural network without knowledge of the reference sequence or the usual comparison of a six-frame translation with a protein sequence database
Summary
Over the past one and a half decades, Generation Sequencing (NGS) has revolutionized genomics and adjacent fields of research. Since the introduction of the Roche 454, the first commercially successful NGS machine [1], in 2005, the number of bases in GenBank has grown from about 1010 to almost 1012, at a staggering average rate of 5 × 1010 bases per month—the same number of bases every two months that it had previously taken 22 years to accumulate [2]. This is just the analyzed tip of the iceberg: The Sequence Read Archive (SRA) currently holds over 4 × 1016 bases of raw NGS data [3].
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