Abstract
Estimating the taxonomic composition of viral sequences in a biological samples processed by next-generation sequencing is an important step in comparative metagenomics. Mapping sequencing reads against a database of known viral reference genomes, however, fails to classify reads from novel viruses whose reference sequences are not yet available in public databases. Instead of a mapping approach, and in order to classify sequencing reads at least to a taxonomic level, the performance of artificial neural networks and other machine learning models was studied. Taxonomic and genomic data from the NCBI database were used to sample labelled sequencing reads as training data. The fitted neural network was applied to classify unlabelled reads of simulated and real-world test sets. Additional auxiliary test sets of labelled reads were used to estimate the conditional class probabilities, and to correct the prior estimation of the taxonomic distribution in the actual test set. Among the taxonomic levels, the biological order of viruses provided the most comprehensive data base to generate training data. The prediction accuracy of the artificial neural network to classify test reads to their viral order was considerably higher than that of a random classification. Posterior estimation of taxa frequencies could correct the primary classification results.
Highlights
Next-generation sequencing (NGS) is regularly used to identify viral sequences in the biological sample of an infected host in order to relate the presence of a virus with disease symptoms of the host [1,2,3]
The taxonomy database containing data needed for taxonomic classification was downloaded from the ftp-server
We found that the artificial neural networks (ANN) models and an support vector machines (SVM) with polynomial kernel performed clearly better than a mapping approach which can not cope with unknown viruses, and better than linear discriminant analysis (LDA) and SVMs with other kernels
Summary
Next-generation sequencing (NGS) is regularly used to identify viral sequences in the biological sample of an infected host in order to relate the presence of a virus with disease symptoms of the host [1,2,3]. Most computational virus detection pipelines or pipelines for determining the taxonomic composition map sequencing reads or assembled contigs against viral reference sequences available in public or own curated databases [6,7,8,9,10,11,12,13]. These mapping approaches have been proven to be successful in a large number of examples, they mostly fail to classify reads from new emerging viruses whose sequences are not yet deposited in a database. Despite improvements of the metagenomic data analysis algorithm, Kraken-2 [17] still shows low sensitivity on novel viruses
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