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Explorative assessment of coronavirus-like short sequences from host-associated and environmental metagenomes.

ABSTRACT: The ongoing COVID-19 pandemic has not only globally caused a high number of causalities, but is also an unprecedented challenge for scientists. False-positive virus detection tests not only aggravate the situation in the healthcare sector, but also provide ground for speculations. Previous studies have highlighted the importance of software choice and data interpretation in virome studies. We aimed to further expand theoretical and practical knowledge in bioinformatics-driven virome studies by focusing on short, virus-like DNA sequences in metagenomic data. Analyses of datasets obtained from different sample types (terrestrial, animal and human related samples) and origins showed that coronavirus-like sequences have existed in host-associated and environmental samples before the current COVID-19 pandemic. In the analyzed datasets, various Betacoronavirus-like sequences were detected that also included SARS-CoV-2 matches. Deepening analyses indicated that the detected sequences are not of viral origin and thus should not be considered in virome profiling approaches. Our study confirms the importance of parameter selection, especially in terms of read length, for reliable virome profiling. Natural environments are an important source of coronavirus-like nucleotide sequences that should be taken into account when virome datasets are analyzed and interpreted. We therefore suggest that processing parameters are carefully selected for SARS-CoV-2 profiling in host related as well as environmental samples in order to avoid incorrect identifications.

SUBMITTER: Mora M

PROVIDER: S-EPMC8222970 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:With the ever-increasing pace of genome sequencing, there is a great need for fast and accurate computational tools to automatically identify genes in these genomes. Although great progress has been made in the development of gene-finding algorithms during the past decades, there is still room for further improvement. In particular, the issue of recognizing short exons in eukaryotes is still not solved satisfactorily. This article is devoted to assessing various linear and kernel-based classification algorithms and selecting the best combination of Z-curve features for further improvement of the issue. Eight state-of-the-art linear and kernel-based supervised pattern recognition techniques were used to identify the short (21-192?bp) coding sequences of human genes. By measuring the prediction accuracy, the tradeoff between sensitivity and specificity and the time consumption, partial least squares (PLS) and kernel partial least squares (KPLS) algorithms were verified to be the most optimal linear and kernel-based classifiers, respectively. A surprising result was that, by making good use of the interpretability of the PLS and the Z-curve methods, 93 Z-curve features were proved to be the best selective combination. Using them, the average recognition accuracy was improved as high as 7.7% by means of KPLS when compared with what was obtained by the Fisher discriminant analysis using 189 Z-curve variables (Gao and Zhang, 2004 ). The used codes are freely available from the following approaches (implemented in MATLAB and supported on Linux and MS Windows): (1) SVM: http://www.support-vector-machines.org/SVM_soft.html. (2) GP: http://www.gaussianprocess.org. (3) KPLS and KFDA: Taylor, J.S., and Cristianini, N. 2004. Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge, UK. (4) PLS: Wise, B.M., and Gallagher, N.B. 2011. PLS-Toolbox for use with MATLAB: ver 1.5.2. Eigenvector Technologies, Manson, WA. Supplementary Material for this article is available at www.liebertonline.com/cmb.

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Explorative assessment of coronavirus-like short sequences from host-associated and environmental metagenomes.

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