What is it about?
Some of the most devastating pandemic diseases have arisen through the transmission of emerging viruses that have not been detected before the tragic consequences of their dissemination. The detection of novel viruses is a challenging task due to the their high evolutionary rates, making the development of new computational approaches of utmost importance. Profile HMMs are a powerful way of modeling sequence diversity and constitute a very sensitive approach to detect emerging viruses. In this review we discuss some important aspects related to the use of profile HMMs in viral discovery, in light of the most recent developments of virology and bioinformatics software.
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Why is it important?
A paradigm of the diagnosis of infectious agents is the fact that conventional serological- and nucleic acid–based assays rely on previously known information on the target of detection. Given the broad diversity and high divergence rate of viruses, currently available assays can hardly be useful for the detection of emergent viruses. Although novel viruses may be quite different from already known viruses, some proteins may still contain conserved motifs. Since good profile HMMs are built from multiple sequence alignments that sample sequence diversity from a variety of viruses, they might potentially detect sequences that have not been sampled, within a reasonable range of divergence. This means that these models could detect a novel virus for the very first time, even if it had never been isolated and characterized. While such approach does not represent a complete disruption of the aforementioned diagnosis paradigm, it may significantly improve our ability to detect emergent viruses, on the one hand, and accelerate the pace of development of new diagnostic tests, on the other. Since this process implies the diagnosis of novel infectious agents, we propose to define it as “de novo diagnosis”.
Perspectives
The development of rational approaches to build profile HMMs can increase our currently available methods to detect novel viruses that are distantly related to currently known viruses. De novo diagnosis has the potential to become a major strategy for epidemiological surveillance, especially in some sensitive locations such as hospitals, sewage treatment stations, animal production facilities, and migratory bird colonies. A routine collection of environmental samples, followed by metagenomics sequencing and screening with a well-established set of viral profile HMMs, could constitute a framework for such strategy.
Arthur Gruber
Universidade de São Paulo
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This page is a summary of: Use of profile hidden Markov models in viral discovery: current insights, Advances in Genomics and Genetics, July 2017, Dove Medical Press,
DOI: 10.2147/agg.s136574.
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