Rational Design of Profile Hidden Markov Models for Viral Classification and Discovery

ABSTRACT

This chapter provides an overview of the theoretical concepts and practical applications of methods for the rational design and application of profile hidden Markov models (profile HMMs) in viral discovery and classification. Profile HMMs are probabilistic models that represent sequence diversity and constitute a very sensitive approach for detecting remote homologs. One of the most relevant and challenging applications of profile HMMs is the discovery of viruses in metagenomic samples, a fundamental task for epidemiological surveillance. In this chapter, publicly available resources of viral profile HMMs are presented, and the methods involved in their construction are discussed. Several aspects to be considered for the generation of profile HMMs are presented, including technical pitfalls that should be avoided, and the potential applications of such models for detecting specific viral sequences. This chapter also introduces a bioinformatics application that implements methods to select informative regions of a multiple sequence alignment and build profile HMMs with different taxonomic specificities. Additional programs using profile HMMs for targeted sequence assembly and detection of multigene entities are also presented. Such programs, integrated into a common framework for viral research, are discussed in light of several biological issues that involve the classification and discovery of potentially emerging viral pathogens.