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

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