Bioinformatic methods for characterization of viral pathogens in metagenomic samples

Abstract

Virus infections impose a huge disease burden on humanity and new viruses are continuously found. As most studies of viral disease are limited to theinvestigation of known viruses, it is important to characterize all circulating viruses. Thus, a broad and unselective exploration of the virus flora would be the most productive development of modern virology. Fueled by the reduction in sequencing costs and the unbiased nature of shotgun sequencing, viral metagenomics has rapidly become the strategy of choice for this exploration. This thesis mainly focuses on improving key methods used in viral metagenomics as well as the complete viral characterization of two sets of samples using these methods. The major methods developed are an efficient automated analysis pipeline for metagenomics data and two novel, more accurate, alignment algorithms for 454 sequencing data. The automated pipeline facilitates rapid, complete and effortless analysis of metagenomics samples, which in turn enables detection of potential pathogens, for instance in patient samples. The two new alignment algorithms developed cover comparisons both against nucleotide and  protein databases, while retaining the underlying 454 data representation. Furthermore, a simulator for 454 data was developed in order to evaluate these methods. This simulator is currently the fastest and most complete simulator of 454 data, which enables further development of algorithms and methods. Finally, we have successfully used these methods to fully characterize a multitude of samples, including samples collected from children suffering from severe lower respiratory tract infections as well as patients diagnosed with chronic fatigue syndrome, both of which presented in this thesis. In these studies, a complete viral characterization has revealed the presence of both expected and unexpected viral pathogens as well as many potential novel viruses