Challenges in the analysis of viral metagenomes

Rose, Rebecca; Constantinides, Bede; Tapinos, Avraam; Robertson, David L; Prosperi, Mattia

Sequence classification is one of the most studied problems in computational biology, and taxonomic assignment is a key objective of metagenome analysis. All classification methods, to some extent, depend upon detecting similarity between a query sequence and a collection of annotated sequences. Classification may be undertaken using either unassembled reads or the reconstructed contigs arising from the assembly process. The computational requirements of available approaches vary dramatically according to their ability to detect homology in divergent sequences; for example, exact k-mer matching approaches permit rapid sequence classification, yet typically struggle to identify divergent sequences of viral origin, while high-sensitivity protein alignment searches may be prohibitively slow, especially in application to entire sequencing datasets. Some of the more contemporary and speed-optimized taxonomic assignment approaches also have high RAM requirements, limiting scope for their use with readily available computer hardware. The output of sequence homology search tools is not itself easily interpreted, requiring post-processing in order to yield meaningful classifications. Retroactive taxonomic assignment using these results is non-trivial, requiring additional database lookups, for example, for determination of a conservative 'lowest common ancestor' (LCA) taxon shared by all matches for each query sequence. This kind of complexity necessitates the need for the integration of different tools within application-specific 'pipelines'.