The Use of Informativity in the Development of Robust Metaviromics-based Examinations

Abstract

The field of metagenomics has developed insight into many of the complex microbial communities responsible for maintaining life on this planet. Sequencing efforts often uncover novel genetic content; this is most evident for viral metagenomics, in which upwards of 90% of all sequences demonstrate no sequence similarity with present databases. For the small fraction which can be identified, the top BLAST hit is often posited as being representative of the phage taxon. However, as previous research has shown, the top BLAST hit is sometimes misinterpreted. Furthermore, the appearance of a particular gene homolog is frequently not representative of the presence of the particular taxon in question. To circumvent these limitations, we have developed a new method for the analysis of metaviromic datasets. BLAST hits are weighted, integrating the sequence identity and length of alignments as well as a phylogenetic signal. A genic rather than genomic approach is presented in which each gene is evaluated with respect to its information content. Through this quantifiable metric, predictions of viral community structure can be made with greater confidence. As a proof-of-concept, the approach presented here was implemented and applied to seven metaviromes. While providing a more robust means of evaluating metaviromic data, the tool is versatile and can easily be customized to investigations of any environment or biome.