Frequency analysis techniques for identification of viral genetic data

Environmental metagenomic samples and samples obtained as an attempt to identify a pathogen associated with the emergence of a novel infectious disease are important sources of novel microorganisms. The low costs and high throughput of sequencing technologies are expected to allow for the genetic material in those samples to be sequenced and the genomes of the novel microorganisms to be identified by alignment to those in a database of known genomes. Yet, for various biological and technical reasons, such alignment might not always be possible. We investigate a frequency analysis technique which on one hand allows for the identification of genetic material without relying on alignment and on the other hand makes possible the discovery of nonoverlapping contigs from the same organism. The technique is based on obtaining signatures of the genetic data and defining a distance/similarity measure between signatures. More precisely, the signatures of the genetic data are the frequencies of k-mers occurring in them, with k being a natural number. We considered an entropy-based distance between signatures, similar to the Kullback-Leibler distance in information theory, and investigated its ability to categorize negative-sense single-stranded RNA (ssRNA) viral genetic data. Our conclusion is that in this viral context, the technique provides a viable way of discovering genetic relationships without relying on alignment. We envision that our approach will be applicable to other microbial genetic contexts, e.g., other types of viruses, and will be an important tool in the discovery of novel microorganisms.