ABSTRACT
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.
IMPORTANCE Multiple factors contribute to the emergence of novel infectious diseases. Implementation of effective measures against such diseases relies on the rapid identification of novel pathogens. Another important source of novel microorganisms is environmental metagenomic samples. The low costs and high throughput of sequencing technologies provide a method for the identification of novel microorganisms by sequence alignment. There are several obstacles to this method, as follows: our knowledge of biology is biased by an anthropomorphic view, microbial genomic material could be a minuscule fraction of the sample, the sequencing and enrichment technologies can be a source of errors and biases, and finally, microbes have high diversity and high evolutionary rates. As a result, novel microorganisms could have very low genetic similarity to already known genomes, and the identification by alignment could be computationally prohibitive. We investigate a frequency analysis technique which allows for the identification of novel genetic material without relying on alignment.
GapMind for Carbon Sources: Automated annotations of catabolic pathways
