Next Generation Sequencing: Signature Sequence Detection For In Silico Primer Design

Identification of bacterial organisms and viruses plays an important role in a multitude of areas such as healthcare, biodefense, research, and food safety. Continually increasing databases of sequence data has made it... [ view full abstract ]

Identification of bacterial organisms and viruses plays an important role in a multitude of areas such as healthcare, biodefense, research, and food safety. Continually increasing databases of sequence data has made it possible to identify regions specific to species, even serotypes of bacteria, as well as various classes of virus. As a part of an internal research project, Noblis examined the possibility of identifying regions of specific genomes that could be used to differentiate them from all other known genomes, including those of closely related organisms.

Rational design of new primers targeting highly discriminatory regions is essential for the effectiveness of diagnostic assays. Most of the existing species-specific assays were designed based on limited sequence information and were gene-specific due to historical or laboratory specific influences. Signature Sequence Domain detection provides a method to supplement these existing assays and can quickly integrate new sequencing data as it becomes available.

To discover such regions, Noblis improved our previously developed tool, BioVelocity, which can create an index comprised of all available reference genomes and enables an exhaustive comparison approach by executing a brute force search method. A new bioinformatics pipeline was implemented for Signature Sequence Domain (SSD) detection; a method designed to compare one organism, either a reference genome or a read set, to all other references available within an index to determine unique k-mers. These k-mers comprise signature sequences for a desired target derived from whole genome sequencing data and can be used to design new primers for PCR-based identification in silico. This approach extracts signature sequences by simultaneously comparing all possible k-mers generated from a chosen target genome with all available reference genomes from which it would be desirable to generate distinct primers. From this one to many comparison, a set of candidate signature sequence regions are identified. These regions can then be used to design primers with high specificity.

The viability of generated primers was confirmed by BLAST validation and evaluation by the open source tool Primer3. These signature sequences were detectible even in very closely related organisms such as different Salmonella serovars. This method has potential in designing specific primers capable of distinguishing between otherwise very similar organisms down to the serovar level. Further research is needed to determine the biological relevance of the signature regions beyond identification. This process can also be used to determine if existing primers are still valid to detect newly sequenced bacterial or viral samples by examining how closely the primers resemble the signature regions of a newer sample.