GenomicSign: A computational method to discover unique, specific, and amplifiable signatures of target genomic sequences

Molecular diagnostics for the rapid identification of infectious, virulent, and pathogenic organisms are key to health and global security. Such methods rely on the identification and detection of signatures possessed by the organism. In this work, we outline a computational algorithm, GenomicSign, to determine unique and amplifiable genomic signatures of a set of target sequences against a background set of non-target sequences. The set of target sequences might comprise variants of a pathogen of interest, say SARS-CoV2 virus. Unique k-mers of the consensus target sequence for a range of k-values are determined, and the threshold k-value yielding a sharp transition in the number of unique k-mers is identified as k opt . Corresponding unique k-mers for k ≥ k opt are compared against the set of non-target sequences to identify target-specific unique k-mers. A pair of proximal such k-mers could enclose a potential amplicon. Primers to such pairs are designed and scored using a custom scheme to rank the potential amplicons. The top-ranked resulting amplicons are candidates for unique and amplifiable genomic signatures. The entire workflow is demonstrated using a case study with the SARS-CoV2 omicron genome. A case study distinguishing the SARS-CoV2 omicron target strain against non-target other SARS-CoV2 variants is performed to illustrate the workflow. GenomicSign has been implemented in Python and is available as an open-source software under MIT licence ( https://www.github.com/apalania/GenomicSign ).