An Alignment-Independent Approach for the Study of Viral Sequence Diversity at Any Given Rank of Taxonomy Lineage

Abstract

The study of viral diversity is imperative in understanding sequence change and its implications for intervention strategies. The widely used alignment-dependent approaches to study viral diversity are limited in their utility as sequence dissimilarity increases, particularly when expanded to the genus or higher ranks of viral species lineage. Herein, we present an alignment-independent algorithm, implemented as a tool, UNIQmin, to determine the effective viral sequence diversity at any rank of the viral taxonomy lineage. This is done by performing an exhaustive search to generate the minimal set of sequences for a given viral non-redundant sequence dataset. The minimal set is comprised of the smallest possible number of unique sequences required to capture the diversity inherent in the complete set of overlapping k-mers encoded by all the unique sequences in the given dataset. Such dataset compression is possible through the removal of unique sequences, whose entire repertoire of overlapping k-mers can be represented by other sequences, thus rendering them redundant to the collective pool of sequence diversity. A significant reduction, namely ~44%, ~45%, and ~53%, was observed for all reported unique sequences of species Dengue virus, genus Flavivirus, and family Flaviviridae, respectively, while still capturing the entire repertoire of nonamer (9-mer) viral peptidome diversity present in the initial input dataset. The algorithm is scalable for big data as it was applied to ~2.2 million non-redundant sequences of all reported viruses. UNIQmin is open source and publicly available on GitHub. The concept of a minimal set is generic and, thus, potentially applicable to other pathogenic microorganisms of non-viral origin, such as bacteria.

UNIQmin, an alignment-free tool to study viral sequence diversity across taxonomic lineages: a case study of monkeypox virus

“…As per the definition of the minimal set, the k-mer size needs to be defined. Various k-mer sizes may be explored; see Chong et al 2021 for the various considerations. Herein, as an example, we will utilise the k-mer size of nine (9-mer) for immunological applications, such as studying the viral diversity in the context of the cellular immune response (antigenic diversity).…”

Negligible peptidome diversity of SARS-CoV-2 and its higher taxonomic ranks

“…The 27 protein deduplicated datasets were then further compressed, through the removal of unique sequences by use of UNIQmin without incurring any loss of information in terms of the total peptidome repertoire (relevant to the k-mer of choice). The overlapping k-mer size of nine (9-mer or nonamer) was selected for immunological applications (see Chong et al, 2021 for various considerations on k-mer size). The resulting 27 protein minimal datasets totalled 273,851 sequences (~0.5% of the retrieved dataset) (Figure 2(B); minimal datasets are available at https://github.com/ChongLC/UNIQmin_PublicationData/tree/main/ApplicationPaper_SARS-CoV-2).…”

“…Sequence diversity is a major obstacle in the design of effective surveillance and intervention (vaccines, drugs, and diagnostics) strategies against viruses. Primary sequences are treasure troves for studies of sequence diversity, which can be alignment-dependent or alignment-free 1 . Big sequence data poses a major challenge to alignment-dependent approach, which is root to many sequence-based comparison studies.…”

“…Big sequence data poses a major challenge to alignment-dependent approach, which is root to many sequence-based comparison studies. The approach is generally compute-intensive, time-consuming, and of reduced reliability with increase in number and variability of sequences, and thus, constraining its applications 1 . Multiple sequence alignment particularly becomes impractical when expanding the analysis to include all species under a genus, family or higher ranks of the taxonomic lineage.…”

“…Our recently published tool, UNIQmin offers an alignment-free approach for the study of viral sequence diversity at any given rank of taxonomy lineage 1 . The tool performs an exhaustive search to generate a minimal set for a given sequence dataset of interest and is big data ready.…”