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Use of an Alignment-Free Method for the Geographical Discrimination of GTPVs Based on the GPCR Sequences.

ABSTRACT: Goatpox virus (GTPV) belongs to the genus Capripoxvirus, together with sheeppox virus (SPPV) and lumpy skin disease virus (LSDV). GTPV primarily affects sheep, goats and some wild ruminants. Although GTPV is only present in Africa and Asia, the recent spread of LSDV in Europe and Asia shows capripoxviruses could escape their traditional geographical regions to cause severe outbreaks in new areas. Therefore, it is crucial to develop effective source tracing of capripoxvirus infections. Earlier, conventional phylogenetic methods, based on limited samples, identified three different nucleotide sequence profiles in the G-protein-coupled chemokine receptor (GPCR) gene of GTPVs. However, this method did not differentiate GTPV strains by their geographical origins. We have sequenced the GPCR gene of additional GTPVs and analyzed them with publicly available sequences, using conventional alignment-based methods and an alignment-free approach exploiting k-mer frequencies. Using the alignment-free method, we can now classify GTPVs based on their geographical origin: African GTPVs and Asian GTPVs, which further split into Western and Central Asian (WCA) GTPVs and Eastern and Southern Asian (ESA) GTPVs. This approach will help determine the source of introduction in GTPV emergence in disease-free regions and detect the importation of additional strains in disease-endemic areas.

SUBMITTER: Chibssa TR

PROVIDER: S-EPMC8073112 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundWhile multiple alignment is the first step of usual classification schemes for biological sequences, alignment-free methods are being increasingly used as alternatives when multiple alignments fail. Subword-based combinatorial methods are popular for their low algorithmic complexity (suffix trees ...) or exhaustivity (motif search), in general with fixed length word and/or number of mismatches. We developed previously a method to detect local similarities (the N-local decoding) based on the occurrences of repeated subwords of fixed length, which does not impose a fixed number of mismatches. The resulting similarities are, for some "good" values of N, sufficiently relevant to form the basis of a reliable alignment-free classification. The aim of this paper is to develop a method that uses the similarities detected by N-local decoding while not imposing a fixed value of N. We present a procedure that selects for every position in the sequences an adaptive value of N, and we implement it as the MS4 classification tool.ResultsAmong the equivalence classes produced by the N-local decodings for all N, we select a (relatively) small number of "relevant" classes corresponding to variable length subwords that carry enough information to perform the classification. The parameter N, for which correct values are data-dependent and thus hard to guess, is here replaced by the average repetitivity kappa of the sequences. We show that our approach yields classifications of several sets of HIV/SIV sequences that agree with the accepted taxonomy, even on usually discarded repetitive regions (like the non-coding part of LTR).ConclusionsThe method MS4 satisfactorily classifies a set of sequences that are notoriously hard to align. This suggests that our approach forms the basis of a reliable alignment-free classification tool. The only parameter kappa of MS4 seems to give reasonable results even for its default value, which can be a great advantage for sequence sets for which little information is available.

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Use of an Alignment-Free Method for the Geographical Discrimination of GTPVs Based on the GPCR Sequences.

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