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Data · dataset · 2025

Taxonomic reassessment of genomes from a divergent population of Streptococcus suis by average nucleotide identity analysis

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This dataset provides genomic-level re-identification and taxonomic reassessment of Streptococcus suis out-population strains based on average nucleotide identity (ANI), 16S rRNA gene comparisons, and other phylogenetic markers.

Description

The dataset includes an Excel workbook with eight sheets (Sheet A–H), which together constitute Supplementary Table S1. This table contains detailed information on the studied strains, including their disease association, sequence alignment results (identity, coverage, and length) of 16S rRNA genes against both the S. suis type strain S735 and a database of prokaryotic type strains, ANI and AAI values compared to S735, accession numbers for short-read and long-read data, genome assembly IDs, phylogenetic lineage within the S. suis tree, group classification within the out-population, and cluster medoid status indicating representative strains.

Through comparative genomic analysis, this study established an ANI threshold of 93.17% for defining authentic S. suis, revealing that all 645 genomes from the out-population fell below this cutoff and thus did not belong to S. suis. Further pairwise ANI comparison identified 18 distinct clusters among these strains, leading to the proposal of 12 novel Streptococcus species and the reclassification of six as known species.

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This dataset serves as a valuable resource for researchers studying streptococcal taxonomy, zoonotic pathogens, and bacterial evolution. It enables further investigations into genomic diversity, species delineation, and the evolutionary relationships within the genus Streptococcus.

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Topics

Stated by source
Basic medicine
Inferred from text
Disease 75% · Tabular 65%
Provenance · 1 source records, 11 field assertions
SourceKeyLast seenRaw
DataCite10.5061/dryad.v41ns1s7g14 d agoJSON v1
FieldAssertionExtractorEvidence
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concepts[disease].local:disease:diseaseenrichment · DataCitekeyword-concept-rules@1.0.0title+description (75%)
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concepts[modality].local:modality:tabularenrichment · DataCitekeyword-concept-rules@1.0.0title+description (65%)
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