<i>Strand</i>

Drew, Jake; Hahsler, Michael

doi:10.1145/2649387.2649436

Cited by 9 publications

(2 citation statements)

References 21 publications

(20 reference statements)

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“…The other phyla had under 15 genomes per phyla, with Verrucomicrobiota having only a single genome. Using the 16S sequences from each genome, we calculated the MASH distance between every pair of genomes, which accounted for multiple unique 16S genes per genome [31, 32]. Core genes were identified using the UBCG2 database, resulting in 34,051,278 genes across the database, roughly averaging to 70 genes per genome.…”

Section: Resultsmentioning

confidence: 99%

Augmenting Bacterial Similarity Measures Using a Graph-Based Genome Representation

Ramanan,

Sarkar

2024

Preprint

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Relationships between bacterial taxa are traditionally defined using 16S rRNA nucleotide similarity or average nucleotide identity. Improvements in sequencing technology provides additional pairwise information on genome sequences, which may provide valuable information on genomic relationships. Mapping orthologous gene locations between genome pairs, known as synteny, is typically implemented in the discovery of new species and has not been systematically applied to bacterial genomes. Using a dataset of 378 bacterial genomes, we developed and tested a new measure of synteny similarity between a pair of genomes, which was scaled onto 16S rRNA distance using covariance matrices. Based on the input gene functions used (i.e., core, antibiotic resistance, and virulence), we observed varying topological arrangements of bacterial relationship networks by applying (1) complete linkage hierarchical clustering and (2) KNN graph structures to syntenic-scaled 16S data. Our metric improved clustering quality comparatively to state-of-the-art ANI metrics while preserving clustering assignments for the highest similarity relationships. Our findings indicate that syntenic relationships provide more granular and interpretable relationships for within-genera taxa compared to pairwise similarity measures, particularly in functional contexts.

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Section: Resultsmentioning

confidence: 99%

Augmenting Bacterial Similarity Measures Using a Graph-Based Genome Representation

Ramanan,

Sarkar

2024

Preprint

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“…The other phyla had under 15 genomes per phyla, with Verrucomicrobiota having only a single genome. Using the 16S sequences from each genome, we calculated the MASH distance between every pair of genomes, which accounted for multiple unique 16S genes per genome ( 30 , 31 ). Core genes were identified using the UBCG2 database, resulting in 34,051,278 genes across the database, roughly averaging 70 genes per genome.…”

Section: Resultsmentioning

confidence: 99%

Augmenting bacterial similarity measures using a graph-based genome representation

Ramanan,

Sarkar

2024

mSystems

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Relationships between bacterial taxa are traditionally defined using 16S rRNA nucleotide similarity or average nucleotide identity. Improvements in sequencing technology provide additional pairwise information on genome sequences, which may provide valuable information on genomic relationships. Mapping orthologous gene locations between genome pairs, known as synteny, is typically implemented in the discovery of new species and has not been systematically applied to bacterial genomes. Using a data set of 378 bacterial genomes, we developed and tested a new measure of synteny similarity between a pair of genomes, which was scaled onto 16S rRNA distance using covariance matrices. Based on the input gene functions used (i.e., core, antibiotic resistance, and virulence), we observed varying topological arrangements of bacterial relationship networks by applying (i) complete linkage hierarchical clustering and (ii) K-nearest neighbor graph structures to synteny-scaled 16S data. Our metric improved clustering quality comparatively to state-of-the-art average nucleotide identity metrics while preserving clustering assignments for the highest similarity relationships. Our findings indicate that syntenic relationships provide more granular and interpretable relationships for within-genera taxa compared to pairwise similarity measures, particularly in functional contexts. IMPORTANCE Given the prevalence and necessity of the 16S rRNA measure in bacterial identification and analysis, this additional analysis adds a functional and synteny-based layer to the identification of relatives and clustering of bacteria genomes. It is also of computational interest to model the bacterial genome as a graph structure, which presents new avenues of genomic analysis for bacteria and their closely related strains and species.

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