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Record W3030304545 · doi:10.1186/s12864-020-6765-z

Comparative genomic analysis of 142 bacteriophages infecting Salmonella enterica subsp. enterica

2020· article· en· W3030304545 on OpenAlexafffund
Ruimin Gao, Sohail Naushad, Sylvain Moineau, Roger C. Lévesque, Lawrence Goodridge, Dele Ogunremi

Bibliographic record

VenueBMC Genomics · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicBacteriophages and microbial interactions
Canadian institutionsUniversity of GuelphUniversité LavalMcGill UniversityCanadian Food Inspection Agency
FundersOntario Ministry of Agriculture, Food and Rural AffairsCanadian Food Inspection AgencyUniversity of OttawaGenome CanadaJames Madison University
KeywordsProphageGenomeBiologySalmonella entericaComparative genomicsGeneticsBacterial genome sizeGenomicsSalmonellaGC-contentORFSGenome sizeComputational biologyGeneBacteriophageOpen reading frameEscherichia coliBacteriaPeptide sequence

Abstract

fetched live from OpenAlex

BACKGROUND: Bacteriophages are bacterial parasites and are considered the most abundant and diverse biological entities on the planet. Previously we identified 154 prophages from 151 serovars of Salmonella enterica subsp. enterica. A detailed analysis of Salmonella prophage genomics is required given the influence of phages on their bacterial hosts and should provide a broader understanding of Salmonella biology and virulence and contribute to the practical applications of phages as vectors and antibacterial agents. RESULTS: Here we provide a comparative analysis of the full genome sequences of 142 prophages of Salmonella enterica subsp. enterica which is the full complement of the prophages that could be retrieved from public databases. We discovered extensive variation in genome sizes (ranging from 6.4 to 358.7 kb) and guanine plus cytosine (GC) content (ranging from 35.5 to 65.4%) and observed a linear correlation between the genome size and the number of open reading frames (ORFs). We used three approaches to compare the phage genomes. The NUCmer/MUMmer genome alignment tool was used to evaluate linkages and correlations based on nucleotide identity between genomes. Multiple sequence alignment was performed to calculate genome average nucleotide identity using the Kalgin program. Finally, genome synteny was explored using dot plot analysis. We found that 90 phage genome sequences grouped into 17 distinct clusters while the remaining 52 genomes showed no close relationships with the other phage genomes and are identified as singletons. We generated genome maps using nucleotide and amino acid sequences which allowed protein-coding genes to be sorted into phamilies (phams) using the Phamerator software. Out of 5796 total assigned phamilies, one phamily was observed to be dominant and was found in 49 prophages, or 34.5% of the 142 phages in our collection. A majority of the phamilies, 4330 out of 5796 (74.7%), occurred in just one prophage underscoring the high degree of diversity among Salmonella bacteriophages. CONCLUSIONS: Based on nucleotide and amino acid sequences, a high diversity was found among Salmonella bacteriophages which validate the use of prophage sequence analysis as a highly discriminatory subtyping tool for Salmonella. Thorough understanding of the conservation and variation of prophage genomic characteristics will facilitate their rational design and use as tools for bacterial strain construction, vector development and as anti-bacterial agents.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.888
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.034
GPT teacher head0.261
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations29
Published2020
Admission routes2
Has abstractyes

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