Evidence map›Paper›PMID 39448688›Full record

ArticleScientific reports2024

Salmonella enterica virulence databases and bioinformatic analysis tools development.

Jing Han, Hailin Tang, Shaohua Zhao, Steven L Foley

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

  1. Article
  2. Deletion of themBio · 2026
    Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Microorganisms · 2025
    Article
  11. Article
  12. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Jing HanDivision of Microbiology, National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR, 72079, USA. jing.han1@fda.hhs.gov.
Hailin TangDivision of Bioinformatics and Biostatistics, National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR, 72079, USA.
Shaohua ZhaoOffice of Applied Science, Center for Veterinary Medicine, Food and Drug Administration, Laurel, MD, 20708, USA.
Steven L FoleyDivision of Microbiology, National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR, 72079, USA. Steven.Foley@fda.hhs.gov.

Funding

U.S. Food and Drug Administration E0765901
6 · The paper itself

Abstract

Salmonella enterica, a prominent foodborne pathogen, contributes significantly to global foodborne illnesses annually. This species exhibits significant genetic diversity, potentially impacting its infectivity, disease severity, and antimicrobial resistance. Whole genome sequencing (WGS) offers comprehensive genetic insights that can be utilized for virulence assessment. However, existing bioinformatic tools for studying Salmonella virulence have notable limitations. To address this gap, a Salmonella Virulence Database with a non-redundant, comprehensive list of putative virulence factors was constructed. Two bioinformatic analysis tools, Virulence Factor Profile Assessment and Virulence Factor Profile Comparison tools, were developed. The former provides data on similarity to the reference genes, e-value, and bite score, while the latter assesses the presence/absence of virulence genes in Salmonella isolates and facilitates comparison of virulence profiles across multiple sequences. To validate the database and associated bioinformatic tools, WGS data from 43,853 Salmonella isolates spanning 14 serovars was extracted from GenBank, and WGS data previously generated in our lab was used. Overall, the Salmonella Virulence database and our bioinformatic tools effectively facilitated virulence assessment, enhancing our understanding of virulence profiles among Salmonella isolates and serovars. The public availability of these resources will empower researchers to assess Salmonella virulence comprehensively, which could inform strategies for pathogen control and risk evaluations associated with human illnesses.

Indexed as

Computational BiologySalmonella entericaVirulence FactorsDatabases, GeneticGenome, BacterialHumansSalmonella InfectionsVirulenceWhole Genome SequencingVirulence FactorsDatabaseSalmonellaVirulence genesWGS analyses tools

Identifiers

PMID39448688
PMCPMC11502889

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.