Evidence map›Paper›PMID 34846288›Full record

ArticleMicrobial genomics2021

RFPlasmid: predicting plasmid sequences from short-read assembly data using machine learning.

Linda van der Graaf-van Bloois, Jaap A Wagenaar, Aldert L Zomer

Open access · goldAbstract read
In one paragraph

Article in Microbial genomics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers.

0numbers the graph read from it
0cells of the map it votes in
61citing papers in PubMed
4.9field-weighted citation impact, top 3% of its field
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

61 citing papers in PubMed, 88 citations in OpenAlex.

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  16. Complete genome sequence ofMicrobiology resource announcements · 2025
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  19. Global phylogenomic analysis ofMicrobial genomics · 2025
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  20. Article

1 more citing papers are in PubMed but not listed here.

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

3 authors at 1 institution in 1 country.

Linda van der Graaf-van BlooisFaculty of Veterinary Medicine, Department of Infectious Diseases and Immunology, Utrecht University, Utrecht, The Netherlands.
Jaap A WagenaarFaculty of Veterinary Medicine, Department of Infectious Diseases and Immunology, Utrecht University, Utrecht, The Netherlands.
Aldert L ZomerFaculty of Veterinary Medicine, Department of Infectious Diseases and Immunology, Utrecht University, Utrecht, The Netherlands.
Utrecht University · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Antimicrobial-resistance (AMR) genes in bacteria are often carried on plasmids and these plasmids can transfer AMR genes between bacteria. For molecular epidemiology purposes and risk assessment, it is important to know whether the genes are located on highly transferable plasmids or in the more stable chromosomes. However, draft whole-genome sequences are fragmented, making it difficult to discriminate plasmid and chromosomal contigs. Current methods that predict plasmid sequences from draft genome sequences rely on single features, like

Indexed as

Escherichia coliGenome, BacterialMachine LearningPlasmidsWhole Genome Sequencingantibiotic resistancechromosomemachine learningplasmidwhole-genome sequencing

Identifiers

PMID34846288
PMCPMC8743549
OpenAlexW3215940488

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.