Evidence map›Paper›PMID 31849328›Full record

ArticleGenome biology2019

Within-species contamination of bacterial whole-genome sequence data has a greater influence on clustering analyses than between-species contamination.

Arthur W Pightling, James B Pettengill, Yu Wang, Hugh Rand, Errol Strain

Open access · goldAbstract read
In one paragraph

Article in Genome biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
1.1field-weighted citation impact, top 22% 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

14 citing papers in PubMed, 22 citations in OpenAlex.

  1. Article
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  3. Genomic insights intoFrontiers in microbiology · 2026
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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

5 authors at 1 institution in 1 country.

Arthur W PightlingCenter for Food Safety and Applied Nutrition, US Food and Drug Administration, College Park, MD, USA. Arthur.Pightling@fda.hhs.gov.ORCID 0000-0001-8252-6652
James B PettengillCenter for Food Safety and Applied Nutrition, US Food and Drug Administration, College Park, MD, USA.
Yu WangCenter for Food Safety and Applied Nutrition, US Food and Drug Administration, College Park, MD, USA.
Hugh RandCenter for Food Safety and Applied Nutrition, US Food and Drug Administration, College Park, MD, USA.
Errol StrainCenter for Food Safety and Applied Nutrition, US Food and Drug Administration, College Park, MD, USA.
Center for Food Safety and Applied Nutrition · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Although it is assumed that contamination in bacterial whole-genome sequencing causes errors, the influences of contamination on clustering analyses, such as single-nucleotide polymorphism discovery, phylogenetics, and multi-locus sequencing typing, have not been quantified. By developing and analyzing 720 Listeria monocytogenes, Salmonella enterica, and Escherichia coli short-read datasets, we demonstrate that within-species contamination causes errors that confound clustering analyses, while between-species contamination generally does not. Contaminant reads mapping to references or becoming incorporated into chimeric sequences during assembly are the sources of those errors. Contamination sufficient to influence clustering analyses is present in public sequence databases.

Indexed as

DNA ContaminationGenome, BacterialWhole Genome SequencingCluster AnalysisEscherichia coliListeria monocytogenesSalmonella entericaClustering analysesComparative genomicsContaminationEscherichia coliListeria monocytogenesMLSTMulti-locus sequence typingPhylogeneticsSalmonella entericaSingle-nucleotide polymorphismSNPWhole-genome sequencing

Identifiers

PMID31849328
PMCPMC6918607
OpenAlexW2996464444

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.