Evidence map›Paper›PMID 30155371›Full record

ArticlePeerJ2018

Fine-scale differentiation between

Robert A Petit Iii, James M Hogan, Matthew N Ezewudo, Sandeep J Joseph, Timothy D Read

Open access · goldAbstract read
In one paragraph

Article in PeerJ, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.3field-weighted citation impact, top 42% 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

4 citing papers in PubMed, 11 citations in OpenAlex.

  1. Exploring the hypothetical role ofMicrobiology spectrum · 2026
    Article
  2. Article
  3. UniqueInternational journal of molecular sciences · 2020
    Article
  4. 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

5 authors at 2 institutions in 2 countries.

Robert A Petit IiiDepartment of Medicine, Division of Infectious Diseases, Emory University School of Medicine, Atlanta, GA, United States of America.
James M HoganQueensland University of Technology, Brisbane, Australia.
Matthew N EzewudoDepartment of Medicine, Division of Infectious Diseases, Emory University School of Medicine, Atlanta, GA, United States of America.
Sandeep J JosephDepartment of Medicine, Division of Infectious Diseases, Emory University School of Medicine, Atlanta, GA, United States of America.
Timothy D ReadDepartment of Medicine, Division of Infectious Diseases, Emory University School of Medicine, Atlanta, GA, United States of America.
Emory University · USQueensland University of Technology · AU

Funding

NCI NIH HHS HHSN261201400008C
6 · The paper itself

Abstract

backgroundIt is possible to detect bacterial species in shotgun metagenome datasets through the presence of only a few sequence reads. However, false positive results can arise, as was the case in the initial findings of a recent New York City subway metagenome project. False positives are especially likely when two closely related are present in the same sample.

methodsUsing public complete genome sequence datasets, we identified a set of 31-mer signatures that differentiated

resultsWe found 239,503 DISCUSSION: This work shows how extensive libraries of complete genomes can be used to create organism-specific signatures to help interpret metagenomes. We contrast "specialist" approaches to metagenome analysis such as this work to "generalist" software that seeks to classify all organisms present in the sample and note the more general utility of a k-mer filter approach when taxonomic boundaries lack clarity or high levels of precision are required.

Indexed as

AnthraxBacillus anthracisBacillus cereus groupk-merMetagenomeTyping

Identifiers

PMID30155371
PMCPMC6109372
OpenAlexW2888546384

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.