Evidence map›Paper›PMID 30405973›Full record

ArticlePeerJ2018

imGLAD: accurate detection and quantification of target organisms in metagenomes.

Juan C Castro, Luis M Rodriguez-R, William T Harvey, Michael R Weigand, Janet K Hatt, Michelle Q Carter, Konstantinos T Konstantinidis

Abstract 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 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing 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

21 citing papers in PubMed.

  1. Trial
  2. Quantitative metagenomics using a portable protocol.Applied and environmental microbiology · 2026
    Article
  3. Microbial Biofilms Dynamics and Functionality in an UrbanEnvironmental science & technology · 2026
    Article
  4. Article
  5. Review
  6. Article
  7. Review
  8. Article
  9. CoverM: read alignment statistics for metagenomics.Bioinformatics (Oxford, England) · 2025
    Article
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  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
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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

7 authors.

Juan C CastroCenter for Bioinformatics and Computational Genomics, Georgia Institute of Technology, Atlanta, GA, United States of America.
Luis M Rodriguez-RCenter for Bioinformatics and Computational Genomics, Georgia Institute of Technology, Atlanta, GA, United States of America.
William T HarveySchool of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, United States of America.
Michael R WeigandSchool of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, United States of America.
Janet K HattSchool of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, United States of America.
Michelle Q CarterProduce Safety and Microbiology, USDA-ARS Western Regional Research Center, US Department of Agriculture, Albany, CA, United States of America.
Konstantinos T KonstantinidisCenter for Bioinformatics and Computational Genomics, Georgia Institute of Technology, Atlanta, GA, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate detection of target microbial species in metagenomic datasets from environmental samples remains limited because the limit of detection of current methods is typically inaccessible and the frequency of false-positives, resulting from inadequate identification of regions of the genome that are either too highly conserved to be diagnostic (e.g., rRNA genes) or prone to frequent horizontal genetic exchange (e.g., mobile elements) remains unknown. To overcome these limitations, we introduce imGLAD, which aims to detect (target) genomic sequences in metagenomic datasets. imGLAD achieves high accuracy because it uses the sequence-discrete population concept for discriminating between metagenomic reads originating from the target organism compared to reads from co-occurring close relatives, masks regions of the genome that are not informative using the MyTaxa engine, and models both the sequencing breadth and depth to determine relative abundance and limit of detection. We validated imGLAD by analyzing metagenomic datasets derived from spinach leaves inoculated with the enteric pathogen

Indexed as

GenomesLimit of detectionMetagenomics

Identifiers

PMID30405973
PMCPMC6216955

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Registered trials

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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.