Evidence map›Paper›PMID 31387857›Full record

ArticleG3 (Bethesda, Md.)2019

IslandCafe: Compositional Anomaly and Feature Enrichment Assessment for Delineation of Genomic Islands.

Mehul Jani, Rajeev K Azad

Abstract read
In one paragraph

Article in G3 (Bethesda, Md.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Genomic Island Prediction via Chi-Square Test and Random Forest Algorithm.Computational and mathematical methods in medicine · 2021
    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

2 authors.

Mehul JaniDepartment of Biological Sciences and BioDiscovery Institute and.ORCID 0000-0002-2486-5696
Rajeev K AzadDepartment of Biological Sciences and BioDiscovery Institute and Rajeev.Azad@unt.edu.ORCID 0000-0002-6874-6146

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

One of the evolutionary forces driving bacterial genome evolution is the acquisition of clusters of genes through horizontal gene transfer (HGT). These genomic islands may confer adaptive advantages to the recipient bacteria, such as, the ability to thwart antibiotics, become virulent or hypervirulent, or acquire novel metabolic traits. Methods for detecting genomic islands either search for markers or features typical of islands or examine anomaly in oligonucleotide composition against the genome background. The former tends to underestimate, missing islands that have the markers either lost or degraded, while the latter tends to overestimate, due to their inability to discriminate compositional atypicality arising because of HGT from those that are a consequence of other biological factors. We propose here a framework that exploits the strengths of both these approaches while bypassing the pitfalls of either. Genomic islands lacking markers are identified by their association with genomic islands with markers. This was made possible by performing marker enrichment and phyletic pattern analyses within an integrated framework of recursive segmentation and clustering. The proposed method, IslandCafe, compared favorably with frequently used methods for genomic island detection on synthetic test datasets and on a test-set of known islands from 15 well-characterized bacterial species. Furthermore, IslandCafe identified novel islands with imprints of likely horizontal acquisition.

Indexed as

Genome, BacterialGenomic IslandsAlgorithmsBacteriaComputational BiologyDNA Transposable ElementsEvolution, MolecularGene Transfer, HorizontalGenomicsReproducibility of ResultsDNA Transposable Elementsclusteringgenomic islandhorizontal gene transfermobile elementssegmentation

Identifiers

PMID31387857
PMCPMC6778810

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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.