Evidence map›Paper›PMID 30823559›Full record

ReviewMedicines (Basel, Switzerland)2019

New Approaches to Detect Biosynthetic Gene Clusters in the Environment.

Ray Chen, Hon Lun Wong, Brendan Paul Burns

Abstract readReview
In one paragraph

Review in Medicines (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.

0numbers the graph read from it
0cells of the map it votes in
28citing 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

28 citing papers in PubMed.

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  8. Diosgenin producingComputational and structural biotechnology journal · 2025
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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

3 authors.

Ray ChenSchool of Biotechnology and Biomolecular Sciences, The University of New South Wales, Sydney 2052, Australia. ray.chen@student.unsw.edu.au.
Hon Lun WongSchool of Biotechnology and Biomolecular Sciences, The University of New South Wales, Sydney 2052, Australia. h.l.wong@unsw.edu.au.
Brendan Paul BurnsSchool of Biotechnology and Biomolecular Sciences, The University of New South Wales, Sydney 2052, Australia. Brendan.burns@unsw.edu.au.ORCID 0000-0002-2962-2597

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Microorganisms in the environment can produce a diverse range of secondary metabolites (SM), which are also known as natural products. Bioactive SMs have been crucial in the development of antibiotics and can also act as useful compounds in the biotechnology industry. These natural products are encoded by an extensive range of biosynthetic gene clusters (BGCs). The developments in omics technologies and bioinformatic tools are contributing to a paradigm shift from traditional culturing and screening methods to bioinformatic tools and genomics to uncover BGCs that were previously unknown or transcriptionally silent. Natural product discovery using bioinformatics and omics workflow in the environment has demonstrated an extensive distribution of BGCs in various environments, such as soil, aquatic ecosystems and host microbiome environments. Computational tools provide a feasible and culture-independent route to find new secondary metabolites where traditional approaches cannot. This review will highlight some of the advances in the approaches, primarily bioinformatic, in identifying new BGCs, especially in environments where microorganisms are rarely cultured. This has allowed us to tap into the huge potential of microbial dark matter.

Indexed as

antiSMASHbiosynthetic gene clustersnatural productssecondary metabolites

Identifiers

PMID30823559
PMCPMC6473659

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.