Evidence map›Paper›PMID 41251845›Full record

ReviewArchives of microbiology2025

Metagenomics and its impact on environmental and therapeutic microbiology.

Sandeep Kaur Saggu, Manoj Kumar, Shiv Kumar

Abstract readReview
PubMed Publisher
In one paragraph

Review in Archives of microbiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

3 authors.

Sandeep Kaur SagguDepartment of Biotechnology, Kanya Maha Vidyalaya, Jalandhar, Punjab, 144004, India.
Manoj KumarDepartment of Microbiology, Guru Nanak Dev University, Amritsar, Punjab, 143005, India.
Shiv KumarDepartment of Microbiology, Guru Gobind Singh Medical College and Hospital, Baba Farid University of Health Sciences, Faridkot, 151203, India. shivkumar1999@gmail.com.ORCID http://orcid.org/0000-0001-8447-6572

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Metagenomics has significantly advanced our understanding of microbial life by enabling the direct analysis of environmental DNA, thereby deciphering the vast microbial dark matter comprising unknown and uncultivable microbial diversity that remains inaccessible through conventional culture-dependent methods. The culture-independent approach provides a comprehensive view of microbial composition, function, and evolution, facilitating discoveries across environmental and clinical domains. Recent developments in high-throughput sequencing, hybrid long-read assemblies, and AI/ML-based genome binning have enhanced our understanding to reconstruct complete genomes, predict metabolic pathways, and engineer microbial consortia. This review summarizes the impact of metagenomics on environmental and therapeutic microbiology, emphasizing its contributions in the field of bioremediation, greenhouse gas mitigation, sustainable agriculture, industrial enzyme discovery, and novel drug development. It further explores metagenomics-driven innovations in pathogen detection, antimicrobial resistance surveillance, and multi-omics integration. Furthermore, it discusses methodological developments, computational challenges, and translational limitations, offering future perspectives for harnessing metagenomic insights in sustainable biotechnology and precision medicine.

Indexed as

BacteriaEnvironmental MicrobiologyMetagenomicsHigh-Throughput Nucleotide SequencingHumansDrug resistanceEnvironmental monitoringIndustrial enzymesMetagenomicsMicrobial dark matterNovel therapeutics

Identifiers

What OpenQuestion holds

Textmetadata
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.