Evidence map›Paper›PMID 42108288›Full record

ReviewMethods in molecular biology (Clifton, N.J.)2026

A Brief Review of Microbial Omics: Methods and Perspectives.

Chao Yan, Feng Zhang, Cimin Long, Yulong Yin, Leli Wang

Abstract readReview
PubMed Publisher
In one paragraph

Review in Methods in molecular biology (Clifton, N.J.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Chao YanLaboratory of Animal Nutritional Physiology and Metabolic Process, Key Laboratory of Agro-Ecological Processes in Subtropical Region, National Engineering Laboratory for Pollution Control and Waste Utilization in Livestock and Poultry Production, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, China.ORCID https://orcid.org/0000-0003-3066-8258
Feng ZhangLaboratory of Animal Nutritional Physiology and Metabolic Process, Key Laboratory of Agro-Ecological Processes in Subtropical Region, National Engineering Laboratory for Pollution Control and Waste Utilization in Livestock and Poultry Production, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, China.ORCID https://orcid.org/0000-0001-6345-4980
Cimin LongLaboratory of Animal Nutritional Physiology and Metabolic Process, Key Laboratory of Agro-Ecological Processes in Subtropical Region, National Engineering Laboratory for Pollution Control and Waste Utilization in Livestock and Poultry Production, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, China.ORCID https://orcid.org/0009-0001-9245-7088
Yulong YinLaboratory of Animal Nutritional Physiology and Metabolic Process, Key Laboratory of Agro-Ecological Processes in Subtropical Region, National Engineering Laboratory for Pollution Control and Waste Utilization in Livestock and Poultry Production, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, China.ORCID https://orcid.org/0000-0002-0248-6900
Leli WangLaboratory of Animal Nutritional Physiology and Metabolic Process, Key Laboratory of Agro-Ecological Processes in Subtropical Region, National Engineering Laboratory for Pollution Control and Waste Utilization in Livestock and Poultry Production, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, China. leliwang@isa.ac.cn.ORCID https://orcid.org/0000-0002-1590-338X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Microbial omics has progressed from isolated genomic analyses into a comprehensive, integrated multi-omics framework, profoundly advancing our understanding of microbial complexity and functionality. This mini-review systematically outlines the core technologies within microbial omics-including genomics, transcriptomics, proteomics, and metabolomics-by introducing their fundamental principles, common experimental workflows, and state-of-the-art bioinformatic strategies. We particularly highlight the emergence of single-cell microbial omics as a transformative methodology that resolves molecular and functional heterogeneity within communities, enabling the identification of rare taxa, strain-level microdiversity, and specialized functional roles that are obscured in bulk analyses. Furthermore, we discuss how artificial intelligence (AI)-driven tools are revolutionizing the interpretation of high-dimensional omics data, uncovering latent biological patterns, improving predictive modeling of microbial behavior, and facilitating the translation of microbiome insights into clinical and environmental applications. The review concludes by comparing the strengths, limitations, and optimal use cases of each omics layer and single-cell approach while also addressing ongoing technical challenges and future directions in the field.

Indexed as

Computational BiologyGenomicsMetabolomicsMicrobiotaArtificial IntelligenceHumansMultiomicsProteomicsSingle-Cell AnalysisMethodsMicrobial omicsMicrobiotaSingle-cell microbial cell artificial intelligence

Identifiers

PMID42108288

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.