Evidence map›Paper›PMID 42491666›Full record

ArticleiMeta2026

microeco 2: A comprehensive R package for downstream analysis of microbiome omics data.

Chi Liu, Xiangzhen Li, Felipe R P Mansoldo, Tong Chen, Fanzheng Meng, Ruixiang Tang, Siyu Zhou, Qinghua Yang, Ruixin Shao, Minjie Yao

Abstract read
In one paragraph

Article in iMeta, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

10 authors.

Chi LiuEngineering Research Center of Soil Remediation of Fujian Province University; College of Resources and Environment, Fujian Agriculture and Forestry University Fuzhou China.ORCID https://orcid.org/0000-0003-4055-8677
Xiangzhen LiEngineering Research Center of Soil Remediation of Fujian Province University; College of Resources and Environment, Fujian Agriculture and Forestry University Fuzhou China.
Felipe R P MansoldoUniversidade Federal do Rio de Janeiro, Instituto de Química, LAGOA-LADETEC, Rio de Janeiro Rio de Janeiro Brazil.
Tong ChenState Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, National Resource Center for Chinese Materia Medica China Academy of Chinese Medical Sciences Beijing China.
Fanzheng MengState Key Laboratory of High-Efficiency Production of Wheat-Maize Double Cropping/College of Agronomy Henan Agricultural University Zhengzhou China.
Ruixiang TangKey Laboratory of Bioresources and Ecoenvironment (Ministry of Education), Sichuan Key Laboratory of Conservation Biology on Endangered Wildlife, College of Life Sciences Sichuan University Chengdu China.
Siyu ZhouState Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute Fudan University Shanghai China.
Qinghua YangState Key Laboratory of High-Efficiency Production of Wheat-Maize Double Cropping/College of Agronomy Henan Agricultural University Zhengzhou China.
Ruixin ShaoState Key Laboratory of High-Efficiency Production of Wheat-Maize Double Cropping/College of Agronomy Henan Agricultural University Zhengzhou China.
Minjie YaoEngineering Research Center of Soil Remediation of Fujian Province University; College of Resources and Environment, Fujian Agriculture and Forestry University Fuzhou China.ORCID https://orcid.org/0000-0001-7501-0519

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Efficient downstream analysis of microbiome data remains a major challenge for researchers. Since its initial release in late 2020, the R microeco package has been widely used for downstream statistical analysis and visualization of omics data, such as amplicon sequencing. Compared with its initial release, the current second version of the microeco package has undergone extensive updates and enhancements. The key upgrades include: (1) The addition of classes for data normalization and machine learning, respectively; (2) The incorporation of additional analytical methods and the addition of functions across various classes; (3) Optimization of the parameter system to expand the applicable scenarios of relevant methods; (4) Code restructuring to enhance the connectivity between statistical analysis and visualization within each class; (5) Extension of certain functions to enable the analysis of abundance data in complex formats generated from bioinformatic analyses of metagenomic/metatranscriptomic data; (6) Incorporation of several analytical methods commonly used in transcriptomic and metabolomic data analyses. Overall, the microeco package 2.0 offers broader method coverage and a wider range of application scenarios compared to the previous version and other existing R packages. The steady growth in user downloads demonstrates that the microeco package, which is built on R6 (a class-based object-oriented programming system for R), has established a broad and active user base. The second version of the microeco R package is open-source and available on the Comprehensive R Archive Network and GitHub (https://github.com/ChiLiubio/microeco).

Indexed as

data normalizationdiversified methodsfunction refactoringmachine learningmetagenomics

Identifiers

PMID42491666
PMCPMC13377413

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.