0
Article ? AI-assigned paper type based on the abstract. Classification may not be perfect — flag errors using the feedback button. Tier 2 ? Original research — experimental, observational, or case-control study. Direct primary evidence. Gut & Microbiome Policy & Risk Sign in to save

The virtual microbiome: A computational framework to evaluate microbiome analyses

PLoS ONE 2023 9 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count. Score: 40 ? 0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.
Belén Serrano-Antón, Francisco Rodríguez-Ventura, Pere Colomer-Vidal, Riccardo Aiese Cigliano, Clemente F. Arias, Federica Bertocchini

Summary

Researchers created virtual bacterial populations mimicking real microbiome ecology to test the accuracy of standard microbiome analysis pipelines, finding that gaps in genomic databases significantly compromise microbiome characterization reliability — an issue typically overlooked in microbiome studies.

Microbiomes have been the focus of a substantial research effort in the last decades. The composition of microbial populations is normally determined by comparing DNA sequences sampled from those populations with the sequences stored in genomic databases. Therefore, the amount of information available in databanks should be expected to constrain the accuracy of microbiome analyses. Albeit normally ignored in microbiome studies, this constraint could severely compromise the reliability of microbiome data. To test this hypothesis, we generated virtual bacterial populations that exhibit the ecological structure of real-world microbiomes. Confronting the analyses of virtual microbiomes with their original composition revealed critical issues in the current approach to characterizing microbiomes, issues that were empirically confirmed by analyzing the microbiome of Galleria mellonella larvae. To reduce the uncertainty of microbiome data, the effort in the field must be channeled towards significantly increasing the amount of available genomic information and optimizing the use of this information.

Sign in to start a discussion.

More Papers Like This

Article Tier 2

Microbiome differential abundance methods produce different results across 38 datasets

Researchers compared 14 commonly used methods for identifying differentially abundant microbes across 38 microbiome datasets. They found that different methods often produced substantially different results when applied to the same data, with high rates of disagreement between tools. The study highlights that the choice of analytical method can significantly influence microbiome research conclusions and calls for greater standardization in the field.

Article Tier 2

Evaluating bioinformatics pipelines for population‐level inference using environmental DNA

Researchers evaluated twelve bioinformatics pipelines for their ability to reliably infer intraspecific genetic variability from environmental DNA samples, finding that amplification and sequencing errors can substantially inflate estimates of genetic diversity. The study provides guidance on pipeline selection for population-level eDNA analysis.

Article Tier 2

Unveiling the Microbial Realm with VEBA 2.0: A modular bioinformatics suite for end-to-end genome-resolved prokaryotic, (micro)eukaryotic, and viral multi-omics from either short- or long-read sequencing

Researchers introduced VEBA 2.0, an open-source bioinformatics software suite for analyzing complex microbial communities including bacteria, archaea, eukaryotes, and viruses from sequencing data. The tool enables comprehensive microbiome research, which is relevant to understanding how microbial communities interact with environmental contaminants like microplastics.

Article Tier 2

VEBA: a modular end-to-end suite for in silico recovery, clustering, and analysis of prokaryotic, microeukaryotic, and viral genomes from metagenomes

Researchers developed VEBA, a modular bioinformatics software suite that automates end-to-end metagenomic analysis for recovering and characterizing genomes of bacteria, microeukaryotes, and viruses from environmental samples, enabling new biological discoveries from existing datasets.

Article Tier 2

The concept of balance in microbiome research

This essay critically examines how the concept of "balance" is used in microbiome research and medical literature. Researchers analyzed multiple interpretations of what a balanced versus imbalanced microbiome means, finding that the term is often used loosely without precise scientific definition. The study argues for more rigorous conceptual frameworks to better understand how microbiome composition relates to health outcomes.

Share this paper