ArticleBMC genomics2016
Statistical evaluation of methods for identification of differentially abundant genes in comparative metagenomics.
Article in BMC genomics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers.
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.
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.
Who cites it
64 citing papers in PubMed.
- Trial
- Untapped enzymatic potential: dehalogenase- and peroxidase-driven PVC degradation by gene carriers in Tibetan plateau pikas.Microbiome · 2026Article
- Prosystemin-derived signals: bridging leaf microbiome dynamics and defense activation.Environmental microbiome · 2026Article
- Lifestyle associates with unique resistome and microbiome signatures in children.BMC microbiology · 2026Article
- Comprehensive evaluation of statistical approaches for differential metaproteomics.bioRxiv : the preprint server for biology · 2026Article
- Species-Level Comparative Metagenomic Analysis of the Bacterial Abundance of the Gut Microbiome in Psoriasis, Hidradenitis Suppurativa, and Pemphigus Foliaceous Patients Using Shotgun Next-Generation Sequencing.International journal of molecular sciences · 2026Article
- Gut Microbiome Differences Across Mixed-Sex and Female-Only Social Rearing Regimes in Female Field CricketsInsects · 2026Article
- Resolving the evolutionary duality of marine symbionts: redefining the genusISME communications · 2026Article
- Microbial Omics.Progress in molecular and subcellular biology · 2026Review
- PredCMB: predicting changes in microbial metabolites based on the gene-metabolite network analysis of shotgun metagenome data.Bioinformatics (Oxford, England) · 2024Article
- Federated privacy-protected meta- and mega-omics data analysis in multi-center studies with a fully open-source analytic platform.PLoS computational biology · 2024Article
- Upper respiratory microbial communities of healthy populations are shaped by niche and age.Microbiome · 2024Article
- A realistic benchmark for differential abundance testing and confounder adjustment in human microbiome studies.Genome biology · 2024Article
- Rodents consuming the same toxic diet harbor a unique functional core microbiome.Animal microbiome · 2024Article
- Upper respiratory microbial communities of healthy populations are shaped by niche and age.bioRxiv : the preprint server for biology · 2024Article
- Statistical normalization methods in microbiome data with application to microbiome cancer research.Gut microbes · 2023Review
- Role of the gut microbiota and their metabolites in hemodialysis patients.International journal of medical sciences · 2023Article
- llperm: a permutation of regressor residuals test for microbiome data.BMC bioinformatics · 2022Article
- Investigating differential abundance methods in microbiome data: A benchmark study.PLoS computational biology · 2022Review
- Article
4 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMetagenomics is the study of microbial communities by sequencing of genetic material directly from environmental or clinical samples. The genes present in the metagenomes are quantified by annotating and counting the generated DNA fragments. Identification of differentially abundant genes between metagenomes can provide important information about differences in community structure, diversity and biological function. Metagenomic data is however high-dimensional, contain high levels of biological and technical noise and have typically few biological replicates. The statistical analysis is therefore challenging and many approaches have been suggested to date.
resultsIn this article we perform a comprehensive evaluation of 14 methods for identification of differentially abundant genes between metagenomes. The methods are compared based on the power to detect differentially abundant genes and their ability to correctly estimate the type I error rate and the false discovery rate. We show that sample size, effect size, and gene abundance greatly affect the performance of all methods. Several of the methods also show non-optimal model assumptions and biased false discovery rate estimates, which can result in too large numbers of false positives. We also demonstrate that the performance of several of the methods differs substantially between metagenomic data sequenced by different technologies.
conclusionsTwo methods, primarily designed for the analysis of RNA sequencing data (edgeR and DESeq2) together with a generalized linear model based on an overdispersed Poisson distribution were found to have best overall performance. The results presented in this study may serve as a guide for selecting suitable statistical methods for identification of differentially abundant genes in metagenomes.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.