Microbial Bioactives
Microbial Bioactives | Online ISSN 2209-2161
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Volume 6 Number 1 2023
REVIEWS (Open Access)
Unmasking Bias in Microbiome Research: A Human-Centered Introduction to Methodological Pitfalls and Meta-Analytic Insights
Most Farhana Akter 1, Md. Robiul Islam 1, Shahadat Hossain 2*
Microbial Bioactives 6 (1) 1-14 https://doi.org/10.25163/microbbioacts.6110672
Submitted: 05 February 2023 Revised: 24 March 2023 Accepted: 06 April 2023 Published: 08 April 2023
Abstract
Microbiome research has rapidly transformed modern biology, offering unprecedented insight into the hidden microbial communities that shape ecosystems, agriculture, and human health. Yet, despite remarkable technological progress, an uncomfortable reality persists: microbiome datasets are often as much a reflection of methodological decisions as they are of true biological variation. This systematic review and meta-analysis critically examines how technical choices across the microbiome workflow influence microbial diversity estimates and community interpretation. Evidence synthesized from studies spanning marine systems, freshwater sediments, host-associated microbiomes, and environmental surveillance revealed that sampling strategies, DNA extraction methods, primer selection, sequencing platforms, and bioinformatic pipelines introduce substantial and frequently systematic biases. Quantitative analyses demonstrated that methodological variability can rival, and occasionally exceed, the magnitude of biological effects. Full-length sequencing approaches consistently recovered greater species richness than short-read methods, while primer-dependent amplification biases led to taxon-specific underrepresentation, including ecologically and clinically relevant microorganisms. Extraction protocols similarly displayed uneven performance across bacterial and eukaryotic taxa. Funnel and forest plot analyses further highlighted considerable methodological heterogeneity among studies. Although advances such as amplicon sequence variants and standardized reporting frameworks have improved reproducibility, persistent issues involving contamination, database inconsistency, and protocol variability remain unresolved. Collectively, these findings suggest that microbiome profiles should be interpreted not as direct biological truths, but as outcomes shaped through methodological lenses requiring rigorous standardization and transparent analytical practices.
Keywords: Microbiome research; methodological bias; 16S rRNA sequencing; primer bias; DNA extraction; bioinformatics
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