Related Experiment Video
Updated: Jun 27, 2025
07:21
Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing
Published on: August 25, 2018
12.8K
Variability and bias in microbiome metagenomic sequencing: an interlaboratory study comparing experimental protocols.
Samuel P Forry1, Stephanie L Servetas2, Jason G Kralj2
1Complex Microbial Systems Group, National Institute of Standards and Technology (NIST), Gaithersburg, MD, USA. sam.forry@nist.gov.
Scientific Reports
|April 29, 2024
Summary
Methodological choices significantly impact microbiome metagenomic sequencing (MGS) results, affecting both measurement bias and robustness. This study highlights the need for standardized protocols to improve comparability across independent microbiome analyses.
Area of Science:
- Microbiology
- Bioinformatics
- Genomics
Background:
- Methodological variability in microbiome analyses complicates result replication and comparability.
- Standardization is crucial for reliable and reproducible microbiome research.
Purpose of the Study:
- To assess the impact of methodological choices on microbiome metagenomic sequencing (MGS) results.
- To survey the methodological landscape of MGS analyses and identify key variables influencing outcomes.
- To evaluate measurement bias and robustness across different MGS protocols.
Main Methods:
- An international interlaboratory study (Mosaic Standards Challenge - MSC) involving 44 labs.
- Analysis of 7 shared reference samples (human stool and mock communities) using participants' standard laboratory methods.
- Collection of detailed metadata on 100 protocol variables for 16S rRNA gene amplicon and whole-genome sequencing (WGS) data.
Main Results:
- Protocol choices significantly affect MGS measurement bias and robustness.
- Variability in results was observed even among labs using similar methodological choices.
- Measurement bias was evident in DNA mock community analyses, despite general consensus.
Conclusions:
- Methodological decisions have a substantial impact on the comparability and reliability of microbiome sequencing data.
- Standardization of MGS protocols is essential for advancing microbiome research.
- Findings provide insights for designing future microbiome measurement studies.
Related Concept Videos
01:11Variability: Analysis
140
Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
The range is a simple measure of variability, indicating the difference between the highest and...
140
01:15Systematic Error: Methodological and Sampling Errors
1.5K
In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
1.5K