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Published on: August 25, 2018
Evaluation of subsampling-based normalization strategies for tagged high-throughput sequencing data sets from gut
Daniel Aguirre de Cárcer1, Stuart E Denman, Chris McSweeney
1CSIRO Preventative Health National Research Flagship and Division of Livestock Industries, Queensland Bioscience Precinct, St. Lucia, QLD 4067, Australia.
Applied and Environmental Microbiology
|October 11, 2011
Summary
Subsampling normalization strategies were tested on gut microbiome sequencing data. Subsampling to the median, not the minimum, improved data analysis and β-diversity metrics for both human and murine datasets.
Area of Science:
- Microbiome analysis
- Bioinformatics
- High-throughput sequencing data
Background:
- Normalization is crucial for high-throughput sequencing data analysis.
- Subsampling is a common normalization technique.
- Different subsampling strategies may impact microbiome data analysis.
Purpose of the Study:
- To compare the effects of different subsampling-based normalization strategies.
- To evaluate normalization efficiencies using β-diversity metrics.
- To determine optimal subsampling methods for gut microbiome data.
Main Methods:
- Applied several subsampling-based normalization strategies.
- Utilized high-throughput sequencing data from human and murine gut environments.
- Compared data characteristics and normalization efficiencies using β-diversity metrics.
Main Results:
- Subsampling to the median improved data analysis compared to subsampling to the minimum.
- Normalization efficiencies varied depending on the strategy and dataset.
- Observed improvements in β-diversity metrics when subsampling to the median.
Conclusions:
- Subsampling to the median is a potentially superior normalization strategy for gut microbiome data.
- The choice of subsampling strategy can significantly influence microbiome analysis outcomes.
- Further research is warranted to optimize normalization techniques for diverse sequencing datasets.
