Identification of Sample Processing Errors in Microbiome Studies Using Host Genetic Profiles.

Area of Science:

• Microbiology
• Genetics
• Bioinformatics

Background:

• Sample processing errors are frequent and hard to detect in large-scale microbiome studies.
• These errors can compromise the integrity and reliability of research findings.

Purpose of the Study:

• To present two novel, complementary methods for identifying sample processing errors in microbiome studies.
• To enhance the accuracy and trustworthiness of microbiome data through robust error detection.

Main Methods:

• Utilizing host DNA single nucleotide polymorphisms (SNPs) inferred from metagenomic sequencing.
• Comparing metagenomics-inferred SNPs with external genotyping data (e.g., microarrays) for sample-donor matching.
• Comparing metagenomics-inferred SNPs between samples to identify samples from the same donor.
• Integrating these methods with experimental metadata for increased confidence in error identification.

Main Results:

• Successfully identified mislabeled samples in a longitudinal vaginal microbiome dataset.
• Demonstrated the robustness of the methods even with low sequencing coverage through subsampling.
• Highlighted the significant frequency of processing errors in microbiome research.

Conclusions:

• The presented host DNA-based methods are effective for detecting sample processing errors in microbiome studies.
• Combining SNP comparison with metadata analysis provides a powerful approach for quality control.
• Recommends implementing these error-detection strategies in microbiome research to ensure data integrity.