A comprehensive quality control pipeline in human microbiome research for large population studies

Abstract

The widespread application of high-throughput Next Generation Sequencing (NGS) technologies has made microbiome research an emerging field in public health and biomedical sciences. However, there are still many challenges that need to be addressed in this field. Pipelines available to generate microbiome data across cohorts are diverse, and sources of variation to be recorded and evaluated during microbiome profiling have not been standardized. Moreover, meticulous quality control of the microbiome data processing, from collection to computational quantification is still challenging, especially in large population studies. Innovative approaches are required to handle samples and to minimize the potential bias introduced by logistic hurdles in biobanking. In this paper, we describe the methodological steps surrounding the optimization of the 16S rRNA gut microbiome profiling in two large prospective cohorts the Generation R Study (mean age 9.83 {+/-} 0.32 years) and the Rotterdam Study (mean age 62.67 {+/-} 5.66 years). This paper also highlights potential solutions to sample mislabeling in large-scale microbiome analysis. To summarize, our study addresses common problems in human microbiome research. It aims to improve the research quality and reliability by integrating more stringent quality control standards into microbiome research.