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OMARU: a robust and multifaceted pipeline for metagenome-wide association study.

Toshihiro KishikawaYoshihiko TomofujiHidenori InoharaYukinori Okada
Published in: NAR genomics and bioinformatics (2022)
Microbiome is an essential omics layer to elucidate disease pathophysiology. However, we face a challenge of low reproducibility in microbiome studies, partly due to a lack of standard analytical pipelines. Here, we developed OMARU ( O mnibus m etagenome-wide a ssociation study with r ob u stness), a new end-to-end analysis workflow that covers a wide range of microbiome analysis from phylogenetic and functional profiling to case-control metagenome-wide association studies (MWAS). OMARU rigorously controls the statistical significance of the analysis results, including correction of hidden confounding factors and application of multiple testing comparisons. Furthermore, OMARU can evaluate pathway-level links between the metagenome and the germline genome-wide association study (i.e. MWAS-GWAS pathway interaction), as well as links between taxa and genes in the metagenome. OMARU is publicly available (https://github.com/toshi-kishikawa/OMARU), with a flexible workflow that can be customized by users. We applied OMARU to publicly available type 2 diabetes (T2D) and schizophrenia (SCZ) metagenomic data ( n  = 171 and 344, respectively), identifying disease biomarkers through comprehensive, multilateral, and unbiased case-control comparisons of metagenome (e.g. increased Streptococcus vestibularis in SCZ and disrupted diversity in T2D). OMARU improves accessibility and reproducibility in the microbiome research community. Robust and multifaceted results of OMARU reflect the dynamics of the microbiome authentically relevant to disease pathophysiology.
Keyphrases
  • case control
  • type diabetes
  • electronic health record
  • healthcare
  • mental health
  • escherichia coli
  • machine learning
  • dna methylation
  • staphylococcus aureus
  • cystic fibrosis
  • artificial intelligence
  • data analysis