Invited Commentary: Improving the Accessibility of Human Microbiome Project Data Through Integration With R/Bioconductor

• Published in: American journal of epidemiology Vol. 188; no. 6; pp. 1027 - 1030
• Main Authors: Griffith, Jocelyn C, Morgan, Xochitl C
• Format: Journal Article
• Language: English
• Published: United States Oxford Publishing Limited (England) 01.06.2019
• Subjects: Access control; Accessibility; Computational Biology; Datasets; Gene sequencing; Genomes; Genotypes; Health care; Humans; Metadata; Metagenome; Metagenomics; Microbial activity; Microbiomes; Microbiota; Microorganisms; Phenotypes; Phylogenetics; Phylogeny; Public health; Researchers; RNA, Ribosomal, 16S; rRNA 16S; databases; R; Bioconductor; microbiome; Human Microbiome Project
• ISSN: 0002-9262; 1476-6256; 1476-6256
• DOI: 10.1093/aje/kwz007

Alterations in the composition of the microbiota have been implicated in many diseases. The Human Microbiome Project (HMP) provides a comprehensive reference data set of the "normal" human microbiome of 242 healthy adults at 5 major body sites. The HMP used both 16S ribosomal RNA gene sequencing and whole-genome metagenomic sequencing to profile the subjects' microbial communities. However, accessing and analyzing the HMP data set still presents technical and bioinformatic challenges, given that researchers must import the microbiome data, integrate phylogenetic trees, and access and merge public and restricted metadata. The HMP16SData R/Bioconductor package developed by Schiffer et al. (Am J Epidemiol. 2019;188(6):1023-1026) greatly simplifies access to the HMP data by combining 16S taxonomic abundance data, public patient metadata, and phylogenetic trees as a single data object. The authors also provide an interface for users with approved Database of Genotypes and Phenotypes (dbGaP) projects to easily retrieve and merge the controlled-access HMP metadata. This package has a broad range of appeal to researchers across disciplines and with various levels of expertise in using R and/or other statistical tools, which translates to improved data accessibility for public health research, with data from healthy individuals serving as a reference for disease-associated studies.