Developing a Reproducible Microbiome Data Analysis Pipeline Using the Amazon Web Services Cloud for a Cancer Research Group: Proof-of-Concept Study.

• Type of Material: Article
• Authors: Jinbing Bai, Emory University; Ileen Jhaney, Emory University; Jessica Wells, Emory University
• Language: English
• Date: 2019-11-11
• Publisher: JMIR Publications
• Publication Version: Final Published Version
• Copyright Statement: ©Jinbing Bai, Ileen Jhaney, Jessica Wells. Originally published in JMIR Medical Informatics
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.2196/14667
• Title of Journal or Parent Work: JMIR Medical Informatics
• ISSN: 2291-9694
• Volume: 7
• Issue: 4
• Start Page: e14667
• End Page: e14667
• Grant/Funding Information: This research project was supported by the Amazon Web Services Cloud Credits for Research program.; This article published with support from Emory Libraries' Open Access Publishing Fund.
• Keywords: microbiome; Amazon Web Services; sequence analysis; cloud computation; pipeline
• Research Categories: Health Sciences, Oncology

Abstract

BACKGROUND: Cloud computing for microbiome data sets can significantly increase working efficiencies and expedite the translation of research findings into clinical practice. The Amazon Web Services (AWS) cloud provides an invaluable option for microbiome data storage, computation, and analysis. OBJECTIVE: The goals of this study were to develop a microbiome data analysis pipeline by using AWS cloud and to conduct a proof-of-concept test for microbiome data storage, processing, and analysis. METHODS: A multidisciplinary team was formed to develop and test a reproducible microbiome data analysis pipeline with multiple AWS cloud services that could be used for storage, computation, and data analysis. The microbiome data analysis pipeline developed in AWS was tested by using two data sets: 19 vaginal microbiome samples and 50 gut microbiome samples. RESULTS: Using AWS features, we developed a microbiome data analysis pipeline that included Amazon Simple Storage Service for microbiome sequence storage, Linux Elastic Compute Cloud (EC2) instances (ie, servers) for data computation and analysis, and security keys to create and manage the use of encryption for the pipeline. Bioinformatics and statistical tools (ie, Quantitative Insights Into Microbial Ecology 2 and RStudio) were installed within the Linux EC2 instances to run microbiome statistical analysis. The microbiome data analysis pipeline was performed through command-line interfaces within the Linux operating system or in the Mac operating system. Using this new pipeline, we were able to successfully process and analyze 50 gut microbiome samples within 4 hours at a very low cost (a c4.4xlarge EC2 instance costs $0.80 per hour). Gut microbiome findings regarding diversity, taxonomy, and abundance analyses were easily shared within our research team. CONCLUSIONS: Building a microbiome data analysis pipeline with AWS cloud is feasible. This pipeline is highly reliable, computationally powerful, and cost effective. Our AWS-based microbiome analysis pipeline provides an efficient tool to conduct microbiome data analysis.