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Updated: Aug 19, 2025

Nanopore DNA Sequencing for Metagenomic Soil Analysis

Nanopore DNA Sequencing for Metagenomic Soil Analysis

Published on: December 14, 2017

Nanopore sequencing data analysis using Microsoft Azure cloud computing service.

Linh Truong^1,2, Felipe Ayora³, Lloyd D'Orsogna^1,2

¹Department of Clinical Immunology, PathWest, Perth, Australia.

|December 2, 2022

Summary

This summary is machine-generated.

This study developed an automated cloud-based pipeline for faster and cheaper HLA genotyping using Microsoft Azure. The pipeline ensures accuracy and efficiency for genetic analysis, improving healthcare outcomes.

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Last Updated: Aug 19, 2025

Nanopore DNA Sequencing for Metagenomic Soil Analysis

Nanopore DNA Sequencing for Metagenomic Soil Analysis

Published on: December 14, 2017

Validating Whole Genome Nanopore Sequencing, using Usutu Virus as an Example

Validating Whole Genome Nanopore Sequencing, using Usutu Virus as an Example

Published on: March 11, 2020

Sequencing of mRNA from Whole Blood using Nanopore Sequencing

Sequencing of mRNA from Whole Blood using Nanopore Sequencing

Published on: June 3, 2019

Area of Science:

Genomics and Bioinformatics
Computational Biology
Health Informatics

Background:

Genetic information is crucial for understanding organismal organization and improving healthcare, such as in allogeneic transplantation.
Cloud computing offers scalable, on-demand resources, making it ideal for handling large genetic datasets.
Advancements in cloud computing and nanopore sequencing necessitate efficient data analysis workflows.

Purpose of the Study:

To develop an automated, scalable, and cost-effective analysis pipeline for HLA genotyping using Microsoft Azure cloud infrastructure.
To optimize virtual machine selection for balancing performance and cost-effectiveness.
To ensure the accuracy and reliability of the automated pipeline through concordance testing.

Main Methods:

Selected optimal virtual machine sizes on Microsoft Azure for performance and cost efficiency.
Built Docker containers to encapsulate all necessary computational tools within the cloud environment.
Compared HLA genotype concordance between the automated cloud pipeline and an in-house manual method.

Main Results:

The Microsoft Azure cloud-based pipeline demonstrated high performance, cost-effectiveness, usability, simplicity, and accuracy.
The pipeline successfully accelerated HLA genotyping services and improved workflow efficiency.
Concordance testing confirmed the accuracy of the automated method compared to manual analysis.

Conclusions:

The automated Microsoft Azure cloud-based pipeline effectively meets performance, cost, usability, and accuracy requirements for HLA genotyping.
The pipeline facilitates ongoing maintenance and version testing before implementation.
This approach is suitable for MinION sequencing data and adaptable to other data analysis applications.