Nanopore sequencing data analysis using Microsoft Azure cloud computing service

Genetic information provides insights into the exome, genome, epigenetics and structural organisation of the organism. Given the enormous amount of genetic information, scientists are able to perform mammoth tasks to improve the standard of health care such as determining genetic influences on outcome of allogeneic transplantation. Cloud-based computing has increasingly become a key choice for many scientists, engineers and institutions as it offers on-demand network access and users can conveniently rent rather than buy all required computing resources. With the positive advancements of cloud computing and nanopore sequencing data output, we were motivated to develop an automated and scalable analysis pipeline utilizing cloud infrastructure in Microsoft Azure to accelerate HLA genotyping service and improve the efficiency of the workflow at lower cost. In this study, we describe (i) the selection process for suitable virtual machine sizes for computing resources to balance between the best performance versus cost-effectiveness; (ii) the building of Docker containers to include all tools in the cloud computational environment; (iii) the comparison of HLA genotype concordance between the in-house manual method and the automated cloud-based pipeline to assess data accuracy. In conclusion, the Microsoft Azure cloud-based data analysis pipeline was shown to meet all the key imperatives for performance, cost, usability, simplicity and accuracy. Importantly, the pipeline allows for the ongoing maintenance and testing of version changes before implementation. This pipeline is suitable for data analysis from MinION sequencing platforms and could be adopted for other data analysis application processes.

遗传信息有助于解析生物体的外显子组（exome）、基因组、表观遗传学（epigenetics）及结构组织特征。鉴于遗传数据体量庞大，科研人员可开展大规模研究以提升医疗保健水准，例如明确遗传因素对异基因移植预后的影响。云计算（cloud computing）凭借按需网络访问的特性，且用户可便捷租赁而非购置全部所需计算资源，正日益成为众多科研人员、工程师与科研机构的核心选择。得益于云计算与纳米孔测序（nanopore sequencing）数据产出的快速发展，本研究团队旨在借助微软Azure（Microsoft Azure）云基础设施开发一套自动化且可扩展的分析流程，以加速HLA基因分型（HLA genotyping）服务，并以更低成本提升工作流效率。本研究详述了三项核心内容：(1) 适配计算资源的虚拟机规格筛选流程，以实现最优性能与成本效益间的平衡；(2) 构建集成云算力环境所需全部工具的Docker容器（Docker）；(3) 对比院内手动分型方法与自动化云计算流程的HLA基因型一致性，以评估数据准确性。综上，基于微软Azure云平台搭建的数据分析流程，在性能、成本、易用性、便捷性与准确性方面均满足核心要求。尤为关键的是，该流程支持在正式上线前对版本更新进行持续维护与测试。本流程适配MinION测序平台的数据分析需求，亦可推广至其他数据分析应用场景。

搜集汇总

背景与挑战

背景概述

该数据集提供了使用微软Azure云计算服务进行纳米孔测序数据分析的实例，旨在开发自动化、可扩展的HLA基因分型流程，以提高效率并降低成本。数据集包含多个FASTQ格式的测序文件（总大小约9.7 GB），展示了实际样本数据，并强调了云计算在生物信息学中的性能、成本和准确性优势。这为研究纳米孔测序与云计算的结合提供了实用资源。