A cloud-based training module for efficient de novo transcriptome assembly using Nextflow and Google cloud

被引:2
作者
Seaman, Ryan P. [1 ]
Campbell, Ross [2 ]
Doe, Valena [3 ]
Yosufzai, Zelaikha [2 ]
Graber, Joel H. [1 ]
机构
[1] MDI Biol Lab, 159 Old Bar Harbor Rd, Bar Harbor, ME 04609 USA
[2] Deloitte Consulting LLP, Hlth Data & AI, 4301 Fairfax Dr,Unit 210, Arlington, VA 22203 USA
[3] Google, Google Cloud, 1900 Reston Metro Plaza, Reston, VA 20190 USA
基金
美国国家卫生研究院;
关键词
cloud computing; nextflow; training module; transcriptome; assembly; annotation; PREDICTION;
D O I
10.1093/bib/bbae313
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
This study describes the development of a resource module that is part of a learning platform named "NIGMS Sandbox for Cloud-based Learning" (https://github.com/NIGMS/NIGMS-Sandbox). The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox at the beginning of this Supplement. This module delivers learning materials on de novo transcriptome assembly using Nextflow in an interactive format that uses appropriate cloud resources for data access and analysis. Cloud computing is a powerful new means by which biomedical researchers can access resources and capacity that were previously either unattainable or prohibitively expensive. To take advantage of these resources, however, the biomedical research community needs new skills and knowledge. We present here a cloud-based training module, developed in conjunction with Google Cloud, Deloitte Consulting, and the NIH STRIDES Program, that uses the biological problem of de novo transcriptome assembly to demonstrate and teach the concepts of computational workflows (using Nextflow) and cost- and resource-efficient use of Cloud services (using Google Cloud Platform). Our work highlights the reduced necessity of on-site computing resources and the accessibility of cloud-based infrastructure for bioinformatics applications.
引用
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页数:8
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