Serverless computing in omics data analysis and integration

Abstract A comprehensive analysis of omics data can require vast computational resources and access to varied data sources that must be integrated into complex, multi-step analysis pipelines. Execution of many such analyses can be accelerated by applying the cloud computing paradigm, which provides...

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Bibliographic Details
Published in:Briefings in Bioinformatics 2022-01, Vol.23 (1)
Main Authors: Grzesik, Piotr, Augustyn, Dariusz R, Wyciślik, Łukasz, Mrozek, Dariusz
Format: Article
Language:English
Subjects:
Bioinformatics
Cloud Computing
Computational Biology
Computer applications
COVID-19
COVID-19 - epidemiology
COVID-19 - genetics
COVID-19 - metabolism
Data analysis
Data sources
Genomics
Humans
Infrastructure
Integration
Pandemics
Parallel processing
Pipelining (computers)
Review
SARS-CoV-2 - genetics
SARS-CoV-2 - metabolism
Software
Virtual environments
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Summary:Abstract A comprehensive analysis of omics data can require vast computational resources and access to varied data sources that must be integrated into complex, multi-step analysis pipelines. Execution of many such analyses can be accelerated by applying the cloud computing paradigm, which provides scalable resources for storing data of different types and parallelizing data analysis computations. Moreover, these resources can be reused for different multi-omics analysis scenarios. Traditionally, developers are required to manage a cloud platform’s underlying infrastructure, configuration, maintenance and capacity planning. The serverless computing paradigm simplifies these operations by automatically allocating and maintaining both servers and virtual machines, as required for analysis tasks. This paradigm offers highly parallel execution and high scalability without manual management of the underlying infrastructure, freeing developers to focus on operational logic. This paper reviews serverless solutions in bioinformatics and evaluates their usage in omics data analysis and integration. We start by reviewing the application of the cloud computing model to a multi-omics data analysis and exposing some shortcomings of the early approaches. We then introduce the serverless computing paradigm and show its applicability for performing an integrative analysis of multiple omics data sources in the context of the COVID-19 pandemic.
ISSN:1467-5463
1477-4054
DOI:10.1093/bib/bbab349