Perspectives of using Cloud computing in integrative analysis of multi-omics data

Abstract

Integrative analysis of multi-omics data is usually computationally demanding. It frequently requires building complex, multi-step analysis pipelines, applying dedicated techniques for data processing and combining several data sources. These efforts lead to a better understanding of life processes, current health state or the effects of therapeutic activities. However, many omics data analysis solutions focus only on a selected problem, disease, types of data or organisms. Moreover, they are implemented for general-purpose scientific computational platforms that most often do not easily scale the calculations natively. These features are not conducive to advances in understanding genotype–phenotypic relationships. Fortunately, with new technological paradigms, including Cloud computing, virtualization and containerization, these functionalities could be orchestrated for easy scaling and building independent analysis pipelines for omics data. Therefore, solutions can be re-used for purposes that they were not primarily designed. This paper shows perspectives of using Cloud computing advances and containerization approach for such a purpose. We first review how the Cloud computing model is utilized in multi-omics data analysis and show weak points of the adopted solutions. Then, we introduce containerization concepts, which allow both scaling and linking of functional services designed for various purposes. Finally, on the Bioconductor software package example, we disclose a verified concept model of a universal solution that exhibits the potentials for performing integrative analysis of multiple omics data sources.

The Cloud-Enabled Architecture of the Clinical Data Repository in Poland

“…Kubernetes (K8s) [42] is currently the most-popular orchestrator for the systems based on containerized microservices. This trend is observable not only in commercial systems for clinical data processing but also in scientific studies (see [43]).…”

Serverless computing in omics data analysis and integration

“…Although most of the current applications of cloud technologies for genomics computing [5] use SaaS [8,10,13,18,26,28,44,47,52] and IaaS [3,9,18,31,32], there is growing interest in serverless implementations.…”

“…A similar concept, container as a service (CaaS), supports containerization and holds several advantages resulting from serverless idea. Within CaaS, containers can be instantiated with an autoscaling option without worrying about a runtime infrastructure [5]. In the following sections, we will examine these services, with a particular focus on cloud platforms and their capabilities and limitations.…”

Accelerating genomic workflows using NVIDIA Parabricks

“…Cloud computing addresses many of the challenges associated with large whole genome sequencing projects, which can suffer from siloed data, long download times, and slow worlkflow runtimes (Tanjo et al, 2021). Several papers have reviewed the potential of cloud platforms for sequence data storage, sharing, and analysis (Augustyn et al, 2021; Cole & Moore, 2018; Grossman, 2019; Grzesik et al, 2021; Koppad et al, 2021; Langmead & Nellore, 2018; Leonard et al, 2019), thus here we focus on one cloud computing challenge, how to select the right compute configuration to optimize both cost and performance (Krissaane et al, 2020; Ray et al, 2021).…”